experimental investigations of electro chemical micro...

EXPERIMENTAL INVESTIGATIONS OF

ELECTRO CHEMICAL MICRO MACHINING

ON NICKEL AND ITS ALLOYS

A THESIS

Submitted by

SARAVANAN D

in partial fulfillment for the requirement of award of the degree

of

DOCTOR OF PHILOSOPHY

FACULTY OF MECHANICAL ENGINEERING

ANNA UNIVERSITY

CHENNAI 600 025 JULY 2012

v

ABSTRACT

The advancement in the field of mechanical engineering is very

essential to meet the growing demands of the industry. In particular the

demand for alloy materials having high hardness, toughness and impact

resistance has grown multi fold due to high level of design constraints. Electro

Chemical Micro Machining (ECMM) machines are used to cut metals of any

hardness or that are difficult or impossible to cut with traditional methods.

These machines also specialize in cutting complex contours or geometries that

would be difficult to produce using conventional cutting methods. Machine

tool industry has made exponential growth in its manufacturing capabilities in

last decade but these machine tools are yet to be utilized at their full potential

due to inadequate data on optimum operating parameters. The problem of

arriving at the optimum levels of the operating parameters on Material

Removal Rate (MRR) has attracted the attention of researcher to take-up

research in this area.

The literature survey has revealed that a little research has been

conducted to obtain the combination of optimal levels of machining

parameters that yield the maximum MRR and best machining quality in

machining of difficult to machine materials like Nickel, Super Duplex

Stainless Steel (SDSS) and Inconel 600. It is difficult to achieve required

quality in parts machined by ECMM process consistently with improper

selection of levels of various process parameters. The selection of

vi

optimum parameters for machining of Nickel and its alloys is a tough

challenge for Aero space, Electronics and Bio-medical industries. Hence,

the Nickel and its alloys are specifically selected for this research.

The objective of the present research work is to investigate the

effects of the various ECMM process parameters on the MRR and

dimensional deviation to obtain the optimal sets of process parameters to

produce efficient high quality machining.

The Taguchi technique has been used to investigate the effects of

the ECMM process parameters and subsequently to predict sets of optimal

parameters for maximum MRR. The working ranges and levels of the

ECMM process parameters are found using one factor at a time approach.

The ANOVA has been used to find optimal combination of machining

parameters. The confirmation experiments are conducted based on the

predicted levels of process parameters. The coherence between confirmation

experiment results, ANOVA and Genetic Algorithms is analyzed.

The experimental setup for this research on ECMM consists of Electrolyte

tank, non-corrosive work holding platform, Feeding device actuated with stepper

motor, Microprocessor based machine control unit and Power supply system.

This setup is capable of maintaining an accuracy of 4 microns in the machining

process. The experiments were conducted on 0.15 mm thick specimens made up

of Nickel, SDSS and Inconel 600 to find the optimum combination of machining

parameters viz., Electrolyte concentration, Machining Voltage, Machining

Current, Duty cycle and Frequency. The following levels of the process

vii

parameters are selected for the present study:

Materials Levels Process Parameters EC V C DC F

Nickel I 0.1 3.5 0.1 33.33 30II 0.2 5.0 0.3 50.00 40III 0.3 6.5 0.5 66.66 50

SDSS I 0.40 8.0 0.6 33.33 30II 0.45 9.0 0.8 50.00 40III 0.50 10.0 1.0 66.66 50

Inconel 600 I 0.40 8.0 0.6 33.33 30II 0.45 9.0 0.8 50.00 40III 0.50 10.0 1.0 66.66 50

EC: Electrolyte Concentration (mol/lit), V: Voltage (Volt), C: Current (Ampere), DC: Duty Cycle (%), F: Frequency (Hz).

The entire set of experiments is carried out in a phased manner. The

experiments in each phase were repeated two times in order to achieve mean

values. The analysis and verification of experimental results using Taguchi

methodology, ANOVA and GA it is concluded that the major factor affecting the

MRR on Nickel is Machining Current, on SDSS is Duty cycle and on Inconel

600 is Electrolyte Concentration. It is inferred from the experiment that the

reduction in % of Nickel present in the alloy, the other processing factors like

Duty Cycle and Electrolyte concentration becomes the major parameter affecting

the MRR.

viii

ACKNOWLEDGEMENT

I express my heartfelt adulation and gratitude to my supervisor,

Dr. M. Arularasu, Principal, Thanthai Periyar Government Institute of

Technology, Vellore, for his unreserved guidance, constructive suggestions

and outstanding inspiration throughout this research work.

I am grateful to Dr. K. Ganesan, Professor, Mechanical Engineering

Department, PSG College of Technology, Coimbatore, for his

incomparable support in every stage of this research work.

I wish to thank Dr. S.R. Devadasan, Professor, Department of

Production Engineering, PSG College of Technology, Coimbatore, for

providing valuable suggestions. I express my sincere thanks to

Dr. G. Mohankumar, Principal, Park college of Engineering, Coimbatore, for

providing expert guidance throughout this research work.

I am also grateful to Dr. R.M. Arunachalam, for extending facilities

to carry out investigations. Thanks are also due to Dr. P. Asokan,

Professor, Department of Production Engineering, NIT, Trichy, who

provided excellent advices to carryout experimental work.

I am thankful to Dr. J. Jerald, Associate Professor, Department of

Production Engineering, National Institute of Technology, Trichy, for his

timely guidance, support and encouragement during the course of my work.

This prefatory remark will become complete by expressing my

deep sense of gratitude to my dear parents for their blessings, to my wife

R. Ananthi, daughter Ms. S.Vijayashanthy and son S.Veera for their care and

support.

I express my sincere thanks to all those who directly or indirectly

helped at various stages of this research work for its successful completion.

Above all, I humbly offer my sincere indebtedness to the

“ALMIGHTY” for every moment of my life.

(D. Saravanan)

ix

TABLE OF CONTENTS

CHAPTER NO. TITLE PAGE NO.

CERTIFICATE ii

PROCEEDINGS iii

ABSTRACT v

ACKNOWLEDGEMENT viii

LIST OF TABLES x

LIST OF FIGURES xii

NOMENCLATURE xiv

1. INTRODUCTION

1.1. INTRODUCTION 1

1.1.1. Importance of ECMM in Present Day Scenario 3

1.1.2. Electrochemical Machining 5

1.1.3. Basic Principles of ECMM Process 7

1.1.4. Mechanism of Material Removal in ECMM 7

1.1.5. Advantages of ECMM 10

1.1.6. Disadvantages of ECMM 11

1.1.7. Applications of ECMM 12

1.2. OBJECTIVES OF PRESENT INVESTIGATION 13

1.3. OUTLINE OF THE RESEARCH WORK 15

1.4. STATEMENT OF THE PROBLEM 16

2. LITERATURE SURVEY

2.1. REVIEW OF LITERATURE 17

2.2. OVERVIEW ON ECMM 17

2.3. OUTCOME OF LITERATURE REVIEW 36

3. EXPERIMENTAL DESIGN

3.1 INTRODUCTION 38

x

3.2 TAGUCHI EXPERIMENTAL DESIGN AND ANALYSIS 41

3.2.1. Taguchi’s Philosophy 41

3.2.2. Experimental Design Strategy 42

3.2.3. Signal to Noise Ratio 45

3.2.4. Selection of orthogonal array (OA) 49

3.2.5. Assignment of parameters and interaction to the OA 52

3.2.6. Experimentation and data collection 52

3.2.7. Data analysis 53

3.2.8. Parameter classification and selection of optimal levels 53

3.2.9. Prediction of the mean 54

3.2.10. Determination of confidence interval 55

3.3 MACHINING PERFORMANCE EVALUATION 56

3.3.1. Material Removal Rate (MRR) 56

3.3.2 Signal-to-Noise Ratio (S/N Ratio) 57

3.3.3. Analysis of variance (ANOVA) 57

3.3.4. Confirmation Test 62

3.4. GENETIC ALGORITHMS (GA) 63

3.4.1 Introduction 63

3.4.2 Implementation of GA 66

3.4.3 Experimental Validation (GA) 70

4. EXPERIMENTAL SET-UP

4.1 INTRODUCTION 71

4.2 MACHINING SETUP STRUCTURE 72

4.2.1 Work Holding Platform 73

4.2.2 Tool Feeding Device 74

4.2.3 Inter Electrode Gap Control System 75

4.2.4 Electrolyte Flow System 76

4.2.5 Microcontroller Unit 78

4.2.6 Power Supply System 79

4.3 MATERIALS FOR RESEARCH 81

xi

4.3.1 Nickel 82

4.3.2 SDSS 85

4.3.3 INCONEL 600 905. EXPERIMENTAL RESULTS AND ANALYSIS

5.1 INTRODUCTION 95

5.2 SELECTION OF ORTHOGONAL ARRAY 95

5.3 EXPERIMENTAL RESULTS 101

5.3.1 Experimental Results - Nickel 103

5.3.2 Experimental Results - SDSS 106

5.3.3 Experimental Results - Inconel 600 109

5.4 ANALYSIS AND DISCUSSION OF RESULTS 112

5.5 CONFIRMATION TEST 112

5.5.1 Results and Discussion for Nickel 113

5.5.2 Results and Discussion for SDSS 121

5.5.3 Results and Discussion for Inconel 600 129

5.6 DIMENSIONAL DEVIATION 138

5.6.1 Dimensional Deviation - Nickel 138

5.6.2 Dimensional Deviation - SDSS 139

5.6.3 Dimensional Deviation - Inconel 600 141

6. SUMMARY AND CONCLUSIONS

6.1 SUMMARY 143

6.2 CONCLUSIONS 144

6.2.1 Conclusion on ECMM of Nickel 145

6.2.2 Conclusion on ECMM of SDSS 146

6.2.3 Conclusion on ECMM of Inconel 600 147

6.3 SUGGESTIONS FOR FUTURE WORK 148

APPENDICES 149-150

REFERENCES 151-155

LIST OF PUBLICATIONS 156

CURRICULUM VITAE 157

xii

LIST OF TABLES

Number Title Page

1.1 Dissolution valence for different metals 10

1.2 Comparison between ECM and ECMM 10

3.1 Degree of Freedom 50

3.2 Calculated ON time and OFF time - Nickel 59

3.3 Calculated ON time and OFF time - SDSS and Inconel 600 60

3.4 Average Current - Nickel 60

3.5 Average Current - SDSS and Inconel 600 61

3.6 Average Voltage - Nickel 61

3.7 Average Voltage - SDSS and Inconel 600 61

4.1 General Properties of Nickel 83

4.2 Physical Properties of Nickel 83

4.3 Atomic Properties of Nickel 83

4.4 Miscellaneous Properties of Nickel 84

4.5 Specifications of SDSS 89

4.6 Compositions of Inconel Alloy 90

4.7 Physical Properties of Inconel 600 91

5.1 Process Parameters and their Levels - Nickel 95

5.2 Process Parameters and their Levels - SDSS 96

5.3 Process Parameters and their Levels - Inconel 600 96

5.4 Experiment Layout using L18 Orthogonal Array 97

5.5 Orthogonal Array of Process Parameters - Nickel 98

5.6 Orthogonal Array of Process Parameters for SDSS 99

5.7 Orthogonal Array of Process Parameters for Inconel 600 100

5.8 Experimental Results for MRR - Nickel 102

5.9 Experimental Results - Nickel 103

xiii

5.10 Experimental Results - SDSS 106

5.11 Experimental Results - Inconel 600 109

5.12 Response Table for Means - Nickel 113

5.13 Response Table for S/N Ratio - Nickel 115

5.14 Results of ANOVA - Nickel 115

5.15 Results of Confirmation Test - Nickel 119

5.16 Response Table for Means - SDSS 122

5.17 Response Table for S/N Ratio - SDSS 123

5.18 Results of ANOVA - SDSS 124

5.19 Results of Confirmation Test - SDSS 127

5.20 Experimental Results - Inconel 600 130

5.21 Response Table for S/N Ratio - Inconel 600 131

5.22 Results of ANOVA - Inconel 600 132

5.23 Results of Confirmation Test - Inconel 600 135

A 1.1 Experimental Results for MRR - SDSS 149

A 1.2 Experimental Results for MRR - Inconel 600 150

xiv

LIST OF FIGURES

Number Title Page

1.1 Physical Model of ECMM 8

1.2 Mechanism of ECMM 9

1.3 Outline of Research Work 15

3.1 Taguchi Loss Function 44

3.2 The Taguchi Loss-Function for HB and LB Characteristics 47

3.3 Taguchi Experimental Design and Analysis Flow Diagram 51

3.4 Structure of Genetic Algorithm 65

4.1 Schematic Diagram of Experimental Setup 71

4.2 Work Holding Platform, Tool holding arrangement 74

4.3 Control System 76

4.4 Electrolyte Filter 77

4.5 Electro Chemical Reactions 78

4.6 Pulse Rectifier 79

4.7 Complete Experimental Setup 81

5.1 Image of micro hole machined in 8th experiment 104









5.10 Main effect plot for means Nickel 114

5.11 Contribution of Process Parameters on MRR Nickel 116

xv

5.12 Normal Probability Plot (S/N Ratio) Nickel 117

5.13 Process Parameter Interaction Plot (MRR) Nickel 117

5.14 Image of micro hole machined for confirmation experiment 119

5.15 Comparison between GA and EV for Nickel 120

5.16 Screen Shot of GA output for Nickel 121

5.17 Main Effect Plot for Means SDSS 122

5.18 Contribution of Process Parameters on MRR SDSS 124

5.19 Normal Probability Plot (S/N Ratio) SDSS 125

5.20 Process Parameter Interaction Plot (MRR) SDSS 126

5.21 Comparison between GA and EV for SDSS 128

5.22 Screen Shot of GA output for SDSS 129

5.23 Main Effect Plot for Means Inconel 600 130

5.24 Contribution of Process Parameters on MRR Inconel 600 132

5.25 Normal Probability Plot (S/N Ratio) Inconel 600 133

5.26 Process Parameter Interaction Plot (MRR) Inconel 600 134

5.27 Image of micro hole machined for confirmation experiment 135

5.28 Comparison between GA and EV for Inconel 600 136

5.29 Screen Shot of GA output for Nickel 137

5.30 MRR Vs Dimensional Deviation - Nickel 138

5.31 MRR Vs Dimensional Deviation - SDSS 140

5.32 MRR Vs Dimensional Deviation - Inconel 600 141

xvi

NOMENCLATURE

SYMBOL DESCRIPTION

y Actual value of characteristic

ANOVA Analysis of Variance

CI Confidence interval

CICEConfidence interval for the confirmation experiments

CIPOP Confidence interval for the population

k Constant depending on the magnitude of characteristic

DOF Degree of freedom

DD Dimensional deviation

DC Duty Cycle (%)

ECM Electro Chemical Machining

ECMM Electro Chemical Micro Machining

EC Electrolyte Concentration (mol/lit)

EL Expected loss

FAO F- test parameter

F Frequency (Hz)

GA Genetic Algorithm

HB Higher the better

IEG Inter Electrode Gap

L(y) Loss in monetary unit

LB Lower the better

C Machining Current (amps)

V Machining Voltage (volts)

MRR Material Removal Rate

µ Mean

xvii

i Mean of S/N ratio at optimum level

MSD Mean Squared Deviation

q Nnumber of significant parameters

NB Nominal is Best

fA Number of degrees of freedom of parameter

R Number of Repetition

N Number of Trials

LN OA designation

Toff OFF Time

Ton ON Time

OA Orthogonal Array

S/N Signal to Noise Ratio

SE Sum of squares based on the error

Sm Sum of squares based on the mean

SA Sum of squares based on the parameter

ST Sum of squares based on the total variation

Ai Sum of the ith level parameter

m Target value for quality characteristic

fLN Total degrees of freedom of an OA

m Total mean of S/N ratio

Ttotal Total Time

VA Variance of parameter

1

CHAPTER 1

INTRODUCTION

1.1 INTRODUCTION

The process of creativity proceeds by way of research, design and

development. The research work concerned with creation of new system, process,

and equipment for the benefit of mankind is Engineering. Research as the art of

executing a partial application of scientific knowledge by utilizing the established

facts, laws and principles of nature for the benefit of human rays. The new system

emerging from innovation may be constituted by mechanical, electro mechanical,

hydraulic, thermal, or other such elements. In these lines, this research tries to

innovate the process of Electro Chemical Micro Machining (ECMM) for Nickel

and its alloys.

Electrochemical machining (ECM) was developed during late 1950s

and early 1960s and used to machine difficult-to-cut materials in aerospace and

other heavy industries for shaping and finishing operations (Datta M 1998). It is

an anodic dissolution process based on the phenomenon of electrolysis, whose

laws were established by Michael Faraday. In ECM, electrolytes serve as

conductor of electricity. ECM offers a number of advantages over other

machining methods. The ECM technique now plays an important role in the

manufacturing of a variety of parts ranging from machining of large metallic

pieces of complicated shapes to opening of windows in silicon that are a few

microns in size. When ECM is performed at micro meter level (material removal

that ranges from 1-999 µm), it is known as ECMM (Bhattacharyya B 2007).

2

In ECMM process, the work piece is connected to anode and the

micro tool is connected to cathode and they are placed inside the electrolyte with

a small gap between them. On the application of adequate electrical energy,

positive metal ions leave from the work piece and machining takes place.

Electrolyte circulation removes the machined particles from the electrode gap. To

continue the machining process, the electrode gap has to be maintained by

moving the tool at required rate.

ECMM is used for making smaller size components with high

precision. Advanced micro machining process consists of various ultra precision

activities to be performed on very small and thin work pieces (Bhattacharyya B

2004). The high precision components with micro sized holes, slots, and complex

surfaces are largely needed in mission critical applications like Nuclear power

plant, Aero space industry, Electronics industry, and Bio-medical field. ECMM

is a very promising technology since it offers several advantages like a) higher

machining rate, b) better precision and control, c) machining wide range of

materials, d) cost effective, and e) environmental friendly.

The ECMM process is capable of machining electrically conductive,

hard to cut materials without introducing any deformation on machined surface.

In this process, no tool wear is produced. Further, no residual stress is caused

because machining is not done with direct force on the work piece. Instead, ionic

dissolution is used to remove the material. Hence, there is no heat generation

involved while machining. The ECMM process can be effectively used for high

precision machining operations such as removal of micro burrs, making patterns

in foils and 3D micromachining. These qualities and capabilities of ECMM

process makes it useful in many industries where difficult-to-cut materials are

processed.

3

1.1.1 Importance of ECMM in Present Day Scenario

The advancement in the field of metallurgy and the demand for high

strength materials in various industries resulted in development of high strength

alloys such as Nickel alloys. These alloys are extremely difficult to machine

using the traditional processes. Machining of these alloys with conventional tools

results in damage of work piece and the tool. The major difference between

conventional and non-conventional machining processes is that conventional

processes remove the material by physical means using a sharp tool. But the non-

conventional techniques remove material by utilizing chemical, thermal, or

electrical energy, or a combination of these energies.

Various machining non-conventional techniques are available to drill

micro holes in hard brittle materials. Few such non-conventional machining

techniques are Electro Chemical Drilling (ECD), Electron Beam Drilling (EBD),

Laser Beam Drilling (LBD) and, Electric Discharge Machining (EDM) (Kock M

2003). But these processes involve either heat or deformation of work piece.

The electro thermal processes such as ECD, EBD, LBD, and EDM

generally do not completely satisfy the quality requirements with respect to the

geometrical and metallurgical characteristics.

Machining materials on micrometric and submicrometric scales is

considered to be one of the key technologies of the future since, the present day

requirements of manufacturing micro and submicro level components for

miniaturized devices in biology, and medical field, chemical micro-reactors etc.

4

An ECMM system setup was developed for carrying out in-depth

research for achieving satisfactory control of ECMM process parameters

(Bhattacharyya B 2003). The effect of machining voltage, pulse duration, and

pulse frequency on machining performance was studied to further improve

ECMM Process (Lee E.S 2007). The influence of ECMM process parameters on

radial overcut was investigated with RSM based approach (Munda J 2010).

The effective range of the process parameters for moderate Material

Removal Rate (MRR) with lesser overcut for 304 Stainless Steel was investigated

(Thanigaivelan R 2010). An ECMM experimental setup with constant electrode

gap control system was used to study the influence of tool tip shape and

machining gap on MRR (Thanigaivelan R 2010). The literature study reveals that

only a few authors have investigated the performance of ECMM process. Further

investigation is required for improvement of machining performance of ECMM

process for many newly developed difficult-to-cut materials.

The miniaturization of various ultra precision parts required for

producing high precision machines and equipments (Bhattacharyya B 2002)

necessitates the development of manufacturing processes capable of

performing micro manufacturing activities. Recent changes in society

demands us to introduce more and more micro-parts into various types of

industrial products. For example, the fuel injection nozzle for automobiles,

several regulations arising from environmental problems have forced

manufacturers to improve the design of compact, accurate, and efficient

nozzles. Inspection of internal organs of human body and surgery without

pain are universally desired. Miniaturization of medical tools is one of the

effective approaches to arrive at this target.

5

Micromachining technology increasingly plays a decisive role in

the miniaturization of components ranging from biomedical applications to

chemical micro-reactors and sensors. The conventional machining methods

can also be used for micro machining of difficult to machine materials, but

the problems generally faced are high tool wear, rigidity problem of the tool,

heat generation at the tool-work piece interface, etc.

Non-traditional machining processes, especially ECMM is getting

importance due to its versatility and control over the process parameters. In

non-conventional machining, most of the processes are thermal oriented,

e.g. Electro discharge machining (EDM), laser beam machining (LBM),

Electron beam machining (EBM), etc. These processes may cause thermal

distortion of the machined surface. Chemical machining and Electrochemical

machining are thermal free processes, but chemical machining cannot be

controlled precisely for the micromachining domain (Bhattacharyya B 2003).

ECMM appears to be a very promising micromachining technology due to its

advantages that include high MRR, better precision and control, rapid

machining time, and environmentally acceptable (Datta M 1997). ECMM

also permits machining of chemically resistant materials like Titanium,

Copper alloys, Super alloys, and Stainless steel, which are widely used in

biomedical, electronic, and MEMS applications.

1.1.2 Electrochemical Machining

Electro chemical machining is a material removal process similar to

electro plating. In this process, the work piece to be machined is connected to

anode i.e. positive terminal and tool is connected to cathode i.e. negative terminal

of an electrolytic cell with an electrolyte made by a salt solution. The tool and

work piece is kept in such a way that there a gap measuring in microns is

6

maintained between them. On the application of potential difference between the

electrodes and when adequate electrical energy is available between tool and

work piece, positive metal ions leave the work piece. Since electrons are removed

from the work piece, oxidation reaction occurs at the anode. The electrolyte

accepts these electrons resulting in reduction reaction. Ion displacement is the

phenomenon of material removal from work piece in electrochemical machining.

Hence the positive ions from the metal react with the negative ions in the

electrolyte forming hydroxides and thus the metal dissolute forming a precipitate.

The electrolyte is constantly flushed in the gap between tool and the

work piece to remove contaminated electrolyte. The non removal of electrolyte

with suspended precipitate from the machining zone leads to accumulation of

debris. This accumulation cause short circuit between the electrodes. The

electrolyte also carries away hydrogen bubbles created at the machining zone.

The tool electrode is advanced into work piece for the machining to be carried

out. A pumping system fitted with electrolyte filter is used to circulate the

electrolyte as the electrolyte carries away machining waste along with the heat

(Dayanand S. B. 2007).

Hard metals can be shaped using ECMM process and the rate of

machining does not depends on their hardness. The tool electrode used in the

process does not wear, and therefore soft metals can be used as tools to form

shapes on harder work pieces, unlike conventional machining method. The tool is

guided towards the work piece to maintain a constant inter electrode gap (IEG)

between them. If the tool feed is not in sync with the machining, either too high

movement of electrode or insufficient movement of electrode will occur. This

will result in either contact between anode and cathode or too large IEG. In both

cases, the premature termination of machining process will occur. Hence a

constant IEG is to be maintained to achieve desired machining.

7

1.1.3 Basic Principles of ECMM Process

There are many industrial processes which works based on the

principal of Faraday’s law of electrolysis. ECM is one of them (Mukherjee S.K

2005). It is considered as the reverse of electroplating process. The major

difference between the ECM process and other electrolytic processes is that, in

ECM there it not merely the removal of material from work piece but also the

change of shape and size of work piece in a controlled manner.

Ions and electrons crossing phase boundaries (the interface

between two or more separate phases, such as liquid-solid) would result in

electron transfer and the reactions carried out at both anode and cathode.

The potential difference is fundamental in understanding the energy

distribution during the ECM process.

In ECMM, to enhance the MRR pulsed current and pulsed voltage

are applied. The use of pulsed voltage and pulsed current enhances the

activity of the cathode by reducing the cathode ionization while improving the

energy usage of the ECMM process effectively (De Silva A.K.M 1998).

The physical model of ECMM is shown in the figure 1.1.

1.1.4 Mechanism of Material Removal in ECMM

Atom-by-atom removal of metal by anodic dissolution is the basic

principle underlying electrochemical metal removal process. The movement

of the ions is accompanied by electrons flow in the opposite direction to the

positive current in the electrolyte (Hocheng H 2003). The reactions are the

consequence of the applied potential difference, that is, voltage from the

8

electric source. The phenomena can be embodied in Faraday’s laws of

electrolysis:

Figure 1.1: Physical Model of ECMM

1. The amount of any substance dissolved or deposited is directly

proportional to the amount of electricity which has conducted.

2. The amount of different substances deposited or dissolved by the

same quantity of electricity is proportional to their chemical

equivalent weights. Since the electrolyte serves as the conductor

of electric current, Ohm’s law could be applied to this type of

conductor.

The Faraday’s law indicates a relation between the numbers of

electrons removed from an atom and the mass of the atom that would

9

dissolve into electrolyte. The simple expression of Faraday’s law can be

described as:

m =kIt (1.1)

where k is the electrochemical equivalent of the anode metal

(=A/(Z·F) in (g/C))

m is the mass of material dissolved

I is the electric current (A)

T is the machining time

A is the atomic weight of dissolving ions

Z is the valence of dissolved ion immediately after dissolution

F is the Faraday’s constant of 96,487 Coulombs (C)

Figure 1.2: Mechanism of ECMM

Ion dissolution valence is required in describing the dissolution

electrochemical process and calculating material removal according to

Faraday’s law. Table 1.1 shows the dissolution valences of some metals in

different metal electrolyte. Ions valence can be varied in different solutions and

process conditions (Masuzava T 2000). The figure 1.2 shows the mechanism of

ECMM. Table 1.2 presents the comparison between ECM and ECMM.

10

Table 1.1: Dissolution valence for different metals

Metal Electrolyte Dissolution valenceNi NaCl 2Ni NaNO3 2*Fe NaCl 2 and 3 Fe NaNO3 2*Cr NaCl 6Cr NaNO3 6

*Accompanied by oxygen evolution

Table 1.2: Comparison between ECM and ECMM

Parameters ECM ECMM

Voltage 10-30 V < 10 V

Current density 20-200 A/cm2 75-100 A/cm2

Power supply Continuous / pulsed Pulsed

Frequency Hz-KHz range KHz-MHz range

Electrolyte flow 10-60 m/s < 3 m/s

Tool size Large to medium Micro

Inter electrode gap 100-600 um 5-50 um

Surface finish Good Excellent

1.1.5 Advantages of ECMM

ECMM offers several advantages over other competing technologies.

These advantages have made ECM the best choice for a variety of applications.

The product after processing is free of burrs

No-contact process principle

The process does not cause thermal or physical strain in the product

Unlike other machining techniques, no upper-layer deformation

3-Dimensional processing in single step (Kurita T 2006)

11

High surface quality level attainable depending on material

High dimensional accuracy attainable (Lee S.J 2008)

No local rust formation on the surface of the workpiece

Gives more freedom of design a product

ECMM is a technique with high machining speed at low costs

Low running and tooling costs (Jinjin Zhou 2005)

The hardness, toughness and thermal resistance has no effect

MRR is high.

MRR is almost independent on the type of material.

Hard and tough alloys are machined at the same speed.

Electrolyte regeneration (micro filtration) has enabled the cleaning

of the electrolyte to a ppm level and can therefore be reused

indefinitely. The produced sludge can often be recycled, depending

on composition and hence environmentally acceptable

Hence, ECMM has emerged as a most widely used non-conventional

technology for machining micro/meso scale components.

1.1.6 Disadvantages of ECMM

In spite of various advantages, ECMM has following few disadvantages.

Each product and material require new research for optimization

Higher production numbers are essential, as a special electrode must

be developed for each product (Jerzy Kozak 2004). The optimum

number of products depends on complexity and material

12

Design of electrode is complex and initially expensive, it can however

be used for difficult-to-machine materials cost effectively.

1.1.7 Applications of ECMM

Due to numbers of advantages of this process, this method is

successfully used for the machining of high strength alloys and chemically

resistant material like nickel, stainless steels etc. It also finds majority of its

applications in deburring and hole drilling (Mithu M.A.H. 2011). The fuel

injection nozzle for automobiles, several regulations arising from environmental

problems have forced manufacturers to improve the design to produce compact

nozzle with high accuracy.

Micromachining technology is extensively used to machine complex

shapes required in medical and electronics industries. Further ECMM is

a promising and cost effective solution for various industrial applications such as

micro slots, complex surface finishes, drilling large number of micro-holes in

a single work piece, etc.

The ECMM process is capable of machining tough and hard materials

without inducing any residual stress and tool wear (Joao Cirilo da Silva Neto

2006). In this process no physical force is directly applied to the material, the

finished work piece is free from any deformation. The ECMM method can be

effectively used for high precision machining operations as this method offers

extensive control over various process parameters which directly affect the

machining process, especially MRR and dimensional deviation.

13

Deburring the machined components manually is a time consuming

process. Electrochemical machining with its inherent advantages is a suitable

choice for deburring. A flat faced tool is used to remove the surface asperities on

the workpiece. As the tool is moved slowly towards the workpiece surface it

encounters the burrs first. Since the tool is relatively large in comparison to the

burrs and the current densities are high at the peaks of the burrs, they are

machined first. This is a fast and simple to control process, being used vastly in

precision manufacturing (Hai Ping Tsuia 2008).

Flushing the precipitate is crucial in ECMM drilling (Mohan Sen

2005). Otherwise the machined particles would pile up and form a short circuit

between tool and workpiece. In order to ensure the material removal only at the

tip of the machining tool, a protective coat with an insulating material is applied

on the sides of the tool and thus quality of drilling is maintained.

In ECMM, a constant gap, termed as Inter Electrode Gap (IEG) is

maintained between the tool and the workpiece as the tool progresses into the

workpiece. In contrast to other processes the electrolyte flow is all over the

workpiece. This process is mainly used to manufacture complex shaped micro

structure components in electronics and medical industries (Rajurkar K.P 2006).

It is also widely used in shaping the high precision components for aerospace

industry.

1.2 OBJECTIVES OF PRESENT INVESTIGATION

Nickel-based alloys are the most sought material for manufacturing

machines and components which needs to withstand high temperature, high

pressure and aggressive chemical environment. Nickel alloys find wide

14

application in a) gas turbines, b) high temperature fasteners, c) chemical

processing and pressure vessels, and d) reactors of nuclear plants, etc.

Micromachining technology enables machining of miniature and

complex to machine shapes and surfaces, drilling of micro-holes, and other

special requirements in electronic industries (Zhang Z 2007). These things are

also performed by using conventional machining techniques, but the problems

generally faced are a) tool wear, b) rigidity problem of the tool, and c) heat

generation at the tool–work piece interface (Jerzy Kozak 2004). In general, it is

very difficult to produce complex shapes without compromising the quality by

using conventional techniques due to its own limitations.

The micro machining of Nickel alloys can be difficult using traditional

machining techniques as they easily harden during machining. High pressure is

developed between the tool and the work piece during machining. Such high

pressure produces a stressed layer of deformed metal on the surface of the work

piece. This deformation causes a hardening effect on the surface of the work

piece that slows down further machining. Due to this reason, age-hardened

Nickel-base alloys, such as alloy Inconel 600, are machined using an aggressive

but slow cut with a hard tool that minimizes the number of passes required. The

application of ECMM for Nickel alloy is more suitable but it cannot be applied

effectively unless the process parameters are optimized.

The levels of process parameters for experimentation is generally

selected either based on the experience or from the propriety machining

handbook. In most cases, selected parameters are conservative and far from

optimum. Extensive and laborious experimentation involving huge time and cost

is required to select the optimum parameters without optimization technique.

Hence, it is essential to use suitable optimization technique to study the complete

15

range of level of process parameter with least number of experiments. The

analysis of variance (ANOVA) is used to verify statistical significance of the

process parameters on MRR. Finally, a non-traditional optimization technique

called Genetic Algorithm (GA) (Jain N.K 2007) is used to derive the optimized

values of process parameters for the maximum MRR.

The objective of this research is to study the influence of ECMM

process parameters such as electrolyte concentration, machining voltage,

machining current, duty cycle and frequency on MRR of Nickel, SDSS and

Inconel 600. Further, design of experiments employing Taguchi’s Technique,

ANOVA and Genetic Algorithms are used to optimize the process parameters.

1.3 OUTLINE OF THE RESEARCH WORK

Figure 1.3 : Outline of Research Work

Experimental investigations of ECMM on nickel and its alloys

Machining of Nickel

Establishing relationship of MRR,

DD and Process Parameters

Optimization of Process Parameters

Comparative Analysis

Conclusion

Machining of SDSS




Machining of Inconel 600




16

1.4 STATEMENT OF THE PROBLEM

An elaborate literature survey has been done and inferred that only

a very few authors have investigated the performance of the ECMM process and

its parameters. Further investigation is required for machining performance

improvement for many newly developed difficult-to-cut materials.

Experiments are to be conducted to understand the influence of the

various ECMM parameters on MRR. Statistical and optimization techniques play

an important role in modeling the machining parameters and performing the

optimization of machining parameters for achieving the selected objectives.

Further research is needed to optimize the ECMM process parameters for the

most widely used materials like Nickel, Super Duplex Stainless Steel and Inconel

600 is need of the hour since use of these materials has grown in many industries.

This research mainly concentrates in finding optimum ECMM process

parameters to achieve maximum MRR in Nickel, Super Duplex Stainless Steel

and Inconel 600 alloys. The parameters subjected to the study are 1) Electrolyte

Concentration 2) Machining Voltage, 3) Machining Current, 4) Duty Cycle, and

5) Frequency.

In this study, Taguchi methodology is used to conduct complete

analysis of influence of process parameters with least number of experiments.

The analysis of variance (ANOVA) is used to verify statistical significance of the

process parameters and its optimal combination for maximum MRR. A non-

traditional optimization technique called Genetic Algorithm (GA) is used to

optimize the process parameters for maximum MRR. Necessary confirmation

experiments are conducted and the results are verified with the GA results.

17

CHAPTER 2

LITERATURE SURVEY

2.1 REVIEW OF LITERATURE

ECMM is an important machining process in many manufacturing

industries, viz. aero space, bio-medical, electrical, and electronics, auto mobile,

thermal power plants, nuclear power plants, etc., where hard to cut materials are

used. Several researchers have attempted to improve the performance

characteristics of ECMM process by studying the effect of process parameters on

the machining process. But the full potential utilization of ECMM process is yet

to be achieved. This is due to complex and stochastic nature and number of

variables involved.

2.2 OVERVIEW ON ECMM

The literature survey made for this research work revealed that the

researches conducted on ECMM are related to recent trends in ECMM and

effects of process parameters on MRR. It is also inferred that more research

involving number of process parameters are to be done in this area.

The research titled "Electrochemical machining: new possibilities

for micromachining" highlights various design and development activities of

an ECMM system set up (Bhattacharyya B. 2002). A successful attempt has

been made to develop an ECMM setup for carrying out in-depth independent

18

research for achieving satisfactory control of ECM process parameters to meet

the micromachining requirements. The developed ECMM setup mainly

consists of various sub-components and systems, e.g., mechanical machining

unit, micro tooling system, electrical power, and controlling system and

controlled electrolyte flow system, etc. All these system components are

integrated in such a way that the developed ECMM system setup will be

capable of performing fundamental research in the area of ECMM fulfilling

the requirements of micromachining objectives.

The recent developments and future trends of EMM were

highlighted in the research titled “Advancement in electrochemical micro

machining” (Bhattacharyya B 2004). It suggests that micro-ECM (ECMM)

method can be effectively used for high precision machining operations such

as removal of burrs, making patterns in foils, and 3D micro-machining. The

research suggests that for utilizing ECMM in micro fabrication, improvement

in micro tool design and development, monitoring and control of the inter

electrode gap (IEG), control of material removal and accuracy, power supply,

and elimination of micro-sparks generation in IEG, and selection of electrolyte

is required.

The study titled “Process monitoring of electrochemical

micromachining” shows the importance of inter-electrode gap in ECMM set

up (De Silva A.K.M 1998). Electrochemical micro-machining utilizes very

small inter electrode gaps in order to obtain the accuracies. The narrow gaps

make the control of process much more complex than normal ECM. In order

to formulate spark prevention and gap control strategies, this paper investigate

the discharge mechanism in narrow electrolytic gap.

19

The paper titled “Electrochemical micro-machining : new

possibilities for micro-manufacturing”, highlights the design and development

of ECMM set up which includes various component like mechanical

machining components, electrical system and an electrolyte flow system etc.

(Bhattacharyya B 2001). A microprocessor controlled IEG controlling system

has been developed for this setup. The set up has versatile system components

such as controlled tool feed, controlled electrolyte flow, and pulse power

supply. The developed ECMM set up opens many challenging possibilities for

effective utilizations of the electrochemical material removal mechanism.

The paper titled “A review of electrochemical macro to micro-hole

drilling processes”, discusses about the Electrochemical machining processes

for drilling macro and micro holes with exceptionally smooth surface and

reasonably acceptable taper in numerous industrial applications particularly in

aerospace, electronic, computer and micro-mechanics industries (Mohan Sen

2005). Also this paper highlights about the hole-drilling processes like jet

electrochemical drilling have found acceptance in producing large number of

quality holes in difficult-to-machine materials. This paper highlights the recent

developments, new trends, and the effect of key factors influencing the quality of

the holes produced by these processes

The research titled “Selected problems of microelectrochemical

machining”, included the study of electrochemical copying of slots, mini

holes, grooves, and insulating groove features (Jerzy Kozac 2004). The

limiting conditions of ECMM are considered from the point of view of

copying and micro shaping using non profiled tool electrodes. For improving

micro machining capabilities of ECM processes, the application of ultra short

pulse current and ultra small gap size is recommended which is main point of

discussion in this paper.

20

A rare application of electrochemical micromachining was

discussed in the paper titled “Electro-chemical micro drilling using ultra short

pulses” (Se Hyun Ahn 2004). In this work, ultra short pulses with tens of

nanoseconds duration are used to localize dissolution area. The effect of

voltage, pulse duration, and pulse frequency on localization distance were

studied. High quality micro holes with 8 micron diameter were drilled on 304

stainless steel foil having 20 micron thickness.

An ECµM system with a machining gap control system was

discussed in the research titled “A study of three-dimensional shape machining

with an ECµM System” (Kurita T 2006). The applications of ultra short pulse

current and ultra small gap size improves micromachining capabilities of ECM

process. The utilization of edge cut electrode is advantageous to machine

micro holes with high aspect ratio.

In the work titled “Localized electrochemical Micromachining with

gap control”, an approach to electrochemical micromachining was presented

in which side-insulated electrode, micro gap control between the cathode and

anode, and the pulsed current are synthetically utilized (Li Yong 2003). An

experimental set-up for electrochemical micromachining is constructed, which

has machining process detection and gap control functions; also a pulsed

power supply and a control computer are involved in. Microelectrodes are

manufactured by micro electro-discharge machining (EDM) and side-insulated

by chemical vapor deposition (CVD). A micro gap control strategy is

proposed based on the fundamental experimental behavior of electrochemical

machining current with the gap variance. Machining experiments on micro

hole drilling, scanning machining layer-by-layer, and micro electrochemical

deposition are carried out. Preliminary experimental results show the

21

feasibility of electrochemical micromachining and its potential capability for

better machining accuracy and smaller machining size.

An experimental set up for micro electro chemical machining was

developed with a machining gap control system for the research titled

“Theoretical and Experimental investigation on electrochemical micro

machining” (Zhang Z 2007). Experiments were conducted to identify the

optimum parameters for machining voltage, pulse on time, piezo oscillation

amplitude and electrolyte concentration. Based on the optimum parameters,

three dimensional shapes with sub millimeter range was successfully

machined.

The research titled “Electrochemical Micromachining of Stainless

Steel by Ultra short Voltage Pulses” discusses the application of ultra short

voltage pulses to a tiny tool electrode under suitable electrochemical

conditions enables precise three-dimensional machining of stainless steel

(Laurent Cagnon 2003). In order to reach sub micrometer precision and high

processing speed, the formation of a passive layer on the work piece surface

during the machining process has to be prevented by proper choice of the

electrolyte. Mixtures of concentrated hydrofluoric and hydrochloric acid are

well suited in this respect and allow the automated machining of complicated

three-dimensional microelements. The dependence of the machining precision

on pulse duration and pulse amplitude was investigated in detail.

A comprehensive mathematical model for analyzing the effects of

various process parameters on the micro-spark and stray current affected zone

was studied in the research titled “Control of micro spark and stray current

effect during EMM process” (Munda J. 2007). Micro-spark and stray current

22

affected zone has been reduced as low as 0.0001 mm under proper controlled

machining parametric combination.

The paper titled “Influence of tool vibration on machining

performance in electrochemical micro-machining of copper” highlights the

influence of various electrochemical micromachining parameters like

machining voltage, electrolyte concentration, pulse period and frequency on

material removal rate, accuracy and surface finish in microscopic domain

(Bhattacharyya B 2007). According to their experimental study, the most

effective values for micromachining parameters have been considered as 3 V

machining voltage, 55 Hz frequency, and 20 g/l electrolyte concentration that

can enhance the accuracy with highest possible amount of material removal.

The research titled “Experimental investigation on the influence of

electrochemical machining parameters on machining rate and accuracy in

micromachining domain”, has made an attempt to develop an EMM

experimental set-up for carrying out in-depth research for achieving a

satisfactory control of the ECMM process parameters to meet the

micromachining requirements (Bhattacharyya B 2003). Keeping in view these

requirements, sets of experiments have been conducted to investigate the

influence of some of the predominant electrochemical process parameters such

as machining voltage, electrolyte concentration, pulse on time, and frequency

of pulsed power supply on the material removal rate (MRR) and accuracy to

achieve the effective utilization of electrochemical machining system for

micromachining. A machining voltage range of 6 to 10 V gives an appreciable

amount of MRR at moderate accuracy

23

The paper titled “Experimental research on the localized

electrochemical micro-machining” proposes a method of electrochemical

micromachining of micro hole or dimple array, in which a patterned insulation

plate coated with metal film as cathode is closely attached to work piece plate

(Zhang Z 2008). When voltage is applied across the work piece and cathode

film over which the electrolyte flows at high speed, hole or dimple array will

be produced. The proposed technology offers unique advantages such as short

lead time and low cost. The effect of process parameters on the microstructure

shape was demonstrated numerically and experimentally. Arrays of holes or

dimples of several hundred micrometers diameter have been produced.

The work titled “Experimental investigation into electrochemical

micromachining (EMM) process”, with a suitable ECMM setup mainly

consists of mechanical machining unit, micro-tooling system, electrical power,

and controlling system and controlled electrolyte flow system to control

electrochemical machining (ECM) (Bhattacharyya B 2003). Investigation

indicates most effective zone of predominant process parameters such as

machining voltage and electrolyte concentration, which give the appreciable

amount of material removal rate (MRR) with less overcut. The experimental

results and analysis on ECMM will open up more application possibilities for

ECMM.

The research work titled “Experimental study on the influence of

tool electrode tip shape on Electrochemical Micromachining of 304 stainless

steel”, used an experimental set-up with constant gap control system

(Thanigaivelan R 2010). The experimental study on the influence of tool tip

shape on machining rate and machining gap for 304 stainless steel has been

presented. The tool electrode tips of different shapes like flat ended, conical

ended, round ended and wedge shape are used for this study. The experimental

24

results show that the round ended tip improves the machining rate and conical

shape tip reduces the machining gap when compared with the other shapes.

In the paper titled “Experimental study of overcut in

electrochemical micromachining of 304 stainless steel” an attempt was made

to determine optimum machining condition of ECMM for 304 SS

(Thanigaivelan R 2010). From the experimental results, it is evident that the

most effective range of pulse on-time and electrolyte concentration can be

considered as 25-30 ms and 0.23-0.29 mole/l, which gives lower overcut.

Overcut increases with increase in pulse on-time and machining voltage. After

the preliminary ECMM experiments the Taguchi experimental design has

been applied to determine the optimal combinations of the machining

parameters levels. According to the Taguchi’s quality design concepts, a L16

orthogonal array was used. The optimal combinations of machining

parameters levels for lesser overcut are machining voltage at 12V, pulse on-

time at 25ms,machining current at 0.8 A and then electrolyte concentration of

.29 mole/l.

An experimental study titled “Study of dominant variables in

Electrochemical Micromachining” was carried out to determine the effects of

dominant variables like pulse on time, electrolyte concentration and voltage on

machining speed and overcut of stainless steel (Thanigaivelan R 2010). With

the experimental results, it is inferred that machining speed reaches maximum

at a pulse on time of 30 ms. The most effective range of pulse on time and

electrolyte concentration can be considered as 25-30 ms and 0.23-0.29 mol/lit

which gives moderate machining speed and lower overcut.

25

The paper titled “Investigation into the influence of Electrochemical

Micromachining (EMM) parameters on Radial Overcut through RSM-based

approach” highlights the features of the development of mathematical model for

correlating the interactive and higher-order influences of various machining

parameters (Munda J 2010). This paper also highlights mathematical models for

analyzing the effects of various process parameters on the machining rate and

overcut phenomena. These parameters can be used in order to achieve

maximization of the metal removal rate and the minimum overcut effects for

optimal accuracy of shape features.

The work titled “Hole quality and inter electrode gap dynamics during

pulse current electrochemical deep hole drilling” presents an experimental

investigation of pulse-current shaped-tube electrochemical deep hole drilling

(PC-STED) of nickel-based superalloy (Dayanand S. B. 2007). Influence of

five process variables (voltage, tool feed rate, pulse on-time, duty cycle, and

bare tip length of tool) on the responses, namely, depth-averaged radial

overcut (DAROC), mass metal removal rate (MRRg), and linear metal

removal rate (MRRl) have been discussed. Mathematical models have been

developed to express the effects of these process variables. The proposed

model permits quantitative evaluation of the hole quality and process

performance simultaneously. The results have been confirmed for the profile

of the drilled hole and MRRl obtained experimentally. In all the experiments,

through holes of 26 mm depth with diameters ranging from 2.205 mm to 3.279

mm were drilled. The results have been explained by the inter electrode gap

dynamics prevailing during pulse electrochemical deep hole drilling. Optimum

parameters determined from these experiments can be used to efficiently drill

high-quality deep holes.

26

The paper titled “Effect of over voltage on Material Removal Rate

during Electrochemical machining” gives a report about the MRR in

electrochemical machining by using over voltage and conductivity of the

electrolyte solution (Mukherjee S.K 2005). It is observed that over voltage plays

an important role equilibrium gap and tool feed rate. MRR decreases due to

increase in over voltage and decrease in current efficiency, which is directly

related to the conductivity of the electrolyte solution.

The study titled “State of the art of micromachining” discusses about

the miniaturization in manufacturing various types of industrial products

(Masuzava T 2000). Micromachining is the foundation of the technology to

realize such miniaturized products. In this paper, the author summarizes the

basic concepts and applications of major methods of micromachining. The

basic characteristics of each group of methods are discussed based on different

machining phenomena. Promising methods are introduced in detail hinting at

suitable areas of application. Finally, the present state of these technologies is

shown with examples of experimental and practical applications.

The work titled “Experimental investigation of microholes in

electrochemical machining using pulse current” investigates the influences of

some of the predominant electrochemical process parameters such as pulse

frequency, feed rate of tool, machining voltage, and electrolyte concentration

on the machining accuracy of micro-holes (Zhiyong Li 2008). According to

the investigation, the most effective zone of pulse on time and electrolyte

concentration can be considered as 15-50 µs and 30-50 g/l, respectively, which

can gives a desirable machining accuracy for micro-holes. A machining

voltage range of 6-10 V can be commended to obtain high machining

accuracy. From the micrographs of the machined micro-holes, it may be

observed that a lower value of electrolyte concentration with moderate

27

machining voltage and moderate value of pulse on time will produce more

accurate shape of micro-holes.

In the paper titled “Electrochemical micromachining with ultrashort

voltage pulses – a versatile method with lithographical precision” discusses

about the application of ultrashort voltage pulses electrochemical reactions

(Kock M 2003). As an example, electrochemical machining parameters for the

micromachining of Ni are derived from conventional electrochemical cyclic

voltammetry. Depending on the average potentials of tool and workpiece,

overall corrosion of the workpiece and the location of the counter reaction of

workpiece dissolution can be controlled. The pulse duration provides a direct

control for setting the machining accuracy. Machining precisions below 100

nm were achieved by the application of 500 ps voltage pulses.

The research paper titled “Investigation into electrochemical

micromachining (EMM) through response surface methodology based

approach”, attempts to establish a comprehensive mathematical model for

correlating the interactive and higher-order influences of various machining

parameters through response surface methodology (RSM) (Munda J 2008).

Validity and correctness of the developed mathematical models have also been

tested through analysis of variance. Optimal combination of these predominant

micromachining process parameters is obtained from these mathematical

models for higher machining rate with accuracy. Considering MRR and ROC

simultaneously optimum values of predominant process parameters have been

obtained as; pulse on/off ratio, 1.0, machining voltage, 3 V, electrolyte

concentration, 15 g/l, voltage frequency of 42.118 Hz and tool vibration

frequency as 300 Hz.

28

The research paper titled “Improving Machining Accuracy of the EMM

Process through Multi-Physics Analysis” studies the parametric effects of the

EMM process by both numerical simulation and experimental tests (Shuo Jen

Lee 2007). The numerical simulation was performed using commercial

software, FEMLAB, to establish a multi-physics model which consists of

electrical field, convection, and diffusion phenomena to simulate the

parametric effects of pulse rate, pulse duty, electrode gap and inflow velocity.

From the simulated results, the relationship between parameters, and the

distribution of metal removal could be established. Proper process variables

were also chosen to conduct the EMM experiments. After the experiments, the

profile of the processed rectangular slot was measured by a Keyence digital

microscope. Comparing profile of the processed rectangular slot with the

profile of the cathode, the machining accuracy of EMM process could be

determined. It could also verify the goodness of the multi-physics model for

predicting machining accuracy. From this study, the effects of parameters such

as pulse rate, pulse duty, electrode gap, and inflow velocity are better

understood. The simulation model could be employed as a predictive tool to

provide optimal parameters for better machining accuracy and process

stability of the EMM process.

The investigation titled “Electrochemical micromachining,

polishing and surface structuring of metals: fundamental aspects and new

developments” discusses about the application of Electrochemical

micromachining (EMM) as a versatile process for machining and surface

structuring of metallic materials for biomedical and micro systems (Landolt D

2003). From a fundamental point of view EMM presents many similarities

with electrochemical machining (ECM) and electro polishing (EP) provided

one takes into account the scale dependence of phenomena. In the present

paper the role of mass transport, current distribution, and passive films for

shape control and surface smoothing is discussed and illustrated with

29

examples. The usefulness of numerical simulation using simplified models is

stressed. New developments in EMM of titanium are presented, including

oxide film laser lithography permitting EMM on non-planar surfaces without

photo resist and the fabrication of two-level and multi-level structures. Scale

resolved electro chemical surface structuring of titanium leads to well-defined

topographies on the micrometer and nanometer scales, which are of interest

for biomedical applications.

The technical paper titled “Improvement of Electrochemical

Microdrilling Accuracy Using Helical Tool” presents a microhelical tool as

a novel solution in electrochemical microdrilling process to improve the

machining accuracy and ability (Hai-Ping Tsuia 2008). Fluent CFD is adopted

to analyze the flow field status in process. The inlet and outlet diameters of the

microholes are 425 µm and 362 µm, respectively; the values are obtained

using the conventional microsolid cylindrical tool. When the rotation speed of

the helical tool is 20,000 rpm, and the pulse-off time is 90 µs, the inlet and

outlet diameter significantly decline to 335 µm and 299 µm. The experimental

results reveal that the accuracy of microhole shape can be significantly

improved using the microhelical tool in a simple and low-cost way.

The study titled “A study of the characteristics for electrochemical

micromachining with ultrashort voltage pulses” about the application of

voltage pulses between a tool electrode and a workpiece in an electrochemical

environment that allows the three-dimensional machining of conducting

materials with micrometer precision (Lee E.S 2007). In this paper, tool

electrodes (5 m in diameter, 1 mm in length) are developed by EMM and

microholes are manufactured using these tool electrodes. Microholes with

a size of below 50 m in diameter can be accurately achieved by using

ultrashort voltage pulses (1–5 s).

30

The article “A step towards the in-process monitoring for

electrochemical microdrilling”, presents a step towards the in-process

monitoring based on waveforms generated during electrochemical

micromachining (Mithu M.A.H 2011). An attempt has been made to correlate

between the waveforms generated during machining and experimental

outcomes such as material removal rate, machining time, and the dimensions

of the microholes fabricated on commercially available nickel plate with

prefabricated tungsten microtools. An electrical function generator is used as

a signal source and a digital storage oscilloscope is provided for observing the

nature of electrical pulses used and recording the waveforms generated during

machining. The waveforms are subgrouped depending on the parameters used

and analyzed to correlate the waveform shape and the machining outcomes.

The digital storage oscilloscope also facilitates for observing the short-circuit

condition which may occur during microdrilling. These results show that the

shape of the waveforms and their corresponding values are in good agreement

with the material removal rate, machining time, and on the dimension of

fabricated microholes. Therefore, the proposed monitoring technique can be

employed as a predictive tool in electrochemical micromachining.

The research titled “Research on pulse electrochemical finishing

using a moving cathode” discusses about the improvement of the surface

quality of parts with a finishing method Pulse Electrochemical Finishing

(PECF) using a Moving Cathode (Jinjin Zhou 2005). The results reveals that

machining with an inter electrode gap as small as possible could smoothen the

anode surface quickly; with an invariable gap size, the current density and the

machining time are two key parameters influencing the smoothening effect;

there is a critical current density above which a bright surface could be

obtained, and this process could finish a large surface area over the critical

current with a low power supply, which is helpful to get a lustrous surface.

31

The result shows that the surface roughness value (Ra) reduces from 0.5 to

0.065 µm and a mirror-like surface is obtained.

The research work titled “A material removal analysis of

electrochemical machining using flat-end cathode” discusses about the process

to erode a hole of hundreds of micrometers on the metal surface (Hocheng H

2003). The paper also discusses the influence of experimental variables

including time of electrolysis, voltage, molar concentration of electrolyte and

electrode gap upon the amount of material removal and diameter of machined

hole. The results of experiments show the material removal increases with

increasing electrical voltage, molar concentration of electrolyte.

The research article titled “Optimization of electro-chemical

machining process parameters using genetic algorithms” discusses about the

optimum choice of the process parameters for the economic, efficient, and

effective utilization of these processes (Jain N.K 2007). Process parameters of

AMPs are generally selected either based on the experience, and expertise of

the operator or from the propriety machining handbooks. In most of the cases,

selected parameters are conservative and far from the optimum. This hinders

optimum utilization of the process capabilities. Selecting optimum values of

process parameters without optimization requires elaborate experimentation

which is costly, time consuming, and tedious. Process parameters optimization

of AMPs is essential for exploiting their potentials and capabilities to the

fullest extent economically. This paper describes optimization of process

parameters of four mechanical type AMPs namely ultrasonic machining

(USM), abrasive jet machining (AJM), water jet machining (WJM), and

abrasive-water jet machining (AWJM) processes using genetic algorithms

giving the details of formulation of optimization models, solution

methodology used, and optimization results.

32

The study titled “Development of micro machining for air-lubricated

hydrodynamic bearings” uses a specially-built EMM / PECM (Pulse

Electrochemical Machining) cell, an electrode tool fitted with non-conducting

material, a electrolyte flow control system and a small & stable gap control unit

are developed to achieve accurate dimensions (Park J.W 2002). Two

electrolytes, aqueous sodium nitrate and aqueous sodium chloride are

investigated in this study. The former electrolyte with few pits on the surface of

workpiece has better machine-ability than the latter one with many pits on the

surface of workpiece. It is easier to control the machining depth precisely with

pulse electrical current than direct electrical current. This paper also presents an

identification method for the machining depth by in-process analysis of applied

electrical current and inter electrode gap size. The inter electrode gap

characteristics, including pulse electrical current, effective volumetric

electrochemical equivalent and electrolyte conductivity variations, are analyzed

using the model and experimental results.

The research work titled “Micro and nano machining by electro-

physical and chemical processes” discusses the issues related to the supporting

technologies such as standardization, metrology, and equipment design

(Rajurkar K.P 2006). Non-technological issues including environmental effects

and education are also discussed.

The investigation titled “Parametric optimization of electrochemical

machining of Al/15% SiCp composites using NSGA-II” discusses about optimal

parameters for improving cutting performance (Senthilkumar C 2011). MRR and

surface roughness are the most important output parameters, which decide the

cutting performance. There is no single optimal combination of cutting

parameters, as their influences on the metal removal rate and the surface

roughness are quite opposite. A multiple regression model was used to represent

33

relationship between input and output variables and a multi-objective

optimization method based on a non-dominated sorting genetic algorithm-II

(NSGA-II) was used to optimize ECM process. A non-dominated solution set

was obtained.

Application of an environmentally friendly electrolyte of citric acid

for micro electrochemical machining of stainless steel has been discussed in the

research paper entitled “Micro fabrication by electrochemical process in citric

acid electrolyte” (Shi Hyoung Ryu 2009). Micro holes of 60 µm in diameter with

depth of 50 µm and 90 µm in diameter with the depth of 100 µm are perforated

using citric acid electrolyte.

The experimental work titled “Intervening variables in

electrochemical machining” throws light on intervening variables in

electrochemical machining (ECM) of SAE-XEV-F Valve-Steel (Joao Cirilo da

Silva Neto 2006). In this research, the material removal rate (MRR), roughness

and over-cut were studied. Four parameters were changed during the

experiments: feed rate, electrolyte, flow rate of the electrolyte and voltage. Forty-

eight experiments were carried out in the equipment developed. Two electrolytic

solutions were used: sodium chloride (NaCl) and sodium nitrate (NaNO3). The

results show that feed rate was the main parameter affecting the material removal

rate. The electrochemical machining with nitride sodium presented the best

results of surface roughness and over-cut.

The Micro electrochemical machining (ECM) using ultra short pulses

with sulfuric acid as electrolyte to machine 3D micro structures on stainless steel

was discussed in the paper titled “Micro Electrochemical Milling” (Kim B.H

2005). This paper shows how to prevent taper, by using a disk-type electrode. To

34

improve productivity, multiple electrodes were applied and multiple structures

were machined simultaneously. Since the wear of electrode is negligible in

ECM.

The article titled "Taguchi concepts and their applications in marine

and offshore safety studies” discusses about how the Taguchi concepts such as

‘quality loss function’, ‘signal-to-noise ratio’, ‘orthogonal arrays’, ‘degree of

freedom’ and ‘analysis of variance’ may be synthesized in maritime safety

engineering studies (How Sing Sii 2001). Brainstorming, an integral part of the

Taguchi philosophy, is also briefly discussed. Orthogonal arrays are used to

study many parameters simultaneously with a minimum of time and resources to

produce an overall picture for more detailed safety-based design and operational

decision-making. The S/N ratio is employed to measure quality; in this case, risk

level. The loss function is considered as an innovative means for deter-mining

the economic advantage of improving system safety or operational safety. Noise

factors are considered as any uncontrollable or uncontrolled variables or any

other undesired influences.

The article, “Optimizing feed force for turned parts through the

Taguchi technique” discusses about an optimal setting of turning process

parameters (cutting speed, feed rate and depth of cut) resulting in an optimal

value of the feed force when machining EN24 steel with TiC-coated tungsten

carbide inserts (Hari Singh 2006). The effects of the selected turning process

parameters on feed force and the subsequent optimal settings of the parameters

have been accomplished using Taguchi’s parameter design approach. The results

indicate that the selected process parameters significantly affect the selected

machining characteristics. The results are confirmed by further experiments.

35

The research paper titled “Micro fabrication by electrochemical metal

removal” discusses about the recent advancements in the electrochemical metal

removal processes for micro fabrication (Datta M 1998). After a brief description

of the process, several important parameters are identified that determine the

material-removal rate, shape control, surface finishing, and uniformity. The

influence of surface film properties, mass transport, and current distribution on

microfabrication performance are discussed. Several examples of

microelectronic component fabrication are presented. These examples

demonstrate the challenges and opportunities offered by electrochemical metal

removal in microfabrication.

The research paper titled "Electrochemical micromachining: An

environmentally friendly, high speed processing technology" discusses about the

wet chemical etching processes that are employed in the manufacturing of a

variety of microelectronic components (Datta M 1997). These processes use

etchants that generally contain aggressive and toxic chemicals, generate

hazardous waste and have limited resolution. Electrochemical metal removal is

an evolving alternate processing technique that involves controlled metal

shaping by an external current, thereby requiring less aggressive and nontoxic

electrolytes. The application of controlled electrochemical metal removal in the

fabrication of microstructures and micro components is referred to as

electrochemical micromachining (EMM). In this paper a recently developed

EMM process and tool for metal mask fabrication is discussed. EMM

performance is compared to that obtained by the conventional chemical etching

process. Obtained results demonstrate the opportunities offered by EMM

particularly as a high-speed, environmentally friendly processing technology.

The investigation work titled "Ultrasonic measurement of the inter

electrode gap in electrochemical machining" discusses about the dependency of

36

the inter electrode gap with time and process parameters and its usage to

determine process characteristics (Clifton D 2002). Defining process variables to

map out the required gap time function requires the use of time-consuming

iterative trials. In-line monitoring of the gap would enable process control and

tool to workpiece transfer characteristics to be achieved (for ideal conditions)

without the requirement to generate such parameter maps. This work explores

the use of ultrasound applied as a passive, non-intrusive, in-line gap

measurement system for ECM. The accuracy of this technique was confirmed

through correspondence between the generated gap-time and current time data

and theoretical models applicable to ideal conditions. The monitoring of the gap

size has also been shown to be effective when determining shape convergence

under ideal conditions, for the example case of a 2D sinusoidal profile.

2.3 OUTCOME OF LITERATURE REVIEW

The literature survey helped to successfully design, construct and

conduct the experimentation of this research work. Some of the major ideas

learnt from the literature survey are listed below.

1. The experimental setup is designed based on the various

requirements stated by above cited literature.

2. The specific studies of each process parameters made by

various authors on for MRR and Dimensional deviation are

helpful to understand the behaviour of each parameter.

3. Necessary ideas were obtained for making a suitable tool for the

current study.

4. Clear outline about Taguchi methodology, and various other

optimization techniques were learnt.

5. It is learnt that experimental investigation considering 5 most

37

influencing process parameters viz. Electrolyte Concentration,

Machining Voltage, Machining Current, Duty Cycle, and

Frequency on MRR is yet to be conducted.

6. It is understood that further research is to be conducted on

Nickel and its alloys for maximum MRR.

7. Further study is needed in the area of Dimensional deviation.

Hence, it is inferred that more in depth research involving

maximum number of process parameters are to be conducted to achieve

maximum MRR with less dimensional deviation for Nickel and its alloys.

38

CHAPTER 3

EXPERIMENTAL DESIGN

3.1 INTRODUCTION

The experimental design methodology is very important in

maintaining the reliability of entire research work. It is useful for conducting

experiments, recording the experimental results, and analysis of the results.

The methodology for the present research work has been designed effectively

to conduct least number of experiments to study the entire spectrum of levels

of ECMM process parameters for Maximum MRR on Nickel and its alloys.

The reduction in number of experiments greatly reduces the time and the cost.

In order to understand the effect of each ECMM process parameter

on MRR and to identify the significant parameters, experiments need to be

conducted by varying the level of each parameter one at a time. This proves

very cumbersome as the number of experiments to be conducted increases

exponentially with the number of process parameters. Hence, it’s highly

difficult to draw any conclusion with minimum number of experiments in this

approach. Hence, well planned set of experiments, in which all parameters of

interest are varied over a specified range, is a much better approach to obtain

systematic data.

Performing the experiments on the sub set of complete set of

experiments makes the experimentation process quick and cost effective. The

39

Taguchi method using the orthogonal array is highly effective in identifying

the sub set of experiments to be done to study the complete range and

combination of process parameters in minimal number of experiments. Hence,

Taguchi methodology is used to for selecting optimum levels of process

parameters and number of experiments required to ensure the quality of

experimentation. Employing this statistical method to design the experiments

and analyze the result sets enables the researcher to find the optimal levels of

process parameters qualitatively. Estimation of the experimental error greatly

helps to improve the quality of experiments conducted.

The result of analysis using ANOVA is highly effective in deriving

inferences regarding the optimum combination of process parameters for

maximum MRR. The use of GA helps to optimize the set of process

parameters under the process constraints. In this research work Taguchi and

ANOVA are utilized to design, experiment, analyze and confirm the

results. The GA is used to optimize the process parameters.

The different phases of experiments and the techniques used for the

experimentation are given in the following paragraphs.

Phase -I Development of experimental setup providing varying range of

input parameters in ECMM and measuring the various

responses.

Investigation of the working ranges and the levels of the

ECMM process parameters (pilot experiments) affecting the

selected quality characteristics, by using one factor at a time

approach.

40

Phase -II Investigation of the effects of ECMM process parameters on

Material Removal Rate (MRR).

Prediction of optimal combinations of ECMM process

parameters.

Experimental verification of optimized characteristics using

Taguchi’s parameter design approach.

Phase -III The Taguchi L18 orthogonal array has been used to plan the

experiments and to find the effects of process parameters on

MRR.

Phase -IV Development of ANOVA optimization model.

Determination of optimal combination of ECMM process

parameters for maximum MRR.

Phase -VDevelopment of optimization model for optimization using

Genetic Algorithms.

Determination of optimal sets of ECMM process parameters.

Verification of coherences between the experimental, ANOVA

and GA results.

41

3.2 TAGUCHI EXPERIMENTAL DESIGN AND ANALYSIS

3.2.1 Taguchi’s Philosophy

Taguchi’s comprehensive system of quality engineering is one of

the greatest engineering achievements of the 20 th century. His methods

mainly focus on the effective application of engineering strategies. It

includes both upstream and shop-floor quality engineering. Upstream methods

efficiently use small-scale experiments to reduce variability and remain cost-

effective, and robust designs for large-scale production. Shop-floor techniques

provide cost-based, real time methods for monitoring and maintaining

quality in production. The farther upstream a quality method is applied, the

greater leverages it produces on the improvement.

Taguchi’s philosophy is founded on the following three very simple

and fundamental concepts (Phillip J. Ross 1988):

Quality should be designed into the product and not inspected into it.

Quality is best achieved by minimizing the deviations from the target.

The product or process should be so designed that it is immune to

uncontrollable environmental variables.

The cost of quality should be measured as a function of deviation from

the standard and the losses should be measured system-wide.

Taguchi proposes an “off-line” strategy for quality improvement as

an alternative to an attempt to inspect quality into a product on the production

line. Taguchi observes that poor quality cannot be improved by the process of

inspection, screening and salvaging since no amount of inspection can put

42

quality back into the product. Taguchi recommends a three-stage process:

System Design, Parameter Design and Tolerance Design (Phillip J. Ross 1988).

In the present work Taguchi’s Parameter Design approach is used to study the

effect of process parameters on the various responses of the ECMM process.

3.2.2 Experimental Design Strategy

Taguchi recommends orthogonal array (OA) for layout of

experiments. These OA’s are generalized Graeco-Latin squares. To design an

experiment, suitable OA is to be selected. Then the parameters and interactions

of interest are to be assigned to appropriate columns. Use of linear graphs and

triangular tables suggested by Taguchi makes the assignment of parameters

simple. The array forces all experimenters to design almost identical

experiments (Roy R.K 1990).

In the Taguchi method the results of the experiments are analyzed to

achieve one or more of the following objectives (Phillip J. Ross 1988):

To establish the best or the optimum condition for a product or

process

To estimate the contribution of individual parameters and

interactions

To estimate the response under the optimum condition

The optimum condition is identified by studying the main effects of

each of the process parameters. The main effects indicate the general trends of

influence of each parameter. The knowledge about individual parameters and its

contributions is a key in deciding the nature of control to be established on

43

a production process. The analysis of variance (ANOVA) is a statistical

treatment most commonly applied to the results of the experiments in

determining the percent contribution of each parameter against a stated level

of confidence. Study of ANOVA table for a given analysis helps to determine

which of the parameters need control (Phillip J. Ross 1988).

Taguchi suggests (Roy R.K 1990) two different routes to carry

out the complete analysis. First, as a standard approach, the results of a single

run or the average of repetitive runs are processed through main effect and

ANOVA analysis. In the second approach, as per Taguchi’s recommendations,

multiple runs are used to analyze Signal-to-Noise ratio (S/N). The S/N ratio is

a concurrent quality metric linked to the loss function (Barker T.B 2005). By

maximizing the S/N ratio, the loss associated can be minimized. It is sufficient

to generate repetitions at each experimental condition of the controllable

parameters and analyze them using an appropriate S/N ratio.

In the present investigation, the S/N data analysis has been performed.

The effects of the selected ECMM process parameters for maximum MRR have

been investigated through the plots of the main effects. The optimum condition

for maximum MRR has been established through S/N data. No outer array has

been used and instead, experiments have been conducted two times at each

experimental condition.

Loss Function

The heart of Taguchi method is his definition of the nebulous and

elusive term quality as the characteristic that avoids loss to the society from the

time the product is shipped. Loss is measured in terms of monetary units and

44

is related to quantifiable product characteristic. Taguchi defines quality loss via

his loss function. He unites the financial loss with the functional specification

through a quadratic relationship that comes from a Taylor series expansion.

The quadratic function takes the form of a parabola. Taguchi defines the loss

function as a quantity proportional to the deviation from the nominal quality

characteristic. He has found the following quadratic form to be a workable

function (Roy R.K 1990):

L(y) = k (y-m)2 (3.1)

Where,

L = Loss in monetary units

m = value at which the characteristic should be set

y = actual value of the characteristic

k = constant depending on the magnitude of the characteristic and

the monetary unit involved

Figure 3.1: Taguchi Loss Function

The characteristics of the loss function are (Roy R.K 1990):

45

The farther the product’s characteristic varies from the target

value, the greater is the loss. The loss must be zero when the

quality characteristic of a product meets its target value.

The loss is a continuous function and not a sudden step as in

the case of traditional goal post approach. This characteristic of

the continuous loss function illustrates the point that merely

making a product within the specification limits does not

necessarily mean that product is of good quality.

The loss-function can also be applied to product characteristics

other than the situation where the nominal value is the best value (m). The loss-

function for a smaller is better type of product characteristic (LB) is shown in

figure 3.2. The loss function is identical to the “nominal is the best” type of

situation when m=0, which is the best value for “smaller the better”

characteristic (no negative value). The loss function for a “larger the better”

type of product characteristic (HB) is also shown in figure 3.2, where m = 0.

3.2.3 Signal to Noise Ratio

The loss-function discussed above is an effective figure of merit

for making engineering design decisions. However, to establish an appropriate

loss function with its k value to use as a figure of merit is not always cost

effective and easy. In order to address this issue, Taguchi created a transform

function for the loss-function which is named as signal-to-noise (S/N) ratio

(Barker T.B 2005).

46

The S/N ratio, as stated earlier, is a concurrent statistic.

A concurrent statistic is able to look at two characteristics of a distribution

and combine these characteristics into a single figure of merit. The S/N ratio

combines both the parameters (the mean level of the quality characteristic

and variance around this mean) into a single metric (Barker T.B 2005).

A high value of S/N ratio implies that signal is much higher than the

random effects of noise factors. Process operation consistent with highest

S/N ratio always yields optimum quality with minimum variation (Barker T.B

2005). The S/N ratio consolidates several repetitions (at least two data

points are required) into one value.

The mean squared deviation (MSD) is a statistical quantity that

reflects the deviation from the target value. The quality characteristics are

different for MSD expressions. The standard definition of MSD is used for the

“nominal is best” characteristic. The unstated target value is zero for “Lower the

better”. The inverse of each large value becomes a small value and the

unstated target value is zero for “Higher the better”. Hence, the smallest

magnitude of MSD is being sought for all the three expressions.

The equation for calculating S/N ratios for “Lower the better” (LB),

“Higher the better” (HB) and “Nominal is best” (NB) types of characteristics

are as follows (Phillip J. Ross 1988):

47

Characteristic : HB

Loss

(Mon

etar

yU

nit)

L=k(1/y2)

Y

Characteristic : LB

Loss

(Mon

etar

yU

nit)

L=ky2

Y

Figure 3.2: The Taguchi Loss-Function for HB and LB Characteristics

a. Higher the Better:

(S/N)HB = -10log(MSDHB) (3.2)

Where

48

b. Lower the Better:

(S/N)LB= – 10log(MSDLB) (3.3)

Where

c. Nominal the Best

(S/N)NB= – 10log(MSDNB) (3.4)

Where

R = Number of repetitions

Relation between S/N Ratio and Loss Function

Single sided quadratic loss function with minimum loss at the zero

value of the desired characteristic is shown in figure 3.2. As the value of

y increases, the loss grows. Since, loss is to be minimized the target in this

situation for y is zero. The basic loss function (Eq. 3.1) is:

L(y) = k (y–m)2

If m = 0

L(y) = k (y2)

The loss may be generalized by using k=1 and the expected value of

loss may be found by summing all the losses for a population and dividing

by the number of samples R taken from this population. This in turn gives the

following expression (Barker T.B 2005).

EL = Expected loss = ( y2/R) (3.5)

The above expression is a figure of demerit. The negative of

this demerit expression produces a positive quality function. Taguchi adds the

49

final touch to this transformed loss-function by taking the log (base 10) of

the negative expected loss and then he multiplies by 10 to put the metric into

the decibel terminology (Barker T.B 2005). The final expression for “Lower

the better” S/N ratio takes the form of Equation 3.3. The same thought pattern

follows in creation of other S/N ratios.

3.2.4 Selection of orthogonal array (OA)

In selecting an appropriate OA, the pre-requisites are

(Roy .R.K 1990):

Selection of process parameters and/or interactions to be evaluated

Selection of number of levels for the selected parameters

Several methods are suggested by Taguchi to determine the

required parameters for inclusion in an experiment (Phillip J. Ross 1988). They

are:

a) Brainstorming

b) Flow charting

c) Cause-Effect diagrams

The total Degrees of Freedom (DOF) of an experiment is a direct

function of total number of trials. If the number of levels of a parameter

increases, the DOF of the parameter also increases since DOF calculated as

the number of levels minus one. Thus, increasing the number of levels for

a parameter increases the total degrees of freedom in the experiment which in

turn increases the total number of trials. Thus, two levels for each parameter are

recommended to minimize the number of experiment (Phillip J. Ross 1988). If

curved or higher order polynomial relationship between the parameters under

study and the response is expected, at least three levels for each parameter

50

should be considered (Barker T.B 2005). The DOF selected for the process

parameters are given in table 3.1.

Table 3.1: Degree of Freedom

Parameters EC V C DC F Error Total DOF 2 2 2 2 2 7 17

The standard two level and three level arrays (Taguchi 1979) are:

Two level arrays : L4, L8, L12, L16, L32

Three level arrays : L9, L18, L27

The number as subscript in the array designation indicates the

number of trials in that array. The total degrees of freedom (DOF) available

in an orthogonal array are equal to the number of trials minus one

(Phillip J. Ross 1988):

fLN = N – 1 (3.6)

Where, fLN

= Total degrees of freedom of an OA

LN = OA designation

N = Number of trials

When a particular OA is selected for an experiment, the inequality

(fLN> Total degrees of freedom required for parameters and interactions ) must

be satisfied.

In accordance to the total degree of freedom (17), the L18 orthogonal

array has been selected for this experiment. The L18 orthogonal array has

8 columns and 18 rows and it can handle one two-level parameter and seven

three-level process parameters at most. Since, our experiment needs only five

three-level process parameters L18 orthogonal array is most suitable. The array

51

selected has 5 columns and 18 rows and hence 18 experiments are needed to

study the effects of all the five process parameters.

Figure 3.3: Taguchi Experimental Design and Analysis Flow Diagram

Selection of Orthogonal Array (OA)

Decide : Number of parameters Number of Levels Interactions of interest Degrees of freedom (DOF) required

Selection of Orthogonal Array (OA)

Assign parameters and interactions to columns of OA using linear graph and / or Triangular tables

Noise ?

Consider noise factors and use appropriate outer array

Decide the number of repetitions (at least two repetitions)

Run the experiment in random order Record the responses Determine the S/N ratio

Conduct ANOVA on data

Identify control parameters which affect mean of quality characteristics

Classify the factors and select proper levels

Predict the mean at the selected levels Determine confidence intervals Determine optimal range Conduct confirmation experiments Draw conclusions

52

3.2.5 Assignment of parameters and interaction to the OA

The OA’s have several columns available for assignment of

parameters and some columns subsequently can estimate the effect of interactions

of these parameters. Taguchi has provided two tools to aid in the assignment of

parameters and interactions to arrays (Phillip J. Ross 1988):

1. Linear graphs

2. Triangular tables

Each OA has a particular set of linear graphs and a triangular table

associated with it. The linear graphs indicate various columns to which

parameters may be assigned and the columns subsequently evaluate the

interaction of these parameters. The triangular tables contain all the possible

interactions between parameters (columns). Using the linear graphs and / or

the triangular table of the selected OA, the parameters and interactions are

assigned to the columns of the OA.

3.2.6 Experimentation and data collection

The experiment is performed against each trial condition. Each

experiment at a trial condition is repeated. Randomization has been carried to

reduce bias in the experiment. The data are recorded against each trial

condition and S/N ratios of the repeated data points are calculated and

recorded against each trial condition.

53

3.2.7 Data analysis

A number of methods have been suggested by Taguchi for

analyzing the data: observation method, ranking method, column effect

method, ANOVA, S/N ANOVA, plot of average response curves,

interaction graphs etc. (Phillip J. Ross 1988). However, in the present

investigation the following methods have been used:

Plot of mean response curves

ANOVA for data

S/N response graphs

The plot of average responses at each level of a parameter indicates

the trend. It is a pictorial representation of the effect of parameter on the

response. The change in the response characteristic with the change in levels of

a parameter can easily be visualized from these curves. Typically, ANOVA for

OA’s are conducted in the same manner as other structured experiments

(Phillip J. Ross 1988). The S/N ratio is treated as a response of the experiment,

which is a measure of the variation within a trial when noise factors are present.

A standard ANOVA can be conducted on S/N ratio which will identify the

significant parameters (mean and variation).

3.2.8 Parameter classification and selection of optimal levels

When the ANOVA on the data (identifies control parameters which

affect average) and S/N data (identifies control parameters which affect

variation) are completed, the control parameters may be put into four classes

(Phillip J. Ross 1988):

54

Class I : Parameters which affect both average and variation (Significant in ANOVA)

Class II : Parameters which affect variation only (Significant in S/N ANOVA only)

Class III : Parameters which affect average only (Significant in data ANOVA only)

Class IV : Parameters which affect nothing. (Not significant in both ANOVAs)

The parameters design strategy is to select the proper levels of class I

and class II parameters to reduce variation and class III parameters to adjust the

average to the target value. Class IV parameters may be set at the most

economical levels since nothing is affected.

3.2.9 Prediction of the mean

After determination of the optimum condition, the mean of the

response (µ) at the optimum condition is predicted. The mean is

estimated only from the significant parameters. The ANOVA identifies

the significant parameters. Suppose, parameters A and B are significant and

A2B2 (second level of A=A2, second level of B=B2) is the optimal

treatment condition. Then, the mean at the optimal condition (optimal value

of the response characteristic) is estimated as (Phillip J. Ross 1988):

Where, T = Overall mean of the response

A2, B2 = Average values of response at the second levels of parameters A and B respectively

It may also happen that the prescribed combination of

55

parameter levels (optimal treatment condition) is identical to one of those

in the experiment. If this situation exists, then the most direct way to

estimate the mean for that treatment condition is to average out all the

results for the trials which are set at those particular levels

(Phillip J. Ross 1988).

3.2.10 Determination of confidence interval

The estimate of the mean (µ) is only a point estimate based on the

average of results obtained from the experiment. Statistically this provides

a 50% chance of the true average being greater than µ. It is therefore

customary to represent the values of a statistical parameter as a range

within which it is likely to fall, for a given level of confidence

(Phillip J. Ross 1988). This range is termed as the confidence interval (CI). In

other words, the confidence interval is a maximum and minimum value between

which the true average should fall at some stated percentage of confidence.

The following two types of confidence interval are suggested

by Taguchi in regards to the estimated mean of the optimal treatment

condition.

1. Around the estimated average of a treatment condition

predicted from the experiment. This type of confidence

interval is designated as CIPOP (confidence interval for the

population).

2. Around the estimated average of a treatment condition used in

a confirmation experiment to verify predictions. This type of

confidence interval is designated as CICE (confidence interval for

a sample group).

56

The difference between CIPOP and CICE is that CIPOP is for the

entire population i.e., all parts made under the specified conditions, and

CICE is for only a sample group made under the specified conditions. Because

of the smaller size (in confirmation experiments) relative to entire population,

CICE must slightly be wider. The expressions for computing the confidence

intervals are given below (Roy R.K 1990)

3.3 Machining Performance Evaluation

The machining performance is evaluated by material removal rate

(MRR) and Dimensional Deviation. MRR is defined as amount of material

removed per unit machining time. Dimensional deviation of the machined

micro hole has been considered as machining accuracy criteria. It is the

difference between the radius of the machined hole and the radius of the tool

electrode. The diameters of holes drilled were measured with the help of an

optical microscope. Machining time is noted for each experiment. The lower

the dimensional deviation is better the machining performance. The higher the

MRR, is better the machining performance. Therefore, the dimensional

deviation is the “lower the better” and the MRR is the “Higher the better”

performance characteristic respectively.

3.3.1. Material Removal Rate (MRR)

Material removal rate is expressed as the amount of material

removed under a period of machining time (T) in minutes and calculated using

the following equation.

MRR (mm3/min) = Area of the hole (mm2) × depth of the hole (mm) Machining Time (min) (3.7)

57

3.3.2 Signal-to-Noise Ratio (S/N Ratio)

In Taguchi design methodology, basically the experimental results

are converted into a single quality characteristics evaluation index i.e. S/N

ratio. The least variation and the optimal design are obtained by means of the

S/N ratio. The benefits of S/N ratio includes increasing the factor weighting

effect, decreasing mutual action, simultaneously processing the average and

variation, and improving engineering quality. The higher the S/N ratio, the

more stable the achievable quality. Depending on the required objective

characteristics, different calculation methods can be applied as follows:

The smaller the better (SB) where the objective optimal value is the

smaller the better, dimensional deviation.

(3.8)

The larger the better (LB) where the objective optimal value is

larger the better, such as material removal rate.

(3.9)

where = S/N ratio and y = result of experiment (MRR).

3.3.3. Analysis of variance (ANOVA)

The S/N ratio determined from the experimental values were

statistically studied by ANOVA to explore the effects of each machining

parameter on the observed values and to elucidate which machining parameter

significantly affected the MRR. Different software are available to perform

ANOVA such as “DESIGN EXPERT”, “Minitab 15” etc. In this work

58

“Minitab 15” has been used for the analysis purpose. The related equations are

as follows:

Sm = ( i )2 / 18 (3.10)

ST = i2– Sm (3.11)

SA = ( Ai)2 / N – Sm (3.12)

SE = ST – SA (3.13)

VA = SA / fA (3.14)

FAO = VA / VE (3.15)

Where,

Sm = sum of squares based on the mean

ST = sum of squares based on the total variation

SA = sum of squares based on the parameter A (like electrolyte concentration, voltage, current, duty cycle or frequency)

SE = sum of squares based on the error

i = value of in the ith experiment (i = 1 to 18)

Ai = sum of the ith level parameter A (i= 1, 2 or i= 1–3)

N = number of repetition at each level parameter A,

fA = number of degrees of freedom of parameter A

VA = variance of parameter A

FAO = F– test parameter for A

F-test can be used to determine which process parameters have

significant effect on the performance characteristics. P-test is designed to

know whether factor is significant or not depending on its value. If P value is

less than alpha value which is generally taken as 0.05, then factor have

significant effect on performance characteristics.

59

The experimentations are conducted after setting the desired values

of process parameters like voltage, current, duty cycle, and frequency with the

microcontroller based pulsed power supply system. Hence, the calculation of

duty cycle has to be done in advance. In a pulsed power supply, current and

voltage switches between 0 and peak values in a set frequency.

Duty cycle is the ratio between the pulse ON time in relation to the

total experiment time i.e. sum of ON and OFF time. The ON time and OFF

time are calculated using following equations.

Duty Cycle = Ton(ms) / Ttotal(ms) (3.16)

Ttotal = Ton + Toff (3.17)

Ton = Duty cycle * Ttotal (3.18)

Frequency = 1 / Ttotal (3.19)

Table 3.2 and table 3.3 shows ON time and OFF time values

required to set for a particular frequency and duty cycles.

Table 3.2: Calculated ON time and OFF time - NICKEL

Frequency(Hz)

TotalTime (ms)

Duty cycle 33.3% 50.00% 66.66%

On time(ms)

OffTime (ms)

On time(ms)

OffTime (ms)

On time(ms)

OffTime (ms)

30 33.33 11.90 22.23 16.66 16.66 22.23 11.90 40 25 8.33 16.66 12.5 12.5 16.66 8.33 50 20 6.66 13.33 10.0 10.0 13.33 6.66 60 16.66 5.55 11.10 8.33 8.33 11.10 5.55

60

Table 3.3: Calculated ON time and OFF time - SDSS and Inconel 600

Frequency(Hz)

TotalTime (ms)

Duty cycle 33.3% 50.00% 66.66%

On time(ms)

OffTime (ms)

On time(ms)

OffTime (ms)

On time(ms)

OffTime (ms)

30 33.33 11.11 22.22 16.67 16.66 22.22 11.11 40 25 8.33 16.67 12.5 12.5 16.67 8.33 50 20 6.67 13.33 10 10 13.33 6.67

The amplitude of the pulse (ON TIME) is called the peak current.

The level of energy which is equal to D.C. level when time (duty cycle) is

considered, is called as average current or machining current. The relationship

between average and peak current is given by:

Average current = Peak current × Duty cycle (3.20)

Peak current values are to be set accordingly for getting required

machining current i.e. average current. The calculated average current for

Nickel, SDSS, and Inconel 600 are tabulated in table 3.4 and table 3.5

respectively.

Table 3.4: Average Current - NICKEL

Average current (amp)

Peak current (amp) DC 33.33% DC 50.00% DC 66.66%

0.1 0.30 0.20 0.150.3 0.90 0.60 0.450.5 1.50 1.00 0.75

61

Table 3.5: Average Current - SDSS and Inconel 600

Average current (amp)

Peak current (amp) DC 33.33% DC 50.00% DC 66.66%

0.6 1.8 1.2 0.90.8 2.4 1.6 1.21.0 3.0 2.0 1.5

Similarly calculation is made for average voltage using equation

Average voltage = Peak voltage × Duty cycle. (3.21)

The required machining voltage (average voltage) can be obtained

by setting appropriate Peak voltage. The calculated machining voltage for

Nickel, SDSS, and Inconel 600 are given in table 3.6 and table 3.7

respectively.

Table 3.6: Average Voltage - NICKEL

Average voltage (volts)

Peak voltage (volts) DC 33.33% DC 50.00% DC 66.66%

3.5 10.50 15.00 19.505.0 7.00 10.00 13.006.5 5.25 7.50 9.75

Table 3.7: Average voltage - SDSS and Inconel 600 Average voltage

(volts) Peak voltage (volts)

DC 33.33% DC 50.00% DC 66.66% 8 24 16 129 27 18 13.5

10 30 20 15

62

During machining, a bubble coming from the bottom side of the

work piece indicates that the hole reached the bottom. It should be observed

carefully to get accurate results. Machining to be continued till a circular hole

at the exit side is machined. For each experiment machining time is noted.

With the help of optical microscope, the diameter of the holes drilled is

recorded.

Material Removal Rate (MRR) is calculated by using machining

time, area of hole and sheet thickness for each experimental combination.

Using calculated MRR values S/N ratio for each experiment were calculated.

ANOVA is performed to determine, factor affects the MRR significantly.

Finally the experimental values are validated with Genetic Algorithms.

3.3.4 Confirmation Test

The optimum level of process parameters has been determined by

using S/N ratio values. Once the optimal level of the process parameters has

been selected, the final step is to predict and verify the improvement of the

performance characteristic using the optimal level of the process parameters.

The purpose of conformation test is to validate the conclusions drawn during

analysis phase.

The predicted or estimated S/N ratio using optimal levels of

process parameters can be calculated as;

q = m + i – m) (3.22)

i=1

where,

m = total mean of S/N ratio

63

q = number of significant parameters

i = mean of S/N ratio at optimum level

After predicting the response (S/N ratio), a confirmation

experiment is designed and conducted with the optimal levels of the

machining parameters to verify the improvement of performance

characteristic.

3.4. GENETIC ALGORITHMS (GA)

3.4.1 Introduction

Genetic algorithms belong to the larger class of evolutionary

algorithms (EA), which generate solutions to optimization problems using

techniques inspired by natural evolution, such as inheritance, mutation,

selection, and crossover. Decision making situation occurs in all fields like

science, technology and management, etc. where GA is applied with an

objective to maximize or minimize a task. In order to solve the problems

related to inventory, transportation, queuing, scheduling etc., many

optimization procedures have been developed over the past six decades.

Most of the traditional optimization procedures end its search in the

“local optima” rather than finding the “global optima”. To overcome this,

many number of non traditional search and optimization algorithms were

developed over the past four decades. They are,

1. Genetic Algorithm (GA)

2. Simulated Annealing (SA)

3. Tabu Search (TS)

4. Ant Colony Optimization (ACO)

64

5. Particle Swarm Optimization (PSO)

6. Scatter Search (SS), etc.

Genetic algorithms (GA) is computerized search and optimization

algorithm based on the mechanics of natural genetics and natural selection

(Goldberg D. 2000). It was inspired by Darwin’s theory about evolution.

Prof. Holland of University of Michigan envisaged the concept of GA.

Number of students and researchers have contributed for the development of

this field.

The optimization model for the ECMM process is multi-variable

non-linear objective function with non-linear constraints and is highly

complicated to solve using the traditional optimization methodologies. The

Genetic Algorithm (GA) in particular have proven to be a powerful tool to

solve such complex optimization problems without any approximation.

Genetic Algorithms are computerized search and optimization algorithms

belonging to the class of evolutionary algorithms (EA) and works with a set of

solutions.

The operation of GA begins with generation of a set of random

solution. The fitness value of each solution has to be evaluated. The higher the

fitness value, the better the solution. The generated population is then operated

by the reproduction, crossover and mutation operators to create the new

population which is evaluated and tested for the termination criterion. One

cycle of population evaluation and subsequent three GA operations constitute

a generation in the GA terminology. The GA operations are continued until

the termination criterion is met for a specified number of generations (Deb

Kalyanmoy 1995).

65

Figure 3.4: Structure of Genetic Algorithm

Start

Generate initial population

Evaluate objective function

Are optimization criteria met?

Selection

Recombination

Mutation

Best Individuals

Result

66

A simple genetic algorithm start with a set of randomly generated

initial population. The basic steps involved in the genetic algorithm are given

below.

1. [Start] Generate random population of n chromosomes (suitable solutions for the problem).

2. [Fitness] Evaluate the fitness F(X) of each chromosome X in the population.

3. [New Population] Create a new population by repeating following steps until new population is complete.

4. [Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected).

5. [Crossover] With a crossover probability cross over the parents to form new offspring (children). If no crossover was performed, offspring is the exact copy of parents.

6. [Mutation] With a mutation probability mutate new offspring at each locus (position in chromosome).

7. [Accepting] Place new offspring in the new population.

8. [Replace] Use new generated population for a further run of the algorithm.

9. [Test] If the end condition is satisfied, stop, and return the best solution in current population.

10. [Loop] Go to step 2.

3.4.2 Implementation of GA

The principle of natural genetics is that ‘Fit parents would yield fit

offspring’. GA has wide variety of applications in engineering problems

because of simplicity and ease of operation. The minimum or maximum of

a function is found based on the variation of X1, X2, X3 . . . Xn beginning with

one or more starting point. GA evaluates a set of points, and the basic element

of GA consists of a chromosome and fitness value. The fitness value describes

67

how well an individual can adapt to survival and mating. In this study, the

basic elements of GA consists of a value of electrolyte concentration,

machining current, machining voltage, duty cycle and frequency.

GA works on the basis of binary code in the form of 0 and 1. An

individual in GA is denoted by I = {EC, C, V, DC, F, f (EC, C, V, DC, F)}.

A set of search individual is called a population and general structure of GA

and convergence GA result depicting. The parameters used in GA are;

population size = 100, length of chromosome = 20, selection operator

= stochastic uniform, crossover probability = 0.8, mutation probability = 0.2,

fitness parameter = MRR. The objective function is given by

MRR = f (EC,C,V,DC,F).

Genetic algorithms can be used to solve the constrained

optimization problems as well as unconstrained optimization problems. GA

can be used to solve maximization problems as well as minimization

problems. In the chapter, a constrained optimization problem is considered to

explain the implementation of genetic algorithm. Let us consider the following

maximization problem.

Subject to the constraints maximize f(x).

XiL Xi Xi

U for i =1, 2, 3……….N (3.23)

The operation of GA begins with a population of encoded solution.

Each string is evaluated to find the fitness value. Then the population is

operated by the three important genetic operators to create a new population.

The performance of GA is mainly influenced by these three operators.

68

1. Selection of Reproduction

2. Crossover

3. Mutation

Fitness Function

GA mimics the survival of the fittest principle of nature to make a

search process. Therefore, GA is naturally suitable for solving maximization

problems. Minimization problems are transformed in to maximization

problems by some suitable transformation. Fitness function F(x) is derived

from the objective function and used in successive genetic operations. Certain

genetic operators require that fitness function be non negative, although

certain operators do not have this requirement. Following are the fitness

function for different objective functions.

F(X) = f(X) for maximization problems (3.24)

F(X) = 1 for minimization problems, if f(X) 0 (3.25) f(X)

F(X) = 1 for minimization problems, if f(X) = 0 (3.26) (1+f(X))

Selection or Reproduction

Selection or reproduction is usually the first operator applied on

population. Reproduction operator selects the best chromosomes from the

population to form a matting pool for next operation. Many number of

selection operators were used in the genetic algorithm literature. The essential

ideas in all of them is, the above average strings are picked from the current

population and their multiple copies are inserted in the matting pool in

69

a probabilistic manner. In genetic algorithm, the probability of selection Ps of

each string depends on the fitness of individual string. The probability of

selection is calculated using the following equation:

Probability of selection of ith string ps = Fi (3.27)nj=1 Fj

Where Fi - Fitness value of ith string, n - Population size

The string has more probability of selection will get more chance

for selection.

Crossover or Recombination

Crossover operator produces new offspring in combining the

information contained in two parents. The crossover operation is performed

with a probability of crossover Pc, crossover occurs only if the random

number generated is less than the crossover probability Pc (like flipping of

a coin with a probability) otherwise the two strings repeated without any

change. Depending on the representation of the variables, the offspring will be

subjected to crossover.

Mutation

After crossover operation is performed, the string is subjected to

mutation operation. This is to prevent falling all solutions of the population

into a local optimum of solved problem. Mutation operator alters

a chromosome locally to hopefully create a better string. The bit wise mutation

is performed with a probability of mutation of mutation Pm. Mutation occurs

only if the random number generated is less than the mutation probability Pm

(like flipping of a coin with a probability) otherwise the bit kept unchanged.

70

A simple genetic algorithm treats the mutation only as a secondary operator

with roll of restoring lost genetic materials. The mutation is also used to

maintain diversity in the population. For example, consider the following

strings.

1 1 1 0 1 0 0 1 1 1 0 0 1 0 0 0

Notice that all four strings have a ‘1’ in the leftmost bit position. If

the true optimum solution requires as a ‘0’ in the position, the selection or

cross operators will not change the value of the bit. The mutation operator will

change its value. Following are the mutation methods available for different

coded string.

1. Binary Valued Mutation

2. Real Valued Mutation

3.4.3 Experimental Validation (GA)

The optimized parameters obtained for the maximum MRR shows

that as the generation progresses the solutions are approaching optimum.

A validation of experiment is conducted using the optimum process

parameters. It is observed that MRR obtained from validation experiments is

closer to the optimized MRR obtained using GAs. It infers the practical

applicability of the combined use of Taguchi methodology, ANOVA and GAs

for optimizing the ECMM process parameters to obtain maximum MRR.

71

CHAPTER 4

EXPERIMENTAL SETUP

4.1 INTRODUCTION

The ECMM system developed to conduct necessary experiments

for this research work has the following five major assemblies. The schematic

diagram of the ECMM setup is shown in figure 4.1.

Work holding platform.

Tool feeding device.

Control system.

Electrolyte flow system.

Power supply system.

Figure 4.1: Schematic Diagram of Experimental Setup

Pulse Generator

Drive Unit

Control System

Servo Motor

Filter Unit

Electrolyte Tank

Machining Chamber

72

4.2 MACHINING SETUP STRUCTURE

Mild steel is selected for the structure of the machine body. The

parts made of mild steel are chromium plated for aesthetic looks and corrosion

resistance. For the parts that come into contact with electrical system which

requires insulation, fiber material is used. Parts that come into contact with

electrolyte require noncorrosive materials and hence acrylic material is used in

those places. The dimensions have been arrived based on the specifications

found in published literatures. They were further modified considering the

compactness, functional movements of mating parts, working conditions,

arrangement constraints and space utilization. The machining setup structure

consists of machining base over which a rectangular column is mounted. The

column is mounted with three angle plates with allen screws.

The size of the angle plate fabricated is 120 × 100 × 8 mm. It is

suitable to accommodate the stepper motor and other associated discrete parts.

The other two angle plates support the lead screw with the help of bearings.

The lead screw is keyed with the stepper motor shaft and passes through the

internally threaded hole of the electrode feeding section made of mild steel.

The diameter and the length of the main screw rod are 12 mm and 183 mm

respectively. In order to achieve very fine feed of the electrode, thread has

been made at 30 threads per inch for a length of 75 mm in the mid portion of

the main screw rod. This enables the linear up and downward feed of electrode

to a required level in accordance with the depth of the electrolyte chamber and

work piece placement in it.

When the stepper motor rotates, the lead screw rotates, which in

turn moves the micro tool electrode holding device which provides electrode

feed movements. Just below the tool electrode holding devices, the machining

73

chamber rests on a base plate. The base plate is provided with four bushes at

the bottom for easy handling. The dimensions of other parts are calculated

considering space arrangement and functional requirements. Inside the

machining chamber, a work holding device is mounted. The work piece which

is of few microns thick is held in the fixture, made up of two block of

insulating material fastened with screws. The one side of the fixture is

connected with a wire to the workpiece and made as anode.

4.2.1 Work Holding Platform

A rigid work holding platform made up of non corrosive material is

very essential for this ECMM setup. The rectangular platform consist of two

detachable parts which can be fastened together by means of screws. The work

piece is placed in between these two and it is tightly fastened. The work

holding platform is immersed in the electrolyte while the machining operation

is carried out. Since, we need a non corrosive platform, acrylic material is used

to fabricate the platform setup. The figure 4.2 shows the work holding

platform with its fasteners inside the electrolyte tank and the tool holding

arrangement.

The machining chamber / electrolyte tank is also made up of acrylic

material. The entire work holding platform is placed inside the chamber. The

chamber is filled with electrolyte, according to need. The electrolyte filtration

and re-circulation is carried out by using a pump and filter arrangement. The

level of electrolyte in the electrolyte chamber is maintained in such a way that

the machining zone (work piece and tool electrode) is immersed during the

machining process. Electrically non-conductive and chemically non-corrosive

materials are used at places where the electrolyte directly contacts the setup

74

such as electrolyte tank, connecting tube, electrolyte flushing nozzle, filter,

pump etc.

Figure 4.2: Work Holding Platform, Tool holding arrangement

4.2.2 Tool Feeding Device

The electrode feed system is made up of mild steel and insulating

material. The mild steel section slides along the vertical column through tie

rods and provide the required tool feed movement through the cylindrical

electrode holder. The cylindrical electrode holder is attached rigidly to the

insulated section. The tool holder is made up of copper rod, with necessary

arrangement to hold the micro machining tool at the bottom end. The

machining tool (cathode) is connected to the negative terminal of pulsed

75

power supply via the tool holder. Since, the rotation of stepper motor is

bidirectional; the tool can be moved forward and backward. The number of

steps and the direction of rotation of stepper motor are controlled with the help

of microprocessor based stepper motor driver unit. A stepper motor with

following specifications has been chosen for this experimental setup.

Resolution = 1.8 / step

Voltage = 12 V

Current = 0.6 A

Torque = 3 kg-cm

The pitch of the screw rod (30 Tpi) has been chosen in such a way

that one step rotation of the stepper motor moves the tool by 4 microns. The

Cyanoacrylate is used for providing insulation by coating it along the

circumference of the machining tool. The coating is made to avoid the stray

current effect and to ensure that the machining process takes place only at the

tip of the tool. After coating is completed, the tip of tool is ground to get round

shape by using double disk grinding machine.

4.2.3 Inter Electrode Gap Control System

Inter electrode gap control is a key factor influencing machining

accuracy during EMM. According to the characteristics of EMM, a closed

loop control is designed using microcontroller and current sensor to ensure

stable machining. The position of the micro tool electrode and the workpiece

are determined through contact sensing function and then, tool electrode is

withdrawn about 24 µm to form the minimum machining gap. The current

sensor used in the IEG system has the advantage of excellent accuracy, very

good linearity, optimized response time, no insertion losses and current

76

overload capability. The output of the current sensor is amplified and

converted into digital signal and fed to the micro controller. The machining

current is sampled during feeding of the tool electrode towards the workpiece.

In case of a short circuit, there will be a current jump-up and this is sensed by

the current sensor. Then, a decision is made to withdraw the machining tool

and to maintain the set IEG (Yong L 2003). The withdrawn tool once again

moves forward to maintain the set IEG after stopping several microns away

from the work piece to facilitate through flushing of IEG clear of all debris

according to the programmed time value.

Figure 4.3: Control System

4.2.4 Electrolyte Flow System

Smooth flow of clean electrolyte should be maintained for better

machining. Hence electrolyte cleaning is essential. Electrolyte NaNO3 is

pumped into the machining chamber with a velocity to drive out the material

77

removed during machining. The size of the machining chamber has been

chosen as 200 × 100 × 60 mm in order to accommodate various components

like work holding platform, tool electrode, nozzles with pipe to circulate the

electrolyte at the IEG etc. along with human working constraints. The

electrolyte is passed through a filter fitted with sedimentary filter cartridge to

remove the impurities. The tank, which houses the filter receives the

contaminated electrolyte from the machining chamber, filters and re-circulates

the cleaned electrolyte continuously into the machining chamber using

a centrifugal pump. The electrolyte pump with a capacity of 16 - 18 lit/min at

a head of 2.5 meter has been chosen for this setup. The rate of flow of the

electrolyte at the machining zone is controlled with a valve is fitted near the

nozzle end of the electrolyte delivery line. The proper cleaning of electrolyte

and adequate rate of flow is very important since, the machined material, if not

removed from the machining zone, would create a short circuit between the

electrodes. The electrolyte filter is shown in the figure.4.4.

Figure 4.4: Electrolyte Filter

78

The flushing of electrolyte is not only useful in removing the

machined particles but also to clear the hydrogen gas bubbles generated at the

machining zone during the machining process. The removal of hydrogen gas

bubbles is also equally important because the gas bubbles between the tool and

the work piece acts as a short circuiting medium and creates micro sparks that

can erode the tool material.

Hence, to avoid the micro spark generation, the electrolyte is

pumped in at a moderate pressure to take away the hydrogen gas generated

(Bhattacharyya B 2003). The various possible electrochemical reactions that

can take place during an electrochemical reaction are shown in the figure 4.5.

Figure 4.5: Electro Chemical Reactions

4.2.5 Microcontroller Unit

The Micro movement of machining tool is achieved through

a precision main screw rod’s rotation. The main screw rod is rotated by the

directly coupled stepper motor. The stepper motor is precisely controlled by

the microcontroller unit. A stepper motor with 1.8 /step, 12 V / ph, 0.6A / ph,

Torque : 3 kg-cm has been selected for this experimental setup. Different

79

programmes are stored in the microcontroller unit for 1) forward motion,

2) reverse motion and 3) slow forward. The micro controller is programmed

with provision to control the IEG between 20 to 50 m. The programs and

necessary commands can be entered through the keyboard connected to the

microcontroller unit. The unit is provided with the Reset Button for stopping

the stepper immediately in case of an emergency.

4.2.6 Power Supply System

The electrochemical micromachining requires variable pulsed DC

power supply. In order to have accurate control over the DC voltage, current,

frequency and duty cycle, a microcontroller based digital pulsed DC power

supply system has been chosen for this research work. The chosen power

supply system made by M/s. Dynatronics, USA gives a wide range of controls

over the various aspects of the power supply system with real time digital

readouts and simple to use controls. The pulse rectifier Dynatronics - DP-40-

15-30 is shown in figure 4.6 followed by the specifications of the instrument.

Figure 4.6: Pulse Rectifier

80

The applications of the direct current through a solution of

electrolyte results in redox reaction where as the application of Alternating

Current (AC) leads to conduction only. This is due to very fast change

polarities in electrodes and the electrode reaction occurring in the first half

cycle is reversed in the other half cycle of the AC current. Hence only DC is

used in this application.

Specifications of Pulse Rectifier DP-40-15-30

o Microprocessor-based controls for accuracy and repeatability

o Soft-touch keypad and digital displays

o Minimum pulse width: 0.1 millisecond on/off

o Selectable pulse timing resolutions

o Typical pulse rise and fall times: <50 microseconds

o Minimum suggested setting: 10% of maximum current rating

o Regulation accuracy: +/– 1% of setting or 0.1% of peak

rating

o Ripple: <1% RMS of maximum rated output voltage

o Selectable current, voltage or cross-over regulation modes

o Output voltage and current tolerance limit settings with alarms

o Ampere time and real time cycle control via front panel

o Resettable ampere time totalizer with password protection

o Selectable ampere time and real time counter resolutions

o Audible alarm for end-of-cycle and out-of-tolerance condition

o Save/Recall/Delete settings feature

o Built in fault detection for over-temperature and power failure

o RS485 Serial control standard & Windows-based program

o Powder coated aluminum enclosure 8.75" × 17" × 23"

o Forced air cooling through sealed heat sink tunnel

o Environmentally sealed to protect power supply from harsh environments

81

The complete experimental setup is shown in figure 4.7.

Figure 4.7: Complete Experimental Setup

4.3 MATERIALS FOR RESEARCH

The work materials chosen for this research work are Nickel, Super

Duplex Stainless Steel (SDSS), and Inconel 600. The chosen materials are

excellent materials for shielding against magnetic interference. They offer

high corrosion resistance, fine thermal, and electrical properties along with

excellent mechanical properties. These difficult to cut alloys are largely being

used in biomedical, Communication, Aerospace, Thermal, and Nuclear power

plants.

82

4.3.1 Nickel

Nickel is the 5th most common element on earth, exists mainly in

the form of sulphide, oxide, and silicate minerals. Nickel is an extremely

important commercial element, playing a key role in global industrial

development and outpacing almost all other industrial metals. Nickel is

a silvery-white lustrous metal with a slight golden tinge. It is one of only four

elements that are magnetic at or near room temperature, the others being iron,

cobalt and gadolinium.

The factors which make nickel and its alloys valuable commodities

include strength, corrosion resistance, high ductility, good thermal and electric

conductivity, magnetic characteristics and catalytic properties. Nickel, above

355 °C becomes non-magnetic material (Curie temperature). The unit cell of

nickel is a face centered cube with the lattice parameter of 0.352 nm giving an

atomic radius of 0.124 nm. Nickel belongs to the transition metals and is hard

and ductile.

The Nickel's slow rate of oxidation at room temperature qualifies it

as corrosion-resistant. The metal is chiefly valuable in the modern world for

the alloys it forms; about 60% of world production is used in nickel-steels

(particularly stainless steel); 14% in nickel-copper and nickel silver alloys; 9%

to make malleable nickel, nickel clad, Inconel, and other superalloys; 6% in

plating; 3% for nickel cast irons; 3% in heat and electric resistance alloys,

such as Nichrome; 2% for Nickel brasses and bronzes; 3% in all other

applications combined. Tables 4.1, 4.2, 4.3, and 4.4 presents the general,

physical, atomic and miscellaneous properties of Nickel respectively.

83

Table 4.1: General Properties of Nickel

Name, symbol, number Nickel, Ni, 28 Element category transition metal

Group, period, block 10, 4, d Standard atomic weight 58.6934(4)(2) Electron configuration [Ar] 4s2 3d8 or [Ar] 4s1 3d9

Electrons per shell 2, 8, 16, 2 or 2, 8, 17, 1

Table 4.2: Physical Properties of Nickel

Phase SolidDensity (near r.t.) 8.908 g·cm 3

Liquid density at m.p. 7.81 g·cm 3

Melting point 1728 K, 1455 °C, 2651 °F Boiling point 3186 K, 2913 °C, 5275 °F Heat of fusion 17.48 kJ·mol 1

Heat of vaporization 377.5 kJ·mol 1

Molar heat capacity 26.07 J·mol 1·K 1

Table 4.3: Atomic Properties of Nickel

Oxidation states 4[1], 3, 2, 1 [2], –1 (mildly basic oxide)

Electro negativity 1.91 (Pauling scale)

Ionization energies (more)

1st: 737.1 kJ·mol 1

2nd: 1753.0 kJ·mol 1

3rd: 3395 kJ·mol 1

Atomic radius 124 pm Covalent radius 124±4 pm

Van der Waals radius 163 pm

84

Table 4.4: Miscellaneous Properties of Nickel

Crystal structure face-centered cubic

Magnetic ordering ferromagnetic

Electrical resistivity (20 °C) 69.3 n ·m

Thermal conductivity 90.9 W·m 1·K 1

Thermal expansion (25 °C) 13.4 µm·m 1·K 1

Speed of sound (thin rod) (r.t.) 4900 m·s 1

Young's modulus 200 GPa

Shear modulus 76 GPa

Bulk modulus 180 GPa

Poisson ratio 0.31

Mohs hardness 4.0

Vickers hardness 638 MPa

Brinell hardness 700 MPa

Nickel is used in many specific and recognizable industrial and

consumer products, including stainless steel, alnico magnets, coinage,

rechargeable batteries, electric guitar strings, microphone capsules, and special

alloys. It is also used for plating and to produce green tint in glass. Nickel is

preeminently an alloy metal, and its chief use is in the nickel steels and nickel

cast irons, of which there are many varieties. It is also widely used in many

other alloys, such as nickel brasses and bronzes, and alloys with copper,

chromium, aluminium, lead, cobalt, silver, and gold (Inconel, Incoloy, Monel,

Nimonic).

Nickel can be used to shield communication devices, transducers

and gyroscopes due to its high magnetic permeability property. This makes it

an excellent material for shielding against magnetic disturbance. Because

85

Nickel offers good mechanical properties, high corrosion resistance, fine

thermal, and electric properties, it can be used to fabricate micro thermocouple

for transient temperature measurement. It can be used as a mould material for

plastic injection moulding to produce micro planetary gears. In production of

micro and meso lenses, Nickel stamper is used.

Nickel is an excellent alloying agent for certain other precious

metals, and so used in the so-called fire assay, as a collector of platinum group

elements (PGE). Nickel and its alloys are frequently used as catalysts for

hydrogenation reactions. It is also used as a binder in the cemented tungsten

carbide or hard metal industry.

Ferronickel is an alloy containing Nickel and Iron, approximately

35% Nickel and 65% Iron. Ferronickel is primarily used in the manufacture of

Austenitic stainless steels (known as 200 and 300 series). These are

nonmagnetic and contains between 8.5% to 25% Nickel, enhancing their

corrosion resistance. They are the most widely used group of stainless steels,

accounting for 70% - 75% of global stainless output.

4.3.2 SDSS

The first widely available super duplex stainless steel was

developed by Gradwell and co workers in the mid 1980’s. This alloy was

called Zeron 100® and was developed as a casting alloy for pump applications

in the oil and gas industry. The performance of the steel in this application

generated a demand for the alloy in wrought product forms also.

86

As demand for the steel grew, clients called for ASTM, NACE,

British Standards, and other codes to include and cover the Zeron range of

products. In 1993-94 ASTM considered the properties of several heats of

ZERON 100 in a range of product forms and on the basis of this designated

the code UNS S32760 to the alloy and introduced this number into several

standards. The term "Super-Duplex" was first used in the 1980's to denote

highly alloyed, high-performance Duplex steel with a pitting resistance

equivalent of >40 (based on Cr% + 3.3Mo% + 16N%).

Super duplex stainless steels (SDSS) may be defined as a group of

steels having a two phase ferrite-austenite microstructure after heat treatment and

water quenching, with a pitting resistance equivalent number (PREN) higher than

40. The PREN value is linked to the content of the three most important elements

in the alloy, namely, Cr, Mo, and N, with each of them weighted according to its

influence on pitting. The approximately equal volume fractions of ferrite and

austenite are achieved by the simultaneous control of the chemical composition

and the annealing temperature.

Due to their excellent corrosion resistance in chloride environments,

these alloys are widely used as structural materials for chemical plants,

phosphoric acid production plants, hydrometallurgy industries, and as materials

for offshore applications. These alloys also possess superior weldability and

better mechanical properties than austenitic stainless steels.

The combination of high strength and corrosion resistance makes

super duplex stainless steel attractive for a number of applications both in sour

process fluids and seawater. Several super duplex alloys exist and each has its

own proprietary chemical composition.

87

Super-Duplex Stainless Steel provides outstanding resistance to

acids, acid chlorides, caustic solutions, and other such environments. This

alloy with its high level of chromium, often replaces 300 series stainless steel,

high nickel super austenitic steels in the chemical / petrochemical, pulp, and

paper industries. SDSS also provides excellent resistance to inorganic acids,

especially those containing chlorides.

The chemical composition based on high contents of chromium,

nickel, and molybdenum improves inter granular and pitting corrosion

resistance. Additions of nitrogen promote structural hardening by interstitial

solid solution mechanism. Due to this, the yield strength and ultimate strength

values are raised without impairing toughness. Further, the two-phase

microstructure guarantees higher resistance to pitting and stress corrosion

cracking.

BENEFITS

High strength.

High resistance to pitting, crevice corrosion resistance.

High resistance to stress corrosion cracking, corrosion fatigue,

and erosion.

Excellent resistance to chloride stress-corrosion cracking.

High thermal conductivity.

Low coefficient of thermal expansion.

Good sulfide stress corrosion resistance.

Low thermal expansion and higher heat conductivity than

austenitic steels.

Good workability and weldability.

High energy absorption.

88

APPLICATIONS

Heat exchangers, tubes, and pipes for production and handling

of gas and oil.

Heat exchangers and pipes in desalination plants.

Mechanical and structural components.

Power industry FGD systems.

Pipes in process industries handling solutions containing

chlorides.

Utility and industrial systems, rotors, fans, shafts and press rolls

where the high corrosion fatigue strength can be utilized.

Cargo tanks, vessels, piping, and welding consumables for

chemical tankers.

High-strength, highly resistant wiring.

Duplex stainless steels are graded for their corrosion performance

depending on their alloy content. Duplex stainless steel can be divided into

four groups:

Lean Duplex such as 2304, which contains no deliberate Mo

addition.

2205, the work-horse grade accounting for more than 80% of

duplex usage.

25 Cr duplex such as Alloy 255 and DP-3.

Super-Duplex; with 25-26 Cr and increased Mo and N

compared with 25 Cr grades, including grades such as 2507,

Zeron 100, UR 52N+, and DP-3W.

In this study, the SDSS 2205 (UNS S31803) is used for the

experiments. Alloy 2205 is a 22% Chromium, 3% Molybdenum, 5 - 6%

89

Nickel, nitrogen alloyed duplex stainless steel with high general, localized and

stress corrosion resistance properties in addition to high strength and excellent

impact toughness. The table 4.5 shows the chemical compositions of various

types of SDSS.

Table 4.5: Specifications of Super Duplex Stainless Steel

Specification Composition

UNS S32760 Super Duplex Stainless Steel. 25% chromium super duplex (austenitic/ferritic) steel with 0.75% tungsten and copper

UNS S31803 Duplex Stainless Steel. 22% chromium duplex (austenitic/ ferritic) steel (2205 type)

UNS S32750 Super Duplex Stainless Steel. 25% chromium copper-free super duplex (austenitic/ferritic) steel (also known as 2507)

UNS S32550Super Duplex Stainless Steel. High performance 25% chromium super duplex (austenitic/ferritic) steel with 1.75% copper

Alloy 2205 provides pitting and crevice corrosion resistance

superior to 316L or 317L austenitic stainless steels in almost all corrosive

media. It also has high corrosion and erosion fatigue properties as well as

lower thermal expansion and higher thermal conductivity than austenitic. The

yield strength is about twice that of austenitic stainless steels. This allows

a designer to save weight and makes the alloy more cost competitive when

compared to 316L or 317L austenitic stainless steels. Alloy 2205 is

particularly suitable for applications covering the – 45ºC / +315ºF temperature

range. Temperatures outside this range may be considered but need some

restrictions, particularly for welded structures.

90

4.3.3 INCONEL 600

Inconel refers to a family of austenitic nickel-chromium-based

superalloys. Inconel alloys are typically used in high temperature applications.

Common trade names for Inconel include: Inconel 625, Chronin 625, Altemp

625, Haynes 625, Nickelvac 625, and Nicrofer 6020.

The Inconel family of alloys was first developed in the 1940s by

research teams at Wiggin Alloys (Hereford, England), which has since been

acquired by SMC, in support of the development of the Whittle jet engine.

Different Inconels have widely varying compositions, but all are

predominantly nickel, with chromium as the second element. Inconel 600 is

covered by the 1) BS 3075 and BS 3076 NA 14, 2) AMS 5687 and 3) ASTM

B166 standards. Inconel 600 is the trade name of Special Metals Group of

Companies and equivalent to: UNS N06600, W.NR 2.4816 and AWS 010.

The tables 4.6 gives the compositions of various Inconel alloys. The physical

properties of Inconel 600 is tabulated in table 4.7.

Table 4.6: Compositions of Inconel Alloy

Inconel Element (% by mass)

600 Ni 72%, Cr 17%, Fe 10%, Mn 1% with Cu, Si, C, S

617 Ni 56%, Cr 24%, Fe 3%, Mo 10%, with Mn, Cu, Al, Ti, Si, C, S

625 Ni 58%, Cr 23%, Fe 5%, Mo 10%, with Mn, Al, Ti, Si, C, S

718 Ni 55%, Cr 21%, Mo 3%, Nb 5% with Co, Fe, Mn, Al, Cu, Ti, Si, C, S

X-750 Ni 70%, Cr 17%, Fe 9%, Nb 1% with Co, Mn, Al, Cu, Ti, Si, C, S

91

Table 4.7: Physical properties of Inconel 600

Density 8.47 g/cm3

Melting point 1413 °C

Coefficient of Expansion 13.3 µm/m.°C (20-100°C)

Modulus of rigidity 75.6 kN/mm2

Modulus of elasticity 206 kN/mm2

Inconel alloys are oxidation and corrosion resistant materials, well

suited for service in extreme environments subjected to pressure and heat.

When heated, Inconel 600 forms a thick, stable, passivating oxide layer

protecting the surface from further attack. Inconel 600 retains strength over

a wide temperature range, attractive for high temperature applications where

aluminum and steel would succumb to creep as a result of thermally-induced

crystal vacancies. Inconel's high temperature strength is developed by solid

solution strengthening or precipitation strengthening, depending on the alloy.

In age hardening or precipitation strengthening varieties, small amounts of

niobium combine with nickel to form the intermetallic compound Ni3Nb or

gamma prime ( '). Gamma prime forms small cubic crystals that inhibit slip

and creep effectively at elevated temperatures.

Inconel 600 is a difficult metal to shape and machine using

traditional techniques due to rapid work hardening. After the first machining

pass, work hardening tends to plastically deform either the workpiece or the

tool on subsequent passes. For this reason, age-hardened Inconels such as 718

are machined using an aggressive but slow cut with a hard tool, minimizing

the number of passes required. Alternatively, the majority of the machining

can be performed with the workpiece in a solutionized form, with only the

final steps being performed after age-hardening. External threads are

92

machined using a lathe to "single point" the threads, or by rolling the threads

using a screw machine. Holes with internal threads are made by welding or

brazing threaded inserts made of stainless steel. Internal threads can also be

formed using EDM machining.

Cutting of plate is often done with a water jet cutter. Internal

threads can also be cut by single point method on lathe, or by thread milling

on a machining center. New whisker reinforced ceramic cutters are also used

to machine nickel alloys. They remove material at a rate typically 8 times

faster than carbide cutters. 718 Inconel can also be roll threaded after full

aging by using induction heat to 1300 degrees F without increasing grain size.

Apart from these methods, Inconel parts can also be manufactured by

Selective laser melting.

Welding Inconel alloys is difficult due to cracking and

microstructural segregation of alloying elements in the heat affected zone.

However, several alloys have been designed to overcome these problems. The

most common welding methods are gas tungsten arc welding and electron

beam welding. New innovations in pulsed micro laser welding have also

become more popular in recent years.

Inconel 600 is chiefly used in gas turbine blades, seals, and

combustors, as well as turbocharger rotors and seals, electric submersible well

pump motor shafts, high temperature fasteners, chemical processing, and

pressure vessels, heat exchanger tubing, steam generators in nuclear

pressurized water reactors, natural gas processing with contaminants such as

H2S and CO2, firearm sound suppressor blast baffles, and Formula One and

93

NASCAR exhaust systems. Inconel 600 is increasingly used in the boilers of

waste incinerators.

Inconel 600 is used in the construction of higher end firearms sound

suppressors and muzzle devices. This is especially common in suppressors

designed to be especially small or for use with machine guns. Rolled Inconel

600 was frequently used as the recording medium by engraving in black box

recorders on aircraft.

Alternatives to the use of Inconel 600 in chemical applications such

as scrubbers, columns, reactors, and pipes are Hastelloy, perfluoroalkoxy

(PFA) lined carbon steel or fiber reinforced plastic.

Alloy 600 is a nonmagnetic, nickel-based high temperature alloy

possessing an excellent combination of high strength, hot and cold

workability, and resistance to ordinary form of corrosion. This alloy also

displays good heat resistance and freedom from aging or stress corrosion

throughout the annealed to heavily cold worked condition range.

The high chromium content of alloy 600 raises its oxidation

resistance considerably above that of pure nickel, while its high nickel content

provides good corrosion resistance under reducing conditions. This alloy

exhibits high levels of resistance to stress and salt water, exhaust gases, and

most organic acids and compounds.

Alloy 600 is not an age hardening alloy; cold working is the only

available means of hardening. Softening by annealing begins at about 871°C,

94

and is reasonably complete after 10 to 15 minutes of heating at 982°C. Above

this temperature, grain growth may be objectionable, although very brief

heating at 1037°C will cause complete softening without undue grain growth.

Since the rate of cooling has no effect on the softening, the material may be

water quenched or air cooled.

Low sulfur reducing furnace atmospheres should be used in

forging. Major hot working should be done between 1260/1010°C, while light

working may be continued as low as 871°C. No hot working should be

attempted between 871/648°C due to lower ductility in that range.

95

CHAPTER 5

EXPERIMENTAL RESULTS AND ANALYSIS

5.1 INTRODUCTION

In this chapter, the experimental results obtained from Taguchi

experimental design method is discussed elaborately. The scheme of

experiments to investigate the effect of process parameter on MRR has been

selected in line with Taguchi design methodology.

5.2 SELECTION OF ORTHOGONAL ARRAY

In order to identify the true behavior of MRR, five process

parameters each at three levels have been considered for this study. The levels

of the individual process parameter are given in table 5.1, 5.2, and 5.3 for

Nickel, SDSS, and Inconel 600 respectively.

Table 5.1: Process Parameters and their Levels - NICKEL

Factor EC V C DC FLevel 1 0.1 3.5 0.1 33.33 30Level 2 0.2 5.0 0.3 50.00 40Level 3 0.3 6.5 0.5 66.66 50

96

Table 5.2: Process Parameters and their Levels - SDSS

Factor EC V C DC FLevel 1 0.40 8 0.6 33.33 30Level 2 0.45 9 0.8 50.00 40Level 3 0.50 10 1.0 66.66 50

Table 5.3: Process Parameters and their Levels - Inconel 600

Factor EC V C DC FLevel 1 0.40 8 0.6 33.33 30Level 2 0.45 9 0.8 50.00 40Level 3 0.50 10 1.0 66.66 50

EC: Electrolyte Concentration (mol/lit), V: Voltage (Volt), C: Current (Ampere), DC: Duty Cycle (%), F: Frequency (Hz).

A set of three levels assigned to each process parameter with two

degrees of freedom (DOF) as per Taguchi experimental design philosophy.

This gives a total of ten DOF for five process parameters selected in this work.

The five process parameters; Electrolyte concentration,

Machining Current, Machining Voltage, Duty Cycle, and Frequency are

studied in this investigation with each parameter having two degrees of

freedom. As per the standards of Taguchi methodology, 7 degrees of

freedom assigned for Error. Thus we have a total of 17 DOF for the factors

as well as the interactions considered for the present experiments. The

nearest three level orthogonal array available satisfying the criterion of

selecting the OA is L18 having 17 DOF (Phillip J. R. 1988). The layout of

experiments designed using Taguchi Design methodology has been

furnished in table 5.4.

97

Table 5.4: Experiment Layout using L18 Orthogonal Array

Exp. No

Levels of Process Parameters Electrolyte

concentrationMachining

Voltage Machining

Current Duty cycle Frequency

1 1 1 1 1 12 1 2 2 2 23 1 3 3 3 34 2 1 1 2 25 2 2 2 3 36 2 3 3 1 17 3 1 2 1 38 3 2 3 2 19 3 3 1 3 210 1 1 3 3 211 1 2 1 1 312 1 3 2 2 113 2 1 2 3 114 2 2 3 1 215 2 3 1 2 316 3 1 3 2 317 3 2 1 3 118 3 3 2 1 2

The levels of process parameters are selected based on the research

done by various researchers and based on the pilot experiments conducted for

this research work. It is observed that, for pure Nickel, the electrolyte

concentration and machining voltage requirement are less than that of SDSS and

Inconel 600. Similarly, the machining current requirement of other Nickel alloys

are higher than that of pure Nickel. The duty cycle and frequency levels are kept

uniform for Nickel, SDSS, and Inconel 600 throughout this experimental

investigation.

98

The levels of process parameters selected based on the Taguchi’s

design methodology for Nickel to fit L18 orthogonal array is given in table 5.5.

Table 5.5: Orthogonal Array of Process Parameters - NICKEL

Exp. No

Electrolyte concentration

(mol/lit)

Machining Voltage (Volts)

Machining Current (Amps)

Duty cycle(%)

Frequency (Hz)

1 0.1 3.5 0.1 33.33 302 0.1 5.0 0.3 50.00 403 0.1 6.5 0.5 66.66 504 0.2 3.5 0.1 50.00 405 0.2 5.0 0.3 66.66 506 0.2 6.5 0.5 33.33 307 0.3 3.5 0.3 33.33 508 0.3 5.0 0.5 50.00 309 0.3 6.5 0.1 66.66 4010 0.1 3.5 0.5 66.66 4011 0.1 5.0 0.1 33.33 5012 0.1 6.5 0.3 50.00 3013 0.2 3.5 0.3 66.66 3014 0.2 5.0 0.5 33.33 4015 0.2 6.5 0.1 50.00 5016 0.3 3.5 0.5 50.00 5017 0.3 5.0 0.1 66.66 3018 0.3 6.5 0.3 33.33 40


design methodology for SDSS to fit L18 orthogonal array is given in table 5.6.

99

Table 5.6: Orthogonal Array of Process Parameters for SDSS

Exp. No


(mol/lit)



Duty cycle(%)

Frequency (Hz)

1 0.40 8 0.6 33.33 302 0.40 9 0.8 50.00 403 0.40 10 1.0 66.66 504 0.45 8 0.6 50.00 405 0.45 9 0.8 66.66 506 0.45 10 1.0 33.33 307 0.50 8 0.8 33.33 508 0.50 9 1.0 50.00 309 0.50 10 0.6 66.66 4010 0.40 8 1.0 66.66 4011 0.40 9 0.6 33.33 5012 0.40 10 0.8 50.00 3013 0.45 8 0.8 66.66 3014 0.45 9 1.0 33.33 4015 0.45 10 0.6 50.00 5016 0.50 8 1.0 50.00 5017 0.50 9 0.6 66.66 3018 0.50 10 0.8 33.33 40


design methodology for Inconel 600 to fit L18 orthogonal array is given in table

5.7.

100

Table 5.7: Orthogonal Array of Process Parameters for Inconel 600

Exp. No


(mol/lit)



Duty cycle(%)

Frequency (Hz)

1 0.40 8 0.6 33.33 302 0.40 9 0.8 50.00 403 0.40 10 1.0 66.66 504 0.45 8 0.6 50.00 405 0.45 9 0.8 66.66 506 0.45 10 1.0 33.33 307 0.50 8 0.8 33.33 508 0.50 9 1.0 50.00 309 0.50 10 0.6 66.66 4010 0.40 8 1.0 66.66 4011 0.40 9 0.6 33.33 5012 0.40 10 0.8 50.00 3013 0.45 8 0.8 66.66 3014 0.45 9 1.0 33.33 4015 0.45 10 0.6 50.00 5016 0.50 8 1.0 50.00 5017 0.50 9 0.6 66.66 3018 0.50 10 0.8 33.33 40

The process parameters for SDSS and Inconel 600 are selected in

similar range as the electro-chemical, physical and mechanical characteristics are

by and large identical. It is inferred from the literature survey that, high density

of current is required for alloys with high steel content.

The machining voltage is chosen in a range from 8 to 10 volts to

achieve an appreciable MRR. It’s observed that higher voltage and moderate

value of pulse on time will produce a more accurate shape with fewer overcuts at

101

moderate MRR (Mithra S, Boro A.K 2002). The moderate electrolyte

concentration with high frequency can reduce the dimensional deviation with

lesser number of micro sparks (Malapati M, Munda J, Sarkar A 2007) and hence

a frequency range of 30 to 50 Hz is selected for this study. High precision is

achieved in ECMM by better monitoring and control of the IEG accurately and

minimizing the micro sparks at IEG. Suitable duty cycle is highly important for

maintaining IEG as the off time is used to clear the debris from the machining

zone.

5.3 EXPERIMENTAL RESULTS

The ECMM experiments are conducted with brass wire tool of

250 microns diameter for Nickel. The tool used for machining SDSS and

Inconel 600 is stainless steel wire of 250 microns diameter. In order to

achieve proper circularity of machined holes, the anode tool is properly

ground. The test job specimens are kept uniform in size for Nickel, SDSS,

and Inconel 600 measuring 50 mm × 25 mm × 0.15 mm. The test

specimens are prepared using WEDM machine and after machining, the

specimens treated to retain their originality. The electrolyte used for

Nickel and its alloys is NaNO3.

The ECMM experiments were conducted twice in each

combination of process parameters to study its effect over MRR. From the

trial 1 and trial 2 experiments, the average MRR is calculated and tabulated for

Nickel in table 5.8. In the present study all the designs, plots and analysis

have been carried out using Minitab statistical software.

102

Table 5.8: Experimental Results for MRR - NICKEL

Exp. No.Machining

Time in Trial 1

Machining Time in Trial 2

MRRTrial 1

MRRTrial 2

Average MRR

1 21.0 15.0 0.001660 0.001604 0.001632 2 8.0 13.0 0.002127 0.002119 0.002123 3 4.0 3.5 0.006223 0.006391 0.006307 4 14.0 22.0 0.001815 0.001863 0.001839 5 6.5 5.5 0.004089 0.003977 0.004033 6 2.8 2.5 0.006767 0.006639 0.006703 7 10.5 19.5 0.002869 0.002879 0.002874 8 2.5 3.8 0.009628 0.009526 0.009577 9 5.5 5.0 0.003399 0.003053 0.003226 10 8.0 11.0 0.003722 0.003726 0.003724 11 12.0 16.0 0.001343 0.001395 0.001369 12 8.0 4.8 0.004621 0.004729 0.004675 13 5.5 8.0 0.004832 0.004782 0.004807 14 4.3 9.8 0.004378 0.004426 0.004402 15 15.0 22.0 0.001873 0.001887 0.001880 16 5.0 5.5 0.003884 0.003978 0.003931 17 5.5 4.3 0.003399 0.003387 0.003393 18 3.0 4.0 0.006474 0.006618 0.006546

The average MRR is calculated in similar fashion for SDSS and

Inconel 600. The respective tables are listed under Appendix 1 as table A 1.1

and table A 1.2. From this point onwards, the Average MRR is termed as

“MRR”.

103

5.3.1 Experimental Results - Nickel

The machining time, MRR, dimensional deviation and

calculated S/N Ratio are given for Nickel in table 5.9.

Table 5.9: Experimental Results - NICKEL

Exp. No

Machining time (min)

MaterialRemoval

Rate(mm3/min.)

Dimensional Deviation (microns)

S/N Ratio

1 18.00 0.001632 29 – 55.7456 2 10.50 0.002123 13 – 53.4610 3 4.00 0.006307 20 – 44.0035 4 18.00 0.001839 21 – 54.7084 5 6.00 0.004033 22 – 47.8874 6 2.63 0.006703 14 – 43.4746 7 15.00 0.002874 25 – 50.8303 8 3.13 0.009577 20 – 40.3754 9 5.25 0.003226 14 – 49.8267 10 9.50 0.003724 25 – 48.5798 11 14.00 0.001369 11 – 57.2719 12 6.38 0.004675 31 – 46.6044 13 6.75 0.004807 22 – 46.3625 14 7.00 0.004402 14 – 47.1270 15 18.50 0.001880 23 – 54.5168 16 5.25 0.003931 15 – 48.1099 17 4.88 0.003393 14 – 49.3883 18 3.50 0.006546 15 – 43.6805

It can be seen from the experimental results of Nickel, the obtained MRR

ranges from 0.001369 to 0.009577 mm3/min, while the dimension deviation

stood between 11 and 31 microns.

104

The eighth combination of levels of process parameters, A3B2C3D2E1

gave maximum MRR of 0.009577 mm3/min. At this combination, the machining

performance is better with a low dimensional deviation of 20 microns. Hence

this combination proved to be the optimum process parameter to produce the

maximum MRR on Nickel. The microscopic image of the outcome of 8th

experiment is shown as figure 5.1.

Figure 5.1: Image of micro hole machined in 8th experiment

Parameters : Electrolyte Concentration : 0.3 mol/lit Voltage: 5 Volts Current: 0.5 amps Duty Cycle: 50% Frequency: 30 Hz MRR: 0.009577 mm3/min Dimensional Deviation: 20 microns

The twelfth combination of process parameters EC1V3C2DC2F1

produced an above average MRR (0.004675 mm3/min) with moderate

machining time. However, the dimensional deviation has peaked with 31

microns. The microscopic image of the outcome of 12th experiment is shown as

figure 5.2.

105


Parameters: Electrolyte Concentration: 0.1 mol/lit Voltage: 6.5 Volts Current: 0.3 amps Duty Cycle: 50% Frequency: 30 Hz MRR: 0.004675 mm3/min Dimensional Deviation: 31 microns

The process parameters combination EC1V2C1DC1F3 is chosen as

the 11th combination in the experimental investigation. This combination

yielded the least MRR (0.001369 mm3/min) with 31 microns of dimensional

deviation in spite of higher machining time (6.38 min.). The microscopic

image of the outcome of 11th experiment is shown as figure 5.3.


Parameters: Electrolyte Concentration: 0.1 mol/lit Voltage: 5 Volts Current: 0.1 Amps Duty Cycle: 33.33% Frequency: 50 Hz MRR: 0.001369 mm3/min Dimensional Deviation: 11 microns

106

5.3.2 Experimental Results - SDSS

The machining time, MRR, dimensional deviation and

calculated S/N Ratio are given for SDSS in table 5.10.

Table 5.10: Experimental Results - SDSS

Exp. No


MaterialRemoval

Rate(mm3/min.)

Dimensional Deviation (microns)

S/N Ratio

1 22.00 0.0009092 20 – 60.8266 2 21.00 0.0020666 24 – 53.6948 3 24.00 0.0026119 26 – 51.6608 4 10.20 0.0025774 22 – 51.7763 5 13.00 0.0045603 28 – 46.8201 6 14.50 0.0025698 20 – 51.8020 7 26.00 0.0008991 21 – 60.9233 8 11.00 0.0057275 38 – 44.8405 9 9.50 0.0075404 39 – 42.4521 10 11.15 0.0020446 20 – 53.7879 11 24.30 0.0018904 22 – 54.4689 12 13.00 0.0028575 28 – 50.8802 13 13.15 0.0043241 32 – 47.2820 14 20.30 0.0017994 23 – 54.8974 15 12.20 0.0046340 27 – 46.6808 16 11.15 0.0092938 25 – 40.6361 17 8.50 0.0170660 30 – 35.3573 18 14.00 0.0025572 26 – 51.8447

It can be seen from the experimental results of SDSS, the obtained

MRR ranged from 0.000899 to 0.0170660 mm3/min. The dimension deviation

varied between 20 and 39 microns.

107


Parameters: Electrolyte Concentration : 0.5 mol/lit Voltage: 9 Volts Current: 0.6 Amps Duty Cycle: 66.66% Frequency: 30 Hz MRR: 0.0170660 mm3/min Dimensional Deviation : 30 microns

The optimum combination of process parameters for SDSS has been

identified as the 17th combination of the experiments carried out

(EC3V2C1DC3F1). The maximum MRR resulted was 0.017066 mm3/min while

the dimensional deviation stood at 30 microns. The machining process was an

effective one with less machining time of 8.5 min. The microscopic image of the

outcome of 17th experiment is shown as figure 5.4.


108

Parameters: Electrolyte Concentration : 0.5 mol/lit Voltage: 8 Volts Current: 1 Amps Duty Cycle: 50 % Frequency: 50 Hz MRR : 0.009294 mm3/min. Dimensional Deviation: 25 microns

The second best result achieved for maximum MRR is with the 16th

combination of levels of process parameters selected. The results obtained were

MRR: 0.009294 mm3/min, dimensional deviation of 25 microns and

a machining time of 11.15 minutes. The microscopic image of the outcome of

16th experiment is shown as figure 5.5.

Figure 5.6: Image of micro hole machined in 7th experiment.

Parameters: Electrolyte Concentration : 0.5 mol/lit Voltage: 8 volts Current: 0.8 Amps Duty Cycle: 33.33% Frequency: 50 Hz MRR : 0.0008991 mm3/min. Dimensional Deviation : 21 microns

The least MRR of 0.0008991 mm3/min. has been exhibited for SDSS

was by the 7th combination (EC3V1C2DC1F3) of levels of experimental

parameters. This was evident with minimum MRR, higher deviation and

maximum machining time posted in this combination. The microscopic image of

the outcome of 7th experiment is shown as figure 5.6.

109

5.3.3 Experimental Results - Inconel 600

Table 5.11: Experimental Results - Inconel 600

Exp. No


MaterialRemoval

Rate(mm3/min.)

Dimension Deviation (microns)

S/N Ratio

1 32.5 0.0003606 10 – 68.8583 2 23.5 0.0008293 32 – 61.6253 3 15.0 0.0010358 35 – 59.6945 4 30.0 0.0006629 18 – 63.5700 5 22.0 0.0007723 29 – 62.2446 6 19.0 0.0010680 36 – 59.4286 7 31.5 0.0003672 16 – 68.7015 8 26.0 0.0004046 18 – 67.8593 9 20.0 0.0007331 22 – 62.6969 10 22.0 0.0007638 24 – 62.3397 11 29.0 0.0006052 19 – 64.3616 12 25.0 0.0007172 19 – 62.8867 13 20.0 0.0008402 31 – 61.5119 14 29.0 0.0006183 18 – 64.1759 15 23.5 0.0007549 20 – 62.4419 16 27.5 0.0006044 22 – 64.3737 17 13.0 0.0012926 30 – 57.7707 18 26.0 0.0004046 16 – 67.8595

It can be observed from the experimental results of Inconel 600 (table

5.11) that the MRR varied between 0.000361 mm3/min and 0.001293

mm3/min for Inconel 600. The dimensional deviation ranged from 10 to 36

microns. Based on the S/N Ratio (higher-the-better), it is inferred that the 17th

combination of process parameters are the best for maximum MRR.

110


Parameters: Electrolyte Concentration : 0.5 mol/lit Voltage: 9 volts Current: 0.6 Amps Duty Cycle: 66.66% Frequency: 30 Hz MRR : 0.0012926 mm3/min. Dimensional Deviation : 30 microns

The Inconel 600 has been machined effectively with the

EC3V2C1DC3F1 combination of levels of process parameters. In this 17th

combination, the MRR obtained was the maximum at 0.0012926 mm3/min

with a dimensional deviation of 30 microns. The microscopic image of the hole

machined with 17th combination is shown in figure 5.7.


111

Parameters: Electrolyte Concentration : 0.45 mol/lit Voltage: 10 volts Current: 1 Amps Duty Cycle: 33.33% Frequency: 30 Hz MRR : 0.0010680 mm3/min. Dimensional Deviation : 36 microns

The second best result has been produced in the 6th experiment with

a process parameter’s combination level of EC2V3C3DC1F1. The MRR

obtained, 0.0010680 mm3/min is the second best for the Inconel 600 with

a dimensional deviation of 36 microns. The microscopic image of the hole

machined with 6th combination is shown in figure 5.8.

Figure 5.9: Image of micro hole machined in 1st experiment

Parameters : Electrolyte Concentration : 0.4 mol/lit Voltage: 8 Volts Current: 0.6 Amps. Duty Cycle: 33.33% Frequency: 30 Hz MRR : 0.0003606 mm3/min Dimensional Deviation : 10 microns

The 1st combination of levels of process parameters EC1V1C1DC1F1

has resulted in the lowest MRR of 0.0003606 mm3/min. Although the

dimensional deviation was nominal with 10 microns, the machining time has

peaked. The microscopic image of the hole machined with 1st combination is

shown in figure 5.9.

112

5.4 ANALYSIS AND DISCUSSION OF RESULTS

In order to study the significance of the process parameters

towards MRR, analysis was done by Taguchi method using Minitab 15

software. The tables include ranks based on delta statistics, which compare the

relative magnitude of effects. The delta statistic is the highest minus the lowest

S/N Ratio for each factor. Minitab assigns ranks based on delta values; rank 1 to

the highest delta value, rank 2 to the second highest, and so on. The ranks

indicate the relative importance of each factor to the response. The mean value

of MRR and S/N ratio for each parameter at different levels were

calculated. The main effects of means of process parameter for S/N data were

plotted. The response curves have been used to examine the effects of process

parameter on the MRR.

Using ANOVA, Adjusted mean squares, F-Test Value, P-Test value

were calculated based on the S/N Ratio and Percentage of Contributions of each

process parameter on MRR has been arrived. The interaction between process

parameters on MRR has been plotted and analyzed.

5.5 CONFIRMATION TEST

The optimum level of process parameters has been determined by

using S/N ratio values (higher-the-better). Once the optimal level of the

process parameters has been selected, the final step is to predict and verify the

improvement of the performance characteristic using the optimal level of the

process parameters. The purpose of conformation test is to validate the

conclusions drawn from analysis of experimental results. The predicted or

estimated S/N ratio using optimal levels of process parameters can be

calculated with the following equation;

113

= m + i=1q ( i – m ) (5.1)

where m = total mean of S/N ratio,

q = no. of significant parameters,

i = mean of S/N ratio at optimum level

After predicting the response (S/N ratio), a confirmation

experiment has been designed and conducted with the optimal levels of the

machining parameters to verify the improvement of performance

characteristics.

5.5.1 Results and Discussion : Nickel

The means of MRR and Delta value are calculated using Taguchi

methodology. Based on the delta value, the process parameters are ranked for its

influence on MRR. Table 5.12 shows the means, delta value and the ranks of

process parameters for Nickel.

Table 5.12: Response table for Means - NICKEL

Level EC V C DC F1 0.003305 0.003134 0.002223 0.003921 0.005131 2 0.003944 0.004149 0.004176 0.004004 0.003643 3 0.004924 0.004889 0.005774 0.004248 0.003399

Delta 0.001619 0.001755 0.003550 0.000327 0.001732 Rank 4 2 1 5 3

114

The above tabulated mean values are plotted as figure 5.10 to

pictorially represent the contribution of each process parameter on MRR.

0.30.20.1

0.006

0.005

0.004

0.003

0.0026.55.03.5 0.50.30.1 66.6650.0033.33 504030

EC

Mea

no

fM

ean

s

V C DC F

Main Effects Plot for MeansData Means

Figure 5.10: Main Effect Plot for Means - NICKEL

The main effect plot for means (Data Means) for Nickel shows that

the major contributor for maximum MRR is Machining Current, followed by

the Machining Voltage (table 5.12). The increase in the machining current

increases the current density at the machining zone. Hence, the high current

density supported with machining voltage has emerged as major contribution

on MRR for Nickel. The following Table 5.13 contains the S/N Ratio calculated

using Taguchi methodology.

115

Table 5.13: Response table for S/N Ratio - NICKEL

Level EC V C DC F1 – 50.95 – 50.72 – 53.64 – 49.69 – 46.93

2 – 49.01 – 49.25 – 48.14 – 49.63 – 49.69

3 – 47.10 – 47.08 – 45.28 – 47.74 – 50.44

The S/N Ratio is used to predict the optimal level of combination of

process parameter for maximum MRR. This predicted level is used to perform

confirmation experiment. The predicted combination is chosen as the maximum

S/N ratio of each parameter. Thus, a combination EC3V3C3DC3F1 has been

achieved for Nickel.

Table 5.14: Results of ANOVA - NICKEL

I II III IV V VI VII VIII

EC 2 45.847 45.847 22.924 7.12 0.021 12.68

V 2 41.763 41.763 20.882 6.48 0.026 11.55

C 2 213.219 213.219 106.609 33.1 0.0 58.98

DC 2 15.757 15.757 7.878 2.45 0.156 4.36

F 2 38.489 38.489 19.244 5.98 0.031 10.65

Error 7 22.544 22.544 3.221 1.78

Total 17 377.619 100.00

EC : Electrolyte Concentration (mol/lit) V : Voltage (volts) C : Current (amps) DC : Duty Cycle (%) F : Frequency (Hz) I : Parameters II : Degrees of Freedom III : Sequential Sum of Squares

116

IV : Adjusted Sum of Squares V : Adjusted mean squares VI : F-Test Value VII : P-Test Value VIII : Contributions %

It is clearly evident from the results of ANOVA (Table 5.14) for

Nickel that the machining current is the dominant factor affecting MRR with

58.98% contribution, which is supported by the frequency and voltage with

a contribution of 12.68% and 11.55% respectively. The graphical

representation of the same is given in figure 5.11.

Figure 5.11: Contribution of Process Parameters on MRR - Nickel

The normal probability plot of residuals shown in figure 5.12

confirms that the experimental results are distributed normally as it follows

a straight line without any outliers.

117

3210-1-2-3

99

95

90

80

7060504030

20

10

5

1

Re sidua l

Per

cent

N orm al Probability Plot(response is SNRA 1)

Figure 5.12: Normal Probability Plot (S/N Ratio) - Nickel

0.009

0.006

0.003

504030

6.55.03.5 66.6650.0033.33

0.009

0.006

0.003

0.009

0.006

0.003

0.009

0.006

0.003

0.30.20.1

0.009

0.006

0.003

0.50.30.1

EC

V

C

DC

F

0.10.20.3

EC

3.55.06.5

V

0.10.30.5

C

33.3350.0066.66

DC

304050

F

Interaction Plot for MRRData Means

Figure 5.13: Process Parameter Interaction Plot (MRR) - Nickel

The interaction plot (Figure 5.13) has been plotted to pictorially

depict the interactions process parameters on MRR. In the above full interaction

118

plot, two panels per pair of process parameters has been shown. The following

are the inference made from the Interaction Plot.

The major contributor, machining current shows interaction with all

other four process parameters.

The maximum MRR has been achieved when Electrolyte

concentration and Current is at 3rd level i.e. 0.3 mol/lit. and 0.5

amps respectively.

The 2nd major contributor for maximum MRR i.e. machining

voltage is at 2nd level (5.0 volts) and machining current at 3rd

level (0.5 amps) produced high MRR. However, when current

and voltage is at 3rd level, the MRR slightly came down.

The combination of 50% duty cycle and 0.5 amps machining

current yielded high MRR. But, when duty cycle and current at

their maximum, the MRR slightly decreased.

The frequency interacted with current and produced a linear

response i.e. when frequency at 30 Hz, it MRR proportionally

increased and reached its maximum with 0.5 amps machining

current.

It is clearly understood from the above interaction plot and the

inferences made, the maximum MRR is resulted only when the machining

current combines with other process parameter at its maximum value of 0.5

amps.

119

Table 5.15: Results of Confirmation Test - NICKEL

Initial Combination Prediction Experiment

Parameter Combination EC1V1C1DC1F1 EC3V3C3DC3F1 EC3V2C3DC2F1

MRR 0.001632 -- 0.009577

S/N Ratio – 55.7482 -- – 40.3754

Since, MRR is the higher the better type quality characteristic, it

can be seen from table 5.15 that for Nickel, the third level of Electrolyte

Concentration (EC3), second level of Machining Voltage (V2), third level of

Machining Current (C3), second level of Duty Cycle (DC2) and first level of

Frequency (F1) provides maximum value of MRR against the predicted

parameter combination of EC3V3C3DC3F1. The deviation between the

predicted and experimental value of S/N Ratio is 4.44%, in other words, the

confidence level of experiments conducted is 95.56%.

Figure 5.14: Image of micro hole machined for confirmation experiment

Parameters: Electrolyte Concentration : 0.3 mol/lit Voltage: 6.5 volts Current: 0.5 Amps Duty Cycle: 66.66% Frequency: 30 Hz MRR : 0.009999 mm3/min. Dimensional Deviation : 18 microns

120

Implementation of GA - Nickel

MRR = – 0.00018 + 0.00810 EC +0.000585 C + 0.00888 V + 0.000010 DC – 0.000087 F

MRR = @(x) – (0.00018 + (0.00810*EC)+(0.000585*C) + (0.00888*V) +(0.000010*DC)–(0.000087*F))

Parameters LevelsElectrolyte concentration (EC): 0.1 EC 0.3 Machining current (C): 0.1 C 0.3 Machining voltage (V): 3.5 V 6.5 Duty cycle (DC): 33.33 DC 66.66 Frequency (F): 30 F 50

Figure 5.15: Comparison between GA and EV for Nickel

The figure 5.15 shows the similarity between the genetically

optimized value (GA) and the experimental value (EV) of process parameters for

Nickel. It can be inferred from the chart that out of five process parameters, two

parameters matches exactly with the GA values. Three parameters i.e.

121

Machining Voltage (V), Machining Current (C), and the Duty Cycle (DC)

differs marginally.

Figure .5.16: Screen Shot of GA output for Nickel

The figure 5.16 shows the screen shot of Genetic Algorithm Tool

used to optimize the process parameters.

5.5.2 Results and Discussion : SDSS

The means of MRR and Delta value are calculated using Taguchi

methodology. Based on the delta value, the process parameters are ranked for its

influence on MRR and tabulated in table 5.16.

122

Table 5.16: Response table for Means - SDSS

Level EC V C DC F1 0.002063 0.003341 0.005770 0.001771 0.005576

2 0.003411 0.005518 0.002877 0.004526 0.003098

3 0.007181 0.003795 0.004008 0.006358 0.003982

Delta 0.005117 0.002177 0.002892 0.004587 0.002478

Rank 1 5 3 2 4

A main effect plot is given in figure 5.17 for the above tabulated

mean values to help easy inference of effects of process parameter on MRR.

0.500.450.40

0.007

0.006

0.005

0.004

0.003

0.002

0.0011098 1.00.80.6 66.6650.0033.33 504030

EC

Mea

nof

Mea

ns

V C DC F

Main Effects Plot for MeansData Means

Figure 5.17: Main Effect Plot for Means - SDSS

123

The main effect plot for means for SDSS shows that the MRR is

greatly influenced by Electrolyte concentration. The second most influencing

process parameter is duty cycle. The greater the electrolyte concentration leads

to more number of ions dissolution and hence it is directly proportional to MRR.

Further, more the ON time will result in more time for electrolysis and hence

more MRR. Thus, these two parameters contribute for achieving maximum

MRR for SDSS.

Table 5.17: Response table for S/N Ratio - SDSS

Level EC V C DC F1 – 54.22 – 52.54 – 48.59 – 55.79 – 48.50

2 – 49.88 – 48.35 – 51.91 – 48.08 – 51.41

3 – 46.01 – 49.22 – 49.60 – 46.23 – 50.20

In order to predict the optimal level of combination of process

parameter for maximum MRR, the S/N Ratio is used by Taguchi methodology.

EC3V2C1DC3F1 has been chosen as the predicted combination process

parameters for SDSS (Table 5.17) based on the higher-the-better of S/N ratio.

The confirmation experiment has been conducted based on the predicted level.

The ANOVA results depicting the percentage contribution of the

process parameters are tabulated in table 5.18. In ECMM of SDSS, the duty

cycle plays the dominant role with 46.76% contribution on MRR. In this

experiment, the maximum MRR achieved with 66% duty cycle. Hence, it can

be inferred that less time is taken for maintaining the IEG and hence it is

possible to increase the duty cycle.

124

Table 5.18: Results of ANOVA - SDSS

Source DF Seq SS Adj SS Adj MS F P Cont. %

EC 2 202.48 202.48 101.24 6.7 0.024 30.66

V 2 58.7 58.7 29.35 1.94 0.213 8.89

C 2 34.62 34.62 17.31 1.15 0.371 5.24

DC 2 308.83 308.83 154.41 10.22 0.008 46.76

F 2 25.66 25.66 12.83 0.85 0.467 3.89

Error 7 105.73 105.73 15.1 4.56

Total 17 736.01 100.00

This characteristic of SDSS also gives room to increase rate of

dissolution by increasing the electrolyte concentration, which is confirmed by

its contribution of 30.66%. The machining voltage comes as third dominant

factor in affecting MRR of SDSS with 8.89%. The graphical representation of

contribution of process parameters on MRR is given in figure 5.18.

Figure 5.18: Contribution of Process Parameters on MRR - SDSS

125

Normal probability plot of residuals is used to analyze the normal

distribution of experimental results. Since, the probability plot for SDSS follows

a straight line without any outliers confirms the normal distribution of the

experimental results.

5.02.50.0-2.5-5.0-7.5

99

95

90

80

7060504030

20

10

5

1

Residual

Pe

rce

nt

Normal Probability Plot(response is SNRA1)

Figure 5.19: Normal Probability Plot (S/N Ratio) - SDSS

The figure 5.19 shows that the normal probability plot plotted for

SDSS showing the normal distribution of experimental results.

The interactions between the process parameters can be inferred

from the interaction plot. The full interaction plot (two panels per parameter)

given by Minitab 15 shows all possible interactions of process parameters.

Figure 5.20 shows the full interaction plot of SDSS.

126

0.010

0.005

0.000

504030

1098 66.6650.0033.33

0.010

0.005

0.000

0.010

0.005

0.000

0.010

0.005

0.000

0.500.450.40

0.010

0.005

0.000

1.00.80.6

EC

V

C

DC

F

0.400.450.50

EC

89

10

V

0.60.81.0

C

33.3350.0066.66

DC

304050

F


Figure 5.20: Process Parameter Interaction Plot (MRR) - SDSS

The interactions between the Duty Cycle (major contributor) and

other four parameters are discussed.

The MRR for SDSS reached its maximum when the duty cycle is

at 66.66%, and Electrolyte Concentration is at 0.5 mol/lit. This

clearly shows that SDSS allows more dissolution with high

electrolyte concentration for longer period.

The machining voltage of 9 volts (2nd level) in combination with

66.66% duty cycle produced high MRR. However, 3rd level of

voltage i.e. 10 volts gave lesser amount of MRR.

The interaction between duty cycle and machining current

reveals that maximum dissolution has been reached when the

current is at 0.6 amps (1st level) and duty cycle at 66.66%.

The MRR has reached the maximum with 66.66% duty cycle and

30Hz frequency (1st level).

127

The above inferences made from the interaction plot of SDSS clearly

reveal that the Duty Cycle supports maximum dissolution when it is at 66.66%

irrespective of the levels of other parameters.

Table 5.19: Results of Confirmation Test - SDSS



MRR 0.000909 -- 0.017066

S/N Ratio – 60.8266 -- – 35.3573

The predicted optimum combination of process parameters for

SDSS is EC3V2C1DC3F1. The confirmation experiment (table 5.19) reveals that

the optimum combination of process parameters for maximum MRR for SDSS

is third level of Electrolyte Concentration (EC3), second level of Machining

Voltage (V2), first level of Machining Current (C1), third level of Duty Cycle

(DC3) and first level of Frequency (F1), which is similar to the predicted

combination. The deviation between the predicted and experimental value

reveals a confidence level of 94.57% on the experimental results.

Implementation of GA - SDSS

MRR = – 0.0210 + 0.0512 EC + 0.000227 C – 0.00440 V + 0.000138 DC – 0.000080F

MRR = @(x) – (0.0210 + (0.0512*EC) + (0.000227*C) – 0.00440*V) + (0.000138*DC) – (0.000080*F))

128

Parameters LevelsElectrolyte concentration (EC): 0.4 EC 0.5 Machining current (C): 0.6 C 1.0Machining voltage (V): 8 V 10 Duty cycle (DC): 33.33 DC 66.66 Frequency (F): 30 F 50

Figure 5.21: Comparison between GA and EV for SDSS

The coherence between the genetically optimized value (GA) and the

experimental value (EV) of process parameters for SDSS has been plotted in

figure 5.21. It can be inferred from the chart that out of five process parameters,

the Machining Voltage (V), Machining Current (C), and Duty Cycle (DC)

matches exactly. The other two parameters i.e. Electrolyte Concentration (EC)

and Frequency (F) differ a little.

129

Figure 5.22: Screen Shot of GA output for SDSS



5.5.3 Results and Discussion of Inconel 600

Using Taguchi methodology, the means of MRR and Delta value are

calculated. The process parameters are ranked for its influence on MRR based

on the delta value. The means, delta value and ranks of process parameters for

Inconel 600 are tabulated in table 5.20.

130

Table 5.20: Experimental Results - Inconel 600

Level EC V C DC F1 0.000719 0.000600 0.000735 0.000571 0.0007812 0.000786 0.000754 0.000655 0.000662 0.0006693 0.000634 0.000786 0.000749 0.000906 0.000690

Delta 0.000152 0.000186 0.000094 0.000336 0.000112Rank 3 2 5 1 4

0 .5 00 .4 50 .4 0

0 .0 0 0 9 5

0 .0 0 0 9 0

0 .0 0 0 8 5

0 .0 0 0 8 0

0 .0 0 0 7 5

0 .0 0 0 7 0

0 .0 0 0 6 5

0 .0 0 0 6 0

1 098 1 .00 .80 .6 6 6 .6 65 0 .0 03 3 .3 3 5 04 03 0

E C

Mea

nof

Mea

ns

V C D C F

M ain E ffec ts P lo t fo r M ean sData Means

Figure 5.23: Main Effect Plot for Means - Inconel 600

The data means plotted for Inconel 600 (Figure 5.23) shows that the

maximum MRR has been achieved due to the 3rd level duty cycle of 66.66%.

The second major influencing factor on MRR is machining voltage, followed by

Electrolyte Concentration and Frequency. Since the dissolution is directly related

to the pulse ON time and debris removal has been achieved in short span of time

(pulse OFF time) Duty Cycle emerged as a dominant factor affecting MRR for

Inconel 600.

131

The Signal to Noise Ratio calculated using Taguchi methodology for

Inconel 600 is tabled in table 5.21.

Table 5.21: Response table for S/N Ratio - Inconel 600

Level EC V C DC F1 – 63.29 – 64.89 – 63.28 – 65.56 – 63.05

2 – 62.23 – 63.01 – 64.14 – 63.71 – 63.71

3 – 64.88 – 62.50 – 62.98 – 61.04 – 63.64

The optimal level of combination of process parameter for

maximum MRR is predicted using the S/N Ratio. The combination is selected

based on “greater the S/N Ratio, higher the performance”. Hence, the

predicted combination is EC3V3C3DC3F1. This predicted combination of levels

of process parameters has been used to conduct the confirmation experiment.

The results of ANOVA (Table 5.22) reveals that the dominant factor

affecting the MRR for Inconel 600 is Duty Cycle with a contribution of 49.15%.

The contribution by electrolyte concentration .is the second major influencing

factor with 16.81%, closely followed by the machining voltage with 15.04%

contribution.

132

Table 5.22 Results of ANOVA - Inconel 600

Source DF Seq SS Adj SS Adj MS F P Cont. %EC 2 21.305 10.652 10.652 1.17 0.364 16.81

V 2 19.061 9.531 9.531 1.05 0.399 15.04

C 2 4.337 2.169 2.169 0.24 0.794 3.42

DC 25 62.28 31.14 31.14 3.43 0.092 49.15

F 2 1.56 0.78 0.78 0.09 0.919 1.23

Error 7 63.592 9.085 9.085 14.35

Total 17 172.135 100.00

The figure 5.24 shows the percentage of contribution of each

parameter towards the maximum MRR for Inconel 600.

Figure 5.24: Contribution of Process Parameters on MRR - Inconel 600

133

5.02.50.0-2.5-5.0

99

95

90

80

7060504030

20

10

5

1

Residual

Pe

rce

nt

Normal Probability Plot(response is SNRA1)

Figure 5.25: Normal Probability Plot (S/N Ratio) - Inconel 600

The normal probability plot for Inconel 600, plotted with the

residuals is shown in figure 5.25. It reveals that the experimental results are

distributed normally.

The interactions made by the Duty Cycle (major contributor) with

other four parameters are discussed.

The MRR for Inconel 600 reached its maximum when the duty

cycle is at 66.66%, and Electrolyte Concentration is at 0.5 mol/lit.

This clearly shows that SDSS allows more dissolution with high

electrolyte concentration for longer period without creating any

short circuit at IEG.

The machining voltage of 9 volts (2nd level) in combination with

66.66% duty cycle produced high MRR. However, 3rd level of

voltage i.e. 10 volts gave lesser amount of MRR.

134

The interaction between duty cycle and machining current

reveals that maximum dissolution has been reached when the

current is at 0.6 amps (1st level) and duty cycle at 66.66%.

The MRR has reached the maximum with 66.66% duty cycle and

30Hz frequency (1st level).

0.00100

0.00075

0.00050

504030

1098 66.6650.0033.33

0.00100

0.00075

0.00050

0.00100

0.00075

0.00050

0.00100

0.00075

0.00050

0.500.450.40

0.00100

0.00075

0.00050

1.00.80.6

EC

V

C

DC

F

0.400.450.50

EC

89

10

V

0.60.81.0

C

33.3350.0066.66

DC

304050

F


Figure 5.26: Process Parameter Interaction Plot (MRR) - Inconel 600

The above inferences made from the interaction plot (Figure 5.26) of

Inconel 600 clearly reveals that the Duty Cycle supports maximum dissolution

when it is at 66.66% irrespective of the levels of other parameters.

135

Table 5.23: Results of Confirmation Test - Inconel 600



MRR 0.000361 -- 0.001293

S/N Ratio – 48.8583 -- – 37.7707

The optimum combination of process parameters calculated

statistically for Inconel 600 is EC2V3C3DC3F1, which is very similar to the

experimental values. The optimum combination of process parameters for

maximum MRR obtained with confirmation test for Inconel 600 (Table 5.23)

is third level of Electrolyte Concentration (EC second level of Machining

Voltage (V2), first level of Machining Current (C1), third level of Duty Cycle

(DC3) and first level of Frequency (F1).

Figure 5.27: Image of micro hole machined for confirmation experiment

Parameters: Electrolyte Concentration : 0.45 mol/lit Voltage: 10.0 volts Current: 1.0 Amps Duty Cycle: 66.66% Frequency: 30 Hz MRR : 0.001306 mm3/min. Dimensional Deviation : 11 microns

136

Implementation of GA - Inconel 600

MRR = – 0.00094 – 0.0084 EC + 0.000929 C + 0.00036 V + 0.000101DC – 0.000045F

MRR= @(x) – (0.00094 – (0.0084*EC) + (0.000929*C) + (0.00036*V) + (0.000101*DC) – 0.000045*F))

Parameters LevelsElectrolyte concentration (EC): 0.4 EC 0.5 Machining current (C): 0.6 C 1.0Machining voltage (V): 8 V 10 Duty cycle (DC): 33.33 DC 66.66 Frequency (F): 30 F 50

Figure 5.28: Comparison between GA and EV for Inconel 600

137

The coherence between the genetically optimized value (GA) and the

experimental value (EV) of process parameters for Inconel 600 has been given in

figure 5.28. The only two parameters which differ very little with GA values are

Electrolyte Concentration (EC) and Frequency (F). The other three process

parameters viz. Machining Voltage (V), Machining Current (C) and Duty Cycle

(DC) matches exactly.

Figure 5.29: Screen Shot of GA output for Inconel 600



138

5.6 DIMENSIONAL DEVIATION

In this research work, although the main emphasis is given for

studying effects on process parameters on MRR, the Dimensional Deviation

(DD) is also taken for analysis. A comparative study on the effects of process

parameters on MRR Vs. DD is made and the outcome is detailed hereunder.

5.6.1 Dimensional Deviation - Nickel

In order to easily compare the MRR and corresponding DD obtained

is plotted on logarithmic scale for Nickel as given in figure 5.30.

Figure 5.30: MRR Vs Dimensional Deviation - Nickel

139

In general, the DD is directly proportional to the MRR. However, it

can be inffered from the figure 5.30 that due to the effect of levels of various

process parameters, the relationship between MRR and DD is not linear.

The maximum DD reported in the 12th experiment. This may be due

to the peak machining voltage, moderate machining current and moderate duty

cycle. The higher machining voltage leads to micro sparks and thus greater DD.

The maximum MRR achieved in 8th combination gives a moderate DD of 20

microns. This is due to 2nd level of machining voltage and duty cycle combined

with 1st level of Frequency. Although the electrolyte concentration is higher

(3rd level), the DD obtained is at moderate level since all other process

parameters are at moderate level, particularly the machining voltage.

The 11th combination of process parameters (EC1V2C1DC1F2)

resulted in least MRR and DD. In this combination, the major contributors for

MRR i.e. machining current and electrolyte concentration are at minmum level,

backed by the 33.33% duty cycle resulted in least MRR as well as DD.

5.6.2 Dimensional Deviation - SDSS

The comparison made between the MRR and DD obtained for SDSS

is represented in figure 5.31.

The DD obtained for SDSS is ranged from 20 to 39 microns for

a MRR range of 0.0008991 to 0017066.

140

Figure 5.31: MRR Vs Dimensional Deviation - SDSS

The maximum DD is recorded in the 9th combination as 39 microns.

This can be attributed to the 3rd level of machining voltage, 3rd level of

electrolyte concentration and 3rd level of duty cycle. A 3rd best MRR is produced

at this combination.

The maximum MRR is reported in 17th combination with 3rd level of

electrolyte concentration and duty cycle combined with 2nd level machining

voltage. The moderate voltage combined with minimum frequency resulted in a

moderate DD of 30 microns.

The minimum DD and least MRR are achieved with the

1st combination (EC1V1C1DC1F1). In this combination, since all the process

parameters are at minimum level.

141

5.6.3 Dimensional Deviation - Inconel 600

The comparison between MRR and DD made for Inconel 600 reveals

the following inferences. The chart plotted to this effect is given as figure 5.32.

Figure 5.32: MRR Vs Dimensional Deviation - Inconel 600

The maximum DD of 36 microns is resulted in 6th combination due

to 3rd level of machining voltage, 3rd level of machining current and moderate

level of electrolyte concentration. This combination of process parameters has

produced the 2nd best MRR of 0.00107 mm3/min.

The minimum DD of 10 microns is achieved in 1st combination.

The MRR reported in this combination is also lowest i.e. 0.0003606 mm3/min.

since all the process parameters are at their minimum level.

142

It can be inferred from the above comparative analysis between

MRR and DD for Nickel, SDSS, and Inconel 600 that the machining voltage

plays a major role in controlling the DD in the ECMM process. The most

influencing factors on MRR, machining current, electrolyte concentration,

machining voltage and duty cycle also affects the DD in a similar fashion.

However, based on the material, the contribution of each process parameter on

DD varies. A detailed study in this respect will be highly useful to fine tune

the process parameters for maximum MRR and minimum DD.

143

CHAPTER 6

SUMMARY AND CONCLUSIONS

6.1 SUMMARY

The experimental studies already made in the field of

electrochemical micro machining reveals the great potential of this method of

precision machining. However, it is learnt from the literature survey that many

researches were done involving only a few input / output process parameters

at a time. Further, the ECMM process is to be optimized specifically for each

material considering the MRR, dimensional deviation and cost.

Hence, this study is conducted on Nickel based alloys with five

processing parameters viz. Electrolyte Concentration(EC), Machining

Voltage(V), Machining Current(C), Duty Cycle(DC), and Frequency(F). In

order to achieve this objective, an experimental setup is designed and

fabricated consists of a) Work holding platform, b) Tool feeding device,

c) Control system, d) Electrolyte flow system, and e) Power supply system.

Preliminary experiments are conducted (one factor at a time

approach) to identify the levels of process parameters. To study the entire

spectrum of levels of process parameters with least number of experiments,

Taguchi Design methodology is used with L18 orthogonal array. The levels of

process parameters for Nickel are chosen as: electrolyte concentration of 0.1,

0.2 and 0.3 mol/lit, machining voltage of 3.5, 5.0, and 6.5 volts, machining

144

current of 0.1, 0.3, and 0.5 amps. For SDSS and Inconel 600 an electrolyte

concentration of 0.4, 0.45, and 0.5 mol/lit, machining voltage of 8, 9, and 10

volts, machining current of 0.6, 0.8, and 1.0 amps., are chosen as levels of

process parameters. Besides, the levels of duty cycle and frequency are kept

similar for Nickel, SDSS and Inconel 600 as 33.33, 50.00, 66.66% and 30, 40,

50 Hz. respectively.

The statistical analysis of variance (ANOVA) technique is used to

determine the contribution of each parameter towards maximum MRR. Based

on the ANOVA results, the confirmation experiments are conducted to ensure

the coherence of the experimental results with predicted values. Genetic

Algorithms are used to identify the optimized level of process parameters and

the same compared with the experimental results.

6.2 CONCLUSIONS

It is evident from this research work that the dominant process

parameter which affects MRR varies based on the Nickel content in the Nickel

Alloy. The 100% pure Nickel has shown high rate of dissolution for the higher

machining current (C). The Inconel 600 alloy, which has 72% Nickel content,

the duty cycle (DC) contributed for maximum MRR while machining current

become less significant. Further, the duty cycle (DC) was the major parameter

affecting the MRR of the SDSS alloy which has only 5 - 6% of Nickel content.

Hence, it can be inferred that higher the Nickel content, the machining current

is more significant factor affecting MRR and DD.

145

The following are the conclusions derived from the obtained results

for Nickel, SDSS, and Inconel 600. Further, the optimum combination of

process parameters to achieve maximum MRR obtained from this research is

also furnished.

6.2.1 Conclusion on ECMM of Nickel

o It is found from the ANOVA results that machining current,

electrolyte concentration and machining voltage have

significant effect on MRR. The predicted combination of

process parameter for maximum MRR is EC3V3C3DC3F1.

o The optimum combination of levels of process parameter for

maximum MRR is achieved from the 8th combination i.e.

EC3V2C3DC2F1. The maximum MRR obtained is 0.009577

mm3/min.

o A 95.56% of confidence level of experiments conducted is

achieved. Based on the S/N Ratio, an improvement of 27.5%

is achieved.

The predicted combination obtained from ANOVA has been

compared with the optimum combination obtained from GA (EC3V3C2DC3F1)

and found that both are matching exactly for all parameters except machining

current. It is inferred from the analysis of experimental results, for nickel, the

machining current is the dominating factor affecting MRR. The confirmation

experiments conducted using the combination obtained from ANOVA also

revealed the same with a maximum MRR of 0.009999 mm3/min.

146

Hence, the combination selected by ANOVA with 3rd level of

machining current i.e. EC3V3C3DC3F1 is recommended as the optimum level

of process parameter for Nickel.

6.2.2 Conclusion on ECMM of SDSS

o ANOVA results for SDSS revels that duty cycle, electrolyte

concentration and machining voltage have significant effect

on MRR. The predicted combination of process parameter for

maximum MRR is EC3V2C1DC3F1.




mm3/min.

o A 94.57% of confidence level of experiments conducted is

achieved. Based on the S/N Ratio, an improvement of 41.8%

is achieved.



and found that both are matching for the duty cycle and the machining current.

Since the combination of 17th experiment which resulted in maximum MRR

exactly matches with the combination obtained from ANOVA with an MRR

of 0.017066 mm3/min.

Hence, the combination selected by ANOVA with EC3V3C3DC3F1

is recommended as the optimum level of process parameter for SDSS.

147

6.2.3 Conclusion on ECMM of Inconel 600

o Results obtained from ANOVA for Inconel 600 exhibits that

duty cycle, electrolyte concentration, and machining voltage

have significant effect on MRR. The predicted combination of

process parameter for maximum MRR is EC2V3C3DC3F1.




mm3/min.

o Based on the S/N Ratio, an improvement of 22.6% is achieved.



and found that both are matching for the duty cycle, electrolyte concentration

and the machining voltage. It is inferred from the analysis of experimental

results, for Inconel 600, the duty cycle is the dominating factor affecting

MRR. The confirmation experiments conducted using the combination

obtained from ANOVA also revealed the same with a maximum MRR of

0.001293 mm3/min.

Hence, the combination selected by ANOVA with a combination

EC2V3C3DC3F1 yielded a maximum MRR of 0.001306 mm3/min is

recommended as the optimum level of process parameter for Inconel 600.

148

6.3 SUGGESTIONS FOR FUTURE WORK

This thorough investigation to optimize the ECMM machining

parameters for Nickel and its alloys paves way for further studies. The

following suggestions may prove useful for future research work:

1. The effects of machining parameters on dimensional deviation may be investigated with variation in electrolyte flow rate, dynamic IEG, tool feed, tool shape, and tool vibrating frequency.

2. The significance to be assigned to MRR, dimensional deviation, surface roughness in multi objective optimization models to meet the growing requirements.

3. Development of revolving micro-helical tool with tool vibration to improve accuracy and MRR.

4. Further research is required to device robust methods and equipments to monitor the inter electrode gap.

5. More research is recommended to accurately monitor the purity, temperature and velocity of electrolyte at IEG.

6. Efforts may be made to investigate the effects of the ECMM process parameters on performance measures in a Cryogenic environment.

7. More research is to be done to prevent over voltage and optimize the total energy used for ECMM.

8. Further research is to be done to study the possibility of gang drilling for high quality mass production.

9. Further research is recommended to exploit the ECMM capabilities in machining Composite materials and Powder metallurgical components.

149

APPENDIX 1

Table A 1.1: Experimental Results for MRR - SDSS

Exp. No.Machining

Time in Trial 1


MRRTrial 1

MRRTrial 2

Average MRR

1 19.00 25.00 0.000799 0.001020 0.000909

2 23.20 18.80 0.002289 0.001845 0.002067

3 19.70 28.30 0.002342 0.002882 0.002612

4 8.60 11.80 0.002844 0.002311 0.002577

5 14.80 11.20 0.004133 0.004987 0.004560

6 13.50 15.50 0.002304 0.002836 0.002570

7 22.70 29.30 0.000790 0.001009 0.000899

8 9.00 13.00 0.006241 0.005214 0.005728

9 8.20 10.80 0.006900 0.008181 0.007540

10 12.65 9.65 0.001825 0.002265 0.002045

11 29.40 19.20 0.001684 0.002096 0.001890

12 11.20 14.80 0.003148 0.002567 0.002858

13 11.55 14.75 0.004733 0.003915 0.004324

14 23.00 17.60 0.001602 0.001997 0.001799

15 10.40 14.00 0.004201 0.005067 0.004634

16 9.95 12.35 0.010049 0.008538 0.009294

17 9.10 7.90 0.018273 0.015859 0.017066

18 12.00 16.00 0.002292 0.002822 0.002557

150

Table A 1.2: Experimental Results for MRR - Inconel 600

Exp. No.Machining

Time in Trial 1


MRRTrial 1

MRRTrial 2

Average MRR

1 30.5 34.5 0.0003110 0.0004100 0.0003607

2 24.1 22.9 0.0007271 0.0009320 0.0008293

3 16.2 13.8 0.0011595 0.0009121 0.0010358

4 28.2 31.8 0.0005787 0.0007470 0.0006630

5 24.7 19.3 0.0008684 0.0006761 0.0007723

6 17.4 20.6 0.0011949 0.0009411 0.0010680

7 33.3 29.7 0.0004177 0.0003168 0.0003672

8 31.1 20.9 0.0003497 0.0004600 0.0004046

9 18.5 21.5 0.0008250 0.0006412 0.0007331

10 20.7 23.3 0.0008591 0.0006686 0.0007639

11 31 27 0.0006831 0.0005273 0.0006052

12 28.3 21.7 0.0006270 0.0008070 0.0007172

13 19 21 0.0009436 0.0007369 0.0008402

14 27.2 30.8 0.0006977 0.0005389 0.0006183

15 25.1 21.9 0.0008492 0.0006607 0.0007549

16 31.8 23.2 0.0006822 0.0005266 0.0006044

17 10.8 15.2 0.0011433 0.0014420 0.0012926

18 23 29 0.0003497 0.0004600 0.0004046

151

REFERENCES

1. Bhattacharyya .B and Munda .J, “Experimental investigation on the influence of electrochemical machining parameters on machining rate and accuracy in micromachining domain”, International Journal of Machine Tools & Manufacture, Vol.43, pp.1301-1310, 2003.

2. Bhattacharyya .B, Doloi B, and Sridhar .P.S, "Electrochemical micro-machining: New possibilities for micro-manufacturing", Journal of Materials Processing Technology, Vol.113, pp.301-305, 2001.

3. Bhattacharyya .B, Malapati .M, Munda .J, and Sarkar .A, "Influence of tool vibration on machining performance in electrochemical micro-machining of copper", International Journal of Machine Tool and manufacture, Vol.47, pp.335-342, 2007.

4. Bhattacharyya .B, Mitra .S, and Boro .A, "Electrochemical machining : New possibilities for micromachining", Robotics and Computer Integrated manufacturing, Vol.18, pp.283-289, 2002.

5. Bhattacharyya .B, Munda .J, and Malapati .M, “Advancement in electrochemical micro machining”, International Journal of Machine Tools & Manufacture, Vol.44, pp.1577-1589, 2004.

6. Bhattacharyya .B, and Munda .J, "Experimental investigation into electrochemical micromachining (EMM) process", Journal of Materials Processing Technology, Vol.140, pp.287-291, 2003.

7. Clifton .D, Mountb .A.R , Aldera .G.M, and Jardinea .D, "Ultrasonic measurement of the inter-electrode gap in electrochemical machining", International Journal of Machine Tools and Manufacture, Vol.42, pp.1259-1267, 2002.

8. Datta .M, Derek Harris, "Electrochemical micromachining: An environmentally friendly, high speed processing technology", Electrochimica Acta, Vol.42, pp.3007-3013, 1997.

9. Datta .M, “Micro fabrication by electrochemical metal removal, IBM Journal of Research and Development, Vol.42, No.5, pp. 655–669, 1998.

152

10. Dayanand .S.B, Jain .V.K, Shekhar .R, and Anjali V. K, "Hole quality and inter electrode gap dynamics during pulse current electrochemical deep hole drilling", International Journal of Advanced Manufacturing Technology, Vol.34, pp.79-95, 2007.

11. De Silva .A.K.M and McGeough .J.A, "Process monitoring of electrochemical micromachining", Journal of Materials Processing Technology, Vol.76, pp.165-169, 1998.

12. Hai-Ping T, Jung-Chou H, Jyun-Cin Y, and Biing-Hwa Y, “Improvement of Electrochemical Microdrilling Accuracy Using Helical Tool”, Materials and Manufacturing Processes, Vol.23 (5), pp.499-505, 2008.

13. Hari .S, Pradeep .K, "Optimizing feed force for turned parts through the Taguchi technique", Sadhana, Vol.31, pp.671-681, 2006.

14. Hocheng .H, Sun .Y.H, Lin .S.C, and Kao .P.S, "A material removal analysis of electrochemical machining using flat-end-cathode", Journal of Materials Processing Technology, Vol.140, pp.264-268, 2003.

15. Sing .S.H, Ruxton .T, and Wang .J, "Taguchi concepts and their applications in marine and offshoresafety studies", Journal of Engineering Design, Vol.12, pp.331-358, 2001.

16. Jain .N.K and Jain .V.K, “Optimization of electro-chemical machining process parameters using genetic algorithms”, Machining Science and Technology: An International Journal, Vol.11, pp.235-258, 2007.

17. Kozak .J, Rajurkar .K.P, and Makkarb .Y, "Selected problems of microelectrochemical machining", Journal of Materials Processing Technology, Vol.149, pp.426-431, 2004.

18. Zhou .J, Pang .G, and Xu .W, “Research on pulse electrochemical finishing using a moving cathode”, International Journal of Manufacturing Technology and Management, Vol.7, pp.352-365, 2005.

19. Da Silva Neto . C.J, Da Silva .M.E, and Da Silva .B.M, "Intervening variables in electrochemical machining", Journal of Materials Processing Technology, Vol.179, pp.92-96, 2006.

20. Kim, B. H, Ryu, S. H, Choi, D.K, and Chu .C. N, "Micro Electrochemical Milling," Journal of Micromechanics and Microengineering, Vol. 15, pp. 124-129, 2005.

153

21. Kock .M, Kirchner .V, and Schuster .R, “Electrochemical micromachining with ultrashort voltage pulses–a versatile method with lithographical precision”, Electrochimica Acta, Vol.48, pp.3213-3219, 2003.

22. Kurita .T, Chikamori .K, Kubota .S, and Hattori .M, "A study of three-dimensional shape machining with an ECµM System", International Journal of Machine Tools & Manufacture, Vol.46, pp.1311-1318, 2006.

23. Landolt .D, Chauvy .P.F, and Zinger .O, “Electrochemical micromachining, polishing and surface structuring of metals: fundamental aspects and new developments”, Electrochimica Acta, Vol.48, pp.3185-3201, 2003.

24. Cagnon .L, Kirchner .V, Kock .M, and Schuster .R, "Electrochemical Micromachining of Stainless Steel by Ultrashort Voltage Pulses", Z. Phys. Chem., Vol.217, pp.299-313, 2003.

25. Lee E.S, Baek .Y.S, and Cho .R.E, “A study of the characteristics for electrochemical micromachining with ultrashort voltage pulses”,The International Journal of Advanced Manufacturing Technology, Vol.31, pp.762-769, 2007.

26. Lee .S.J, Lai .J.J, Lee .Y.M, and Lee .Y.C,"Improving Machining Accuracy of the EMM Process through Multi-physics Analysis", Key Engineering Materials, Vol.364-366, pp.290-296, 2008.

27. Masuzava .T, “State of the art of micromachining”, Annals of the CIRP, Vol.49 (2), pp.473-487, 2000.

28. Mithu M.A.H, Fantoni .G, and Ciampi .J, “A step towards the in-process monitoring for electrochemical microdrilling”, The International Journal of Advanced Manufacturing Technology, Vol.57, pp.969-982, 2011.

29. Sen .M and Shan .H.S, “A review of electrochemical macro to micro-hole drilling processes”, International Journal of Machine Tools & Manufacture, Vol.45, pp.137-152, 2005.

30. Mukherjee .S.K, Kumar .S, and Srivastava .P.K, "Effect of over voltage on Material Removal Rate during Electrochemical machining", Journal of Science and Engineering, Vol.8, pp.23-28, 2005.

154

31. Munda .J and Bhattacharyya .B, "Investigation into electrochemical micromachining (EMM) through response surface methodology based approach", International Journal for Advanced Manufacturing Technology, Vol.35, pp.821-832, 2008.

32. Munda .J, Malapati .M, and Bhattacharyya .B, “Control of micro spark and stray current effect during EMM process”, Journal of Material Processing Technology Vol.194, pp.151-158, 2007.

33. Munda .J, Malapati .M, and Bhattacharyya .B “Investigation into the influence of Electrochemical Micromachining (EMM) parameters on Radial Overcut through RSM-based approach”, International Journal of Manufacturing Technology and Management, Vol.21, pp.54-66, 2010.

34. Park .J.W, Lee .E.S, Won .C.H, Moon .Y.H, "Development of micro machining for air-lubricated hydrodynamic bearings", Microsystem Technologies, Vol.9, pp.61-66, 2002.

35. Rajurkar .K.P, Levy .G, Malshe .A, Sundaram .M.M, McGeough .J, Hu .X, Resnick .R, DeSilva .A, "Micro and nano machining by electro-physical and chemical processes", Annals of the CIRP, Vol.55, pp.643-666, 2006.

36. Se .H.A, Shi .H.R, Deok .K.C.B, and Chong .N.C, "Electro-chemical micro drilling using ultra short pulses", Precision Engineering, Vol.28, pp.129-134, 2004.

37. Senthilkumar .C, Ganesan .G and Karthikeyan .R, "Parametric optimization of electrochemical machining of Al/15% SiCp composites using NSGA-II" Nonferrous Met. Soc. China 21(2011) 2294 2300, Vol.21, pp.2294–2300, 2011.

38. Shi .H.R, "Micro fabrication by electrochemical process in citric acid electrolyte", Journal of Material Processing Technology, Vol.209, pp.2831-2837, 2009.

39. Thanigaivelan .R and Arunachalam .R.M, “Experimental study of overcut in electrochemical micromachining of 304 stainless steel”, Transactions of NAMRI/SME, Vol.38, pp.253-260, 2010.

40. Thanigaivelan .R and Arunachalam .R.M, “Experimental study on the influence of tool electrode tip shape on Electrochemical Micromachining of 304 stainless steel”, Materials & Manufacturing Processes, Vol.25, pp.1181-1185, 2010.

155

41. Thanigaivelan .R and Arunachalam .R.M, “Study of dominant variables in Electrochemical Micromachining”, Manufacturing Technology Today, Vol. 9, pp.22-28, 2010.

42. Yong .L, Yunfei .Z, and Guang .Y, "Localized electrochemical Micromachining with gap control", Sensors and Actuators, Vol.108, pp.144-148, 2003.

43. Zhang .Z, Zhu D, "Experimental research on the localized electrochemical micro-machining", Russian Journal of Electrochemicstry, Vol.44, pp.926-930, 2008.

44. Zhang .Z, Zhu .D, Qu .N, and Wang .M, "Theoretical and Experimental investigation on electrochemical micro machining", Microsyst Technol. Vol.13, pp.607-612, 2007.

45. Zhiyong .L, “Experimental investigation of micro-holes in electrochemical machining using pulse current”, 3rd IEEE International Conference, pp.151-154, 2008.

46. Goldberg .D.E, "Genetic Algorithms in Search optimization and Machine Learning", Addison - Wesley, 2000.

47. Kalyanmoy .K, "Optimization for Engineering Design - Algorithms and Examples", Prentice - Hall of India Private Limited, New Delhi, 1995.

48. Phillip J.R, “Taguchi Techniques for Quality Engineering”, Mcgraw-Hill, New York, 1988.

49. Ranjit K.R, “A Primer on the Taguchi Method”, Society of Manufacturing Engineers, 1990.

50. Thomas .B, “Quality By Experimental Design”, 3rd Edition (Quality and Reliability), Chapman and Hall/CRC, London, 2005.

51. Wysk .R.A, Niebel .B.W, Cohen .P.H, and Simpson .T.W, “Manufacturing Processes: Integrated Product and Process Design”, McGraw Hill, New York, 2000.

52. http://electrochem.cwru.edu/encycl/art-m03-machining.htm

53. http://electrochem.cwru.edu/estir/inet.htm

54. http://www.tsijournals.com/rrechem/RRECHEMTOC.asp

55. http://www.unl.edu/

56. http://www.minitab.com

156

LIST OF PUBLICATIONS

1. Jerald J, Saravanan .D, Arul Arasu.M and Arunachalam.R.M, “Experimental Investigation of Micro Electro Chemical Machining on Nickel”, International Journal of Production Technology and Management Research, Vol.1, pp.67-74, 2010.

2. Saravanan .D, Arularasu M and Ganesan K, “A study on Electro Chemical Micro Machining of SDSS for biomedical filters”, Journal of Engineering and Applied Science Vol 7, No 5, pp.517-523, 2012.

3. Saravanan .D, K.Ganesan, M.Arularasu, “Optimization of Machining Parameters in Electrochemical Micro Machining of Nickel”, International Conference on Advances in Construction Manufacturing and Automation Research, pp.63, 2012.

4. Saravanan .D, K.Ganesan, M.Arularasu, “Optimization of Machining Parameters in Electrochemical Micro Machining of Super Duplex Stainless Steel”, Indian Technology Congress 2012, Ref.TP/ITC/2012-13/095, 2012.

157

CURRICULUM VITAE

D. SARAVANAN

Serving as Principal for Sri Ramakrishna College of Engineering,

Perambalur. Served in various Institutions: 1) Professor Mechanical Engg., for

Sri Ranganathar Institute of Engineering and Technology Coimbatore

(June 2011-June 2012). 2) Professor Mechanical Engg., for Jayaram College

of Engineering and Technology, Thuraiyur (August 2010-May 2011). 3)

Principal for MIT, Musiri (June 2009-August 2010). 4) Prof. & Head, Roever

Engineering College, Perambalur. (June 2001-May 2009). 5) Head of

Department Mechanical Engg. at Roever Polytechnic College, Perambalur.

(June 1986-June 2001) and also served as Vice-Principal.

Have completed Professional Degree in Mechanical Engineering

(B.E.) at Regional Engineering College (Presently NIT), Trichy and Post

Graduate Degree- M.E., Thermal Plant Engg at Shanmuga College of

Engineering (Presently SASTRA), Thanjavur.

Published two Papers in International Journals, two papers in

International Conference, Organized one FDP and attended three FDPs,

Organized one National Conference and attended one National Conference,

Attended two International Conferences.

experimental investigations of electro chemical micro...

Documents