afiq_thesis

0

THE UNIVERSITY OF NEW SOUTH WALES

SCHOOL OF MECHANICAL

AND MANUFACTURING ENGINEERING

Monitoring Abrasive Water Jet (AWJ) Machining Process of Titanium

Alloy using Acoustic Emission

Muhammad Afiq Kamaruddin

3266366

Bachelor of Manufacturing Engineering and Management

May 2012

Prof Jun Wang

Dr Huaizhong Li

i

Declaration of Originality

I hereby declare that this submission is purely of my own individual work and effort. It

was done to the best of my knowledge and belief. It contains no material previously

published or written by another person, nor material which to a substantial extent has

been accepted for the award of any other degree or diploma at UNSW or any other

educational institution, except where due acknowledgment have been made in the text.

I also declare that all intellectual items in this thesis are products of my own work

except the assistance from others in operating the water jet machine or presentation and

linguistic expression in this thesis which are acknowledged.

May 2012

_______________________

Kamaruddin, Muhammad

ii

Abstract

Abrasive Water Jet (AWJ) machining is a niche technology with outstanding

capabilities. For machining difficult-to-machine materials, this method provides

alternatives to other conventional machining method and has proven to be superior in

some aspects of machining such as the absence of the heat-affected zone, no thermal

distortion, and high machining versatility. On-going extensive studies are performed to

improve the performance of AWJ machining. In AWJ machining, offline monitoring

method is often adopted. However, it provides less flexibility to the process especially

to effectively control the depth of cut. To interrupt the drilling process for depth of cut

monitoring is not feasible. Indirect monitoring technique is therefore required.

Literature review indicates there has been little work done in monitoring AWJ

machining process on-line. This justifies the need to develop an online monitoring

strategy to monitor the depth of cut in AWJ machining. In this paper, an Acoustic

Emission (AE) technique is used and found to be one of the promising monitoring tools.

The thesis is aimed to investigate the feasibility of the root mean square of acoustic

emission (AErms) in monitoring the drilling and traverse cut processes. The signal trend

will help to understand the material removal process from on-line monitoring point of

view. The relationship between AErms and the depth of cut will be established. The

AErms is also used to detect any anomalous events that occur during the process. This

will facilitate to fast problem rectification that will save cost and time during

production, thus improving the production efficiency. The experiment is performed on

the commercially used Titanium Alloy (Ti-4Al-6V). Effects of selected parameters on

the cutting profile are also investigated.

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Acknowledgement

Foremost recognition deservedly goes to my main supervisor Dr Huaizhong Li for his

valuable assistance, critics, supports and suggestions in completing my thesis. I am

profoundly grateful for his effort in arranging an official fortnight meeting in which all

queries pertaining to my work were discussed and resolved. His encouragement had

helped me in all the time to improve myself in the research and writing of this thesis.

I would like also to express my utmost gratitude and appreciation to my co supervisor,

Prof Jun Wang for guiding and facilitating me throughout my thesis until it is

completed.

I have furthermore to thank especially Mr Seetha Mahadevan and Mr Martin Smith for

their kindness and generous help to improve my work, and permit me to use the

Advance Manufacturing Lab, particularly in using the abrasive water jet machine with

the assistance of Mr Mirza Ali and Mr Keivan, both are postgraduate students whom I

thanked to.

Thank you to all my friends, especially Mr Raja Taufika for assisting me in the signal

processing stage. Appreciation also to my course mates especially those who are

involved in the water jet research group for the help and support.

Finally, I would like to give a special thanks to my beloved family for their timeless,

never ending love and support both financially and emotionally throughout my research

work and writing of this thesis.

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List of Abbreviations

AWJ Abrasive Water Jet

AE Acoustic Emission

AErms Acoustic Emission Root Mean Square

Ti-4Al-6V Titanium Alloy with 6% Aluminium, 4% Vanadium

NDT Non-Destructive Testing

HSM High Speed Machining

HEA Heat Affected Zone

CNC Computer Numerical Control

RMS Root Mean Square

PSD Power Spectral Density

ARMA Auto Regressive Moving Average

FFT Fast Fourier Transform

UNSW the University of New South Wales

DAQ Data Acquisition

ASTM American Society for Testing and Materials

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Nomenclature

Q Volume of material removed

Mp, mp Abrasive particle mass

Va Abrasive particle velocity

Vw Water jet velocity

α Particle attack angle

σ Target material flow stress

Ψ Ratio of vertical horizontal force

Qdw Volume of material removed by deformation wear

Ԑd Deformation wear factor

Vc Critical particle velocity to remove material

Qcw Volume of material removed by cutting wear

Ԑc Cutting wear factor

C Empirical parameter

R Particle size (radius)

m Weibull constant

k1 constant

pp Abrasive particle density

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Kc Critical stress intensity factor

H Hardness of target material

C1, C2 Constants determined by AErms and depth of cut

EDISS Energy dissipated

EA Input Energy

J1, J2, J3 Constants determined by EDISS and depth of cut

I Acoustic wave intensity

p Wave pressure

v Wave particle velocity

r Source distance

Pw Pump pressure

Vt Traverse speed

Td Drilling time

Tc Traverse cutting time

Hd Drilling depth

Dh Hole diameter

Htc Average depth of cut for traverse cut

fg Gross frequency

fa Sampling frequency

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a,k,j,b,d Constants

Ep Abrasive particle kinetic energy

�w Water density

φ Momentum transfer coefficient

ṁA Abrasive mass flow rate

ṁW Water mass flow rate

te Local exposure time

Dt Nozzle focus diameter

Yd AErms value for drilling

Yt AErms value for traverse cut

f,g Rate of rupture

C1, C2 Time shift

CL Cutting length

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List of Tables

4.1 List of independent parameters in drilling experiment ....................................... 57

4.2 List of independent parameters in traverse cut experiment ................................ 59

4.3 List of constant parameters ................................................................................. 61

4.4 List of material properties for titanium alloy ...................................................... 62

4.5 List of data acquisition specifications ................................................................. 63

5.1 Measurement for drilling results ......................................................................... 70

5.2 Value for constants in drilling equation .............................................................. 78

5.3 Value for constants in traverse cut equation ....................................................... 92

6.1 Drilling depth and AErms measurement results ................................................. 112

6.2 Value for constants in AErms drilling equation .................................................. 115

6.3 Depth of cut measurements ............................................................................... 123

6.4 Average depth of cut and AErms measurement results ...................................... 129

6.5 Value for constants in AErms traverse cut equation ........................................... 133

9.1 Summarized equations for AErms drilling and traverse cut ............................... 156

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List of Figures

1.1 UNSW abrasive water jet machine ....................................................................... 9

1.2 UNSW pump intensifier system ........................................................................ 11

1.3 UNSW abrasive delivery system ........................................................................ 12

1.4 UNSW water jet nozzle ...................................................................................... 13

1.5 ABB Robot mounted water jet ............................................................................ 14

1.6 Variants of energy absorbent .............................................................................. 15

1.7 Single particle micro cutting mechanism ............................................................ 17

1.8 Lateral crack mechanism..................................................................................... 20

1.9 Three stages cutting process................................................................................ 21

2.1 Acoustic emission generation and acquisition .................................................... 23

2.2 Typical AE signal ................................................................................................ 24

2.3 AE parameters ..................................................................................................... 26

2.4 KISTLER acoustic emission sensor .................................................................... 27

3.1 General time domain AE signals for different process stages ............................ 31

3.2 Relationship between AErms and drilling depth .................................................. 32

3.3 AE signal correspond to the depth of cut ............................................................ 34

3.4 FFT peak amplitude and frequency with the depth of cut .................................. 35

3.5 AWJ vertical cutting force signal at different pressures ..................................... 39

3.6 Variation of the AE band denergy with time at 100 MPa ................................... 40

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3.7 Frequency power spectrum of the traverse cut process ...................................... 40

3.8 Cutting force and AErms with regards to the depth of cut ................................... 41

3.9 Location of the AE sensor ................................................................................... 45

3.10 AErms signal in various process conditions .......................................................... 46

3.11 AErms in anomalous events .................................................................................. 47

4.1 Schematic diagram for experiment setup ............................................................ 51

4.2 Initial experiment setup ...................................................................................... 52

4.3 Initial AErms signal for drilling test ..................................................................... 53

4.4 New experiment set up ....................................................................................... 55

4.5 New AErms signal for drilling test ....................................................................... 56

4.6 Movement of nozzle for drilling experiment ...................................................... 58

4.7 Movement of nozzle for traverse cut experiment ............................................... 60

4.8 VERTEX 420 profile machine ............................................................................ 64

4.9 Mitutoyo digital calliper ...................................................................................... 65

5.1 Drilling result ..................................................................................................... 68

5.2 Drilling profile in various drilling time ............................................................... 69

5.3 Hole diameter versus drilling time (200 MPa) .................................................... 71

5.4 Hole diameter versus drilling time (250 MPa) ................................................... 71



5.7 Hole diameter versus drilling time in all pressures ............................................ 73

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5.8 Hole formation in AWJ drilling .......................................................................... 74

5.9 Drilling depth versus drilling time (200 MPa) .................................................... 76




5.13 Drilling depth versus drilling time in all pressures ............................................. 78

5.14 Threshold pressure for drilling ............................................................................ 79

5.15 Traverse cut result ............................................................................................... 81

5.16 Kerf profile for selected traverse speed .............................................................. 82

5.17 Depth of cut versus cut-off distance (200 MPa) ................................................. 84




5.21 Kerf bottom profile ............................................................................................. 86

5.22 Kerf profile for different traverse speed under constant pressure ....................... 87

5.23 Average depth of cut versus traverse speed (200 MPa) ..................................... 89

5.24 Average depth of cut versus traverse speed (250 MPa) ...................................... 89



5.27 Average depth of cut versus traverse speed in all pressures ............................... 91

5.28 Threshold pressure for traverse cut ..................................................................... 93

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6.1 Raw AE signal for drilling and traverse cut ........................................................ 98

6.2 AErms general pattern in drilling .......................................................................... 99

6.3 AErms in various drilling time ............................................................................ 101

6.4 Effects of drilling time on AErms ....................................................................... 102

6.5 Effects of pressure on AErms ............................................................................. 103

6.6 Drilling point and traverse cut length ............................................................... 104

6.7 Graph magnification at AErms maximum amplitude ......................................... 105

6.8 Time to rise for each drilling operation............................................................. 106

6.9 Effective drilling AErms in various cutting time for 200 MPa .......................... 109

6.10 AErms versus drilling depth (200 MPa) ........................................................... 113

6.11 AErms versus drilling depth (250MPa) .............................................................. 113

6.12 AErms versus drilling depth (300MPa) .............................................................. 114

6.13 AErms versus drilling depth (350 MPa) ............................................................. 114

6.14 AErms versus drilling depth in all pressures ...................................................... 115

6.15 AErms general pattern in traverse cut experiment .............................................. 116

6.16 Effects of traverse speed on AErms .................................................................... 116

6.17 AErms versus cutting time .................................................................................. 117

6.18 Three stages of cutting represented by AErms ................................................... 118

6.19 Effects of pressure on AErms ............................................................................. 120

6.20 AErms versus cutting length ............................................................................... 122

6.21 Effective AErms versus cutting time at 2 mm/s ................................................. 126

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6.22 Effective AErms versus cutting time at 10 mm/s ............................................... 128

6.23 AErms versus depth of cut (200 MPa) ................................................................ 130




6.27 AErms versus depth of cut in all pressures ......................................................... 132

7.1 AErms in through cut for drilling........................................................................ 135

7.2 Through cut drilling point and traverse length .................................................. 136

7.3 Hole profile for through cut .............................................................................. 137

7.4 Effective AErms versus drilling time for through cut ......................................... 138

7.5 AErms in through cut for traverse cut ................................................................. 140

7.6 AErms for drilling anomalies .............................................................................. 142

7.7 AErms for traverse cut anomalies ....................................................................... 143

7.8 Through cut profile ........................................................................................... 144

8.1 Hooke’s law for ductile material ....................................................................... 146

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Table of Contents

Declaration of originality ................................................................................................. i

Abstract ............................................................................................................................ ii

Acknowledgement .......................................................................................................... iii

List of abbreviations ...................................................................................................... iv

Nomenclature................................................................................................................... v

List of tables .................................................................................................................. viii

List of figures .................................................................................................................. ix

CHAPTER 1 - Introduction ........................................................................................... 1

1.1 Background ..................................................................................................... 2

1.2 Project description ........................................................................................... 3

1.3 Thesis objectives ............................................................................................. 3

1.4 Thesis outline .................................................................................................. 4

CHAPTER 2 – Literature Review ................................................................................. 6

2.1 Titanium Alloy ................................................................................................ 7

2.1.1 General criteria for cutting tools ...................................................... 7

2.1.2 Limitations in conventional machining of Titanium Alloy.............. 8

2.2 Non-Conventional machining process ............................................................ 9

2.3 Abrasive Water Jet (AWJ) system ................................................................ 10

2.3.1 Pump intensifier ............................................................................. 11

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2.3.2 The abrasive delivery system ......................................................... 12

2.3.3 The cutting head ............................................................................. 13

2.3.4 AWJ control system ....................................................................... 14

2.3.5 Catcher ........................................................................................... 14

2.4 Advantages in AWJ machining ..................................................................... 15

2.5 Limitations in AWJ machining ..................................................................... 16

2.6 Erosion mechanism for ductile materials ...................................................... 17

2.6.1 Micro cutting .................................................................................. 17

2.6.2 Cutting-deformation ....................................................................... 18

2.7 Erosion mechanisms for brittle materials ...................................................... 19

2.7.1 Conical Crack ................................................................................. 19

2.7.2 Lateral Crack .................................................................................. 20

2.8 Cutting stages processes ................................................................................ 21

2.9 Machine process monitoring ......................................................................... 22

2.9.1 Acoustic Emission as monitoring method ..................................... 23

2.9.2 Acoustic Emission sources ............................................................. 24

2.9.3 Acoustic Emission signal and parameters ...................................... 24

2.9.4 Acoustic Emission sensors ............................................................. 26

2.9.5 Acoustic Emission source location techniques .............................. 27

2.10 Acoustic Emission Root Mean Square (AErms) value ................................. 28

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CHAPTER 3 – Development of AE monitoring in water jet industry ..................... 30

3.1 Monitoring AWJ machining with Acoustic Emission method ..................... 31

3.2 The needs for on-line monitoring .................................................................. 39

3.3 The development for on-line monitoring depth of cut .................................. 37

3.3.1 Vertical cutting force monitoring ................................................... 39

3.3.2 Acoustic Emission monitoring ....................................................... 40

3.4 Acoustic Emission to detect energy dissipation in AWJ .............................. 43

3.5 Acoustic Emission in AWJ machine condition monitoring .......................... 45

3.6 Summary of literature review........................................................................ 48

CHAPTER 4 – Experiment Procedure ....................................................................... 57

4.1 Experiment setup ........................................................................................... 51

4.1.1 Initial experiment setup .................................................................. 52

4.1.2 Trial test ......................................................................................... 53

4.1.3 New experiment setup .................................................................... 55

4.2 Experiment procedure .................................................................................. 57

4.2.1 Drilling experiment ........................................................................ 57

4.2.2 Traverse cut experiment ................................................................. 59

4.2.3 Constant experiment parameters ................................................... 61

4.2.4 Material properties ......................................................................... 62

4.3 Methods to measure physical quantities ....................................................... 62

4.3.1 AErms value ..................................................................................... 63

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4.3.2 Drilling hole diameter .................................................................... 63

4.3.3 Drilling depth ................................................................................. 64

4.3.4 Traverse cut depth of cut ................................................................ 64

4.4 Problems encountered during the experiment ............................................... 65

CHAPTER 5 – Results and Discussion on Cutting Experiments ............................. 66

5.1 Drilling result ................................................................................................ 68

5.1.1 Drilling profile ............................................................................... 69

5.1.2 Effects of drilling time on hole diameter ....................................... 71

5.1.3 Effects of drilling time on drilling depth ...................................... 76

5.1.4 Threshold pressure for drilling experiment .................................... 79

5.2 Traverse cut result ......................................................................................... 81

5.2.1 Kerf profile ..................................................................................... 82

5.2.2 Relationship between cut-off distance and depth of cut ................ 84

5.2.3 Effects of traverse speed on the depth of cut ................................. 89

5.2.4 Threshold pressure for traverse cut ................................................ 93

5.3 Discussion ..................................................................................................... 94

5.3.1 Effects of pressure on drilling depth and depth of cut .................. 94

5.3.2 Effects of traverse speed on depth of cut ....................................... 95

5.3.3 Effects of drilling time on drilling depth ....................................... 96

5.3.4 Effects of drilling time on hole diameter ....................................... 96

xviii

CHAPTER 6 – Results on Acoustic Emission Monitoring ........................................ 97

6.1 Acoustic Emission signal for drilling and traverse cut ................................. 98

6.2 Acoustic Emission monitoring for drilling ................................................... 99

6.2.1 Effects of drilling time on AErms signal ....................................... 101

6.2.2 Effects of pressure on AErms signal .............................................. 103

6.2.3 AErms rise time.............................................................................. 104

6.3 Effective AErms for drilling .......................................................................... 107

6.3.1 Effects of drilling time on effective AErms ................................... 108

6.3.2 Modified MATLAB equations ..................................................... 110

6.4 Effects of drilling depth on AErms ............................................................... 113

6.5 Acoustic Emission monitoring for traverse cut ........................................... 116

6.5.1 Effects of traverse speed on AErms ............................................... 116

6.5.2 AErms to identify thress stages cutting process ............................. 118

6.5.3 Effects of pressure on AErms ...................................................... 120

6.6 AErms with respect to the cutting length ...................................................... 121

6.7 Effective AErms for traverse cut................................................................... 124

6.7.1 Effective AErms in various pressures .......................................... 125

6.8 Effects of depth of cut on AErms .................................................................. 130

CHAPTER 7 - AErms in Through Cut and Process Anomalies ............................... 134

7.1 Drilling ........................................................................................................ 135

7.1.1 Drilling observation ..................................................................... 139

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7.2 Traverse cut ................................................................................................. 140

7.2.1 Traverse cut observation .............................................................. 141

7.3 AErms for machine condition monitoring .................................................... 141

7.3.1 Drilling anomalies ........................................................................ 142

7.3.2 Traverse cut anomalies ................................................................. 143

CHAPTER 8 – Discussion on AErms monitoring ...................................................... 145

8.1 The relationship between AErms and the depth of cut.................................. 146

8.2 AErmsin the drilling process ......................................................................... 149

8.2.1 Effect of drilling parameters on AErms ......................................... 149

8.2.1.1 Constant pressure, various drilling time ....................... 149

8.2.1.2 Constant drilling time, various pressures ...................... 149

8.3 Traverse cut process .................................................................................... 150

8.3.1 Effect of traverse cut parameters on AErms .................................. 151

8.3.1.1 Constant traverse speed, various pressures ................... 151

8.3.1.2 Constant pressure, various traverse speed ..................... 151

8.3.2 Relationship between AErms and cutting length .......................... 151

8.4 Anomalies process ...................................................................................... 152

CHAPTER 9 – Conclusion and Future Work .......................................................... 153

9.1 Depth of cut profile ..................................................................................... 154

9.1.1 Drilling ......................................................................................... 154

9.1.2 Traverse cut .................................................................................. 155

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9.2 AErms Monitoring ........................................................................................ 155

9.2.1 Through cut .................................................................................. 156

9.2.2 Anomalous events ........................................................................ 157

9.3 Future work ................................................................................................. 157

References .................................................................................................................... 159

Appendix A : AE Sensor and Coupler Catalogue ........................................................ 165

Appendix B : Drilling and Traverse Cut Profile ........................................................... 166

Appendix C : Measurement Result ............................................................................... 169

Appendix D : MATLAB Equation for Process Parameters .......................................... 171

Appendix E : AErms Graph for Drilling ......................................................................... 174

Appendix F : Effective AErms Graph for Drilling ......................................................... 177

Appendix G : AErms Graph for Traverse Cut................................................................. 183

Appendix H : Effective AErmsGraph for Traverse Cut.................................................. 186

Appendix I : Effects of Pressure on AErms (Traverse Cut)............................................ 189

Appendix J : Effects of Pressure on AErms (Drilling) .................................................... 190

Appendix K : Relationship between AErms and Cutting Length ................................... 191

Appendix L : Effects of Traverse Speed on AErms ........................................................ 192

Appendix M : Effects of Drilling Time on AErms ......................................................... 193

1

Chapter 1: Introduction

2

1.1 Background

Acoustic emission technique has been widely used in various industries for monitoring

process. It is categorised as a Non-Destructive Testing (NDT). Some of the applications

involve detection of crack at the turbine blade, detection of corrosion in the reinforced

concrete structure and even used in the medical industry. Crack and fracture are formed

in the AWJ machining that is caused by hydro-abrasive erosion. The energy released in

the form of elastic wave is the source of acoustic emission and therefore making

acoustic emission to be one of the promising tools to monitor the depth of cut in AWJ

machining. It is difficult to control the depth of cut in AWJ machining due to the nature

of water that deflects and not rigid when penetrating the material as compared to

conventional cutting tools. Moreover, due to the rough nature exhibits in water jet

process, it is difficult to detect whether anomalies occur during the process such as the

blockage of abrasive particles or inconsistent flow of water jet stream. Thus, offline

monitoring method is often used which in turn causing the process to rectify problems

infeasible. Fewer studies have been done to monitor AWJ machining. Latest research in

monitoring AWJ machining was to monitor process anomalies using AErms. Thus, the

study is to focus on the application of AErms in monitoring the depth of cut in AWJ

machining. This will include how AErms performs to detect various anomalous events in

the process to support the previous studies. It is hoped that results from this research

will provide useful knowledge in understanding the material removal mechanism in

improving the on-line monitoring process.

3

1.2 Project description

The project is supervised by Prof Jun Wang and Dr Huaizhong Li from the school of

Mechanical and Manufacturing. Primarily, the project focuses on two parts which is the

study of cutting profile and the study of Acoustic Emission (AE) signal to monitor the

cutting process. Two types of different cutting methods will be used which is drilling

and traverse cut. In the study for cutting profile, emphasize will be given on the effects

of cutting parameters on the cut profile. This includes analysing the depth of cut, kerf

profile and the hole diameter. In the study of AE signal to monitor cutting process,

emphasize will be given on analysing the trend of the AErms signal that corresponds to

the cutting process. Results from these two areas of study will then be discussed and

conclusion will be drawn.

1.3 Thesis objectives

Based on the literature review, several objectives have been developed for this project:

• To monitor and obtain comprehensive understanding in the AWJ drilling and

traverse cut process using the acoustic emission technique.

• To establish the relationship between the AErms and the depth of cut.

• To investigate the influence of selected parameters on the cut profile in the AWJ

drilling and traverse cut process.

• To obtain comprehensive understanding in the material removal mechanisms of

the AWJ and factors that will influence the mechanisms as well as factors that

will affect the AErms signal.

• To improve on-line monitoring process in AWJ cutting.

4

1.4 Thesis outline

Chapter 2 contains reviews from the research from various journals, conference papers,

articles, books and websites in order to provide the fundamental information and

knowledge needed for this thesis. Extensive researches were done since from the

beginning of the water jet industry until its state-of-the art development, wherever

relevant.

Chapter 3 explains literature review in the development of acoustic emission monitoring

on the abrasive water jet machining from the early to the latest stage.

Chapter 4 is dedicated to all experimental works including experimental objectives,

experimental set up, apparatus used, experimental procedure, difficulties, and possible

experimental errors that are found throughout the experiment.

Chapter 5 details the results and discussions drawn from the drilling and traverse cut

experiment. Emphasize is made on the observed and physical cutting profile. Figures

and graphs are used to aid the analyses and discussions with regards to particular sub

topic.

Chapter 6 details the results from AErms monitoring for drilling and traverse cut

experiments. Some comments are made in the chapter. Figures and graphs are used to

aid the analyses and discussions with regards to particular sub topic.

Chapter 7 explains the results obtained during the through cut and anomalous events

that occur during the experiment. Some comments are made in the chapter.

5

Chapter 8 provides in depth discussion pertaining to the AErms monitoring. Emphasize is

made on the relationship between AErms and the depth of cut. Moreover, the effects of

cutting parameters on AErms are also discussed.

Chapter 9 concludes the findings for the project and the significance of the results. This

chapter also summarises the overall research and explains whether or not all thesis

objectives have been achieved and proposed future works.

6

Chapter 2: Literature Review

7

2.1 Titanium Alloy

Titanium and its alloys have over the years proven themselves to be technically superior

and cost effective materials for wide range of applications spanning the industries of

aerospace, industrial, marine, and even for commercial products (Wessel 2004). This is

because of their good combination of mechanical properties to include excellent specific

strength, stiffness, light, high temperature strength and immune to corrosion in sea

water environment (Poondla et al 2009)

The type of Titanium Alloy that will be used in this thesis is Ti-6Al-4V which belongs

to the alpha and beta alloy group and accounts for more than 60% of titanium alloy

production (Davis 1990). The noticeable attractive properties of Ti-6Al-4V have

facilitated in its selection and enhanced use in marine, aerospace and a spectrum of

other performance-critical applications. It is a ductile material and significantly stronger

than commercially pure titanium while having the same stiffness and thermal properties

2.1.1 General criteria for cutting tools

Dudzinski (2003, p.441) states that general requirements for any cutting tool material

used for machining hard-to-machine material like Ti-6Al-4V should include:

• Good wear resistance

• High hot hardness

• High strength and toughness

• Good thermal shock properties

• Adequate chemical stability at elevated temperature

8

2.1.2 Limitations in conventional machining of Titanium Alloy

1. The presence of Heat Affected Zone (HAE) due to low thermal conductivity of

Ti-6Al-4V

a. It has low thermal conductivity. Hence, the heat is unable to dissipate

quickly from the material. Under High Speed Machining (HSM)

environment where the cutting tool is relatively hard and the cutting

speed is significantly high. These combinations will result to an

extremely high temperature at the cutting region called the Heat Affected

Zone (Arrazola et al 2008).

b. At high cutting speed, it is well known that the lubrication in the cutting

zone is not evident and not really effective (Viera et al 2001).

c. Highest temperature at tool-chip interface will lead to diffusion wear and

cutting edge degradation. Since the cutting fluid is not evident at high

cutting speed, this leads to rapid tool wear that caused by thermal

deformation

2. Expensive cutting tool. Thus, rapid cutting tool wear can cause this process to be

less economical as frequent replacement is needed.

3. High clamping force is needed to maintain the work piece due to high elasticity

of Titanium Alloy. However, too much clamping force could deflect the work

piece and affects the surface integrity. Intricate parts can deflect under tool

pressures causing chatter, tool rubbing and tolerance problems (Moiseyev 2006).

9

The difficulties in machining Ti-6Al-4V using conventional method have engineered a

considerable amount of scientific and technological interest in developing alternatives in

machining hard-to machine material using other form of machining methods.

2.2 Non-Conventional machining process

As technology needs capital to fund the progression, from customer’s perspective cost is

an issue. One of the philosophies in engineering is to optimize the solution at the lowest

cost possible. Therefore, from a practical point of view, it is a necessary to develop a

niche technology which can generate optimum output at the lowest cost possible

without compromising with the quality.

Water, being a natural resource provides advantages for engineers to solve the problem.

The water jet technology provides many advantages and unique features that can prove

to be competitive with the current machining process from many areas.

Figure 1.1: UNSW abrasive waterjet machine

Catcher

Polyethylene cover

10

2.3 Abrasive Water Jet (AWJ) system

Since its introduction to the manufacturing industry in 1960, water jet machining has

been used extensively. There are two major types of water jet which is pure water jet

and abrasive water jet. While pure water jet uses pure water to cut the material, abrasive

water jet is a mixture of abrasive particles, most commonly garnet particles and water to

cut hard-to-machine material like metals or alloys (Kulukei 2002).

Seo et al (2003) conducted an experiment to investigate the machinability of titanium

alloy (Ti-6Al-4V) using AWJ. The results reported that optimum parameters are existed

in order of produce excellent cut with excellent surface finish. Optimum parameters are

function of traverse speed, pump pressure, stand-off distance and garnet size. Studies

done by Hascalik et al (2006) found that kerf taper ratio increases when traverse speed

increases in AWJ cutting of the same alloy. Wang (2007) performed an experiment to

predict the depth of jet penetration on ceramics. The predictive models developed shows

high degree of similarity to the actual cut and therefore can be used to mathematically

predict the depth of cut. Hlavac et al (2008) investigated the AWJ trajectory curve

inside the kerf. He developed a model that describes the curvature of the jet trajectory

inside curve. Later, Bound et al (2010) investigated the influence of abrasive

mechanical properties on grid embedment and cut quality for titanium alloy. The cutting

efficiency and grid embedment were not significantly difference for the five type of

abrasive used. Gent et al (2012) developed a study for process optimization in water jet

cutting for ductile material. The study indicates that the optimization of abrasives with

an adequate density can be obtained by maximizing particle size. Main components for

the AWJ system are explained in the next subchapter.

11

2.3.1 Pump intensifier

The intensifier acts as an amplifier as it converts the energy from the low pressure

hydraulic fluid into ultra-high pressure water. The hydraulic system provides fluid

power to a reciprocating piston in the intensifier centre section. During the plunger inlet

stroke, filtered water will enter the high-pressure cylinder through the check valve

assembly. After the plunger reverses direction, the water is compressed and exits as

ultra-high-pressure water (Tonshoff et al 1996). The usual range of pressures in AWJ

applications is between 250 to 400 MPa (Singh et al 1991).

Because of the compressibility of water, the first 15% of the stroke of the piston is used

to pressurise and compress the water in the cylinder without delivering water to the

system that can cause pressure fluctuations that give rise to cutting inaccuracies and

shorten the life of system components. Thus, an attenuator is used in the high pressure

circuit to damp pressure fluctuations (Blickwedel et al 1990).

Figure 1.2: UNSW pump intensifier system

12

2.3.2 The abrasive delivery system

Figure 1.3: UNSW abrasive delivery system

Abrasive delivery system delivers abrasive from the abrasive tank or known as hopper

via delivery line to the mixing chamber at which abrasive-water mixture will form. The

amount of abrasive flow rate is controlled using an orifice disc.

13

2.3.3 The cutting head

Figure 1.4: UNSW water jet nozzle

The cutting head converts the pressurised water into cutting instrument. Particles are

accelerated and pushed out of the water jet, hits the inner wall of the focusing tube,

rebounces and enter the water jet again. Phenomenon happen until velocity direction of

abrasive particles is nearly parallel to the direction of the waterjet. Quite common, the

nozzle is made from sapphire that will give high precision, robustness and accuracy

(Kulukei 2002).

14

2.3.4 AWJ control system

Figure 1.5: ABB robot mounted water jet

Normally there are two types of systems that can control the motion of cutting process.

Firstly is the Computer Numerical Control (CNC) system. Secondly is by using a robot

in which the nozzle is attached to the end effector of the robot. Computer and software

development to control the process have made AWJ possible to cut shapes very close to

the required dimensions. Commercial software includes compensation for mixing

chamber wear, bending of jet stream and variation in cutting speed for corners and

curves (Kulekei 2002).

2.3.5 Catcher

As 75% of the initial energy may still be retained in the jet, if the jet meets no work

piece at all, the full power of jet reaches the catcher. (Akkurt et al 2004).Thus, it is

important for the catcher to be robust, reliable and have a long life time. It is used to

absorb energy that is being exerted by the water jet. There are three types of absorbent

material that commonly used for catcher. They are:

15

a. Steel balls absorbent in a portable catcher

b. Water absorbent in a tank catcher

c. Slot absorbent in a slot catcher

Figure 1.6: Variants of energy absorbent

2.4 Advantages in AWJ machining

Wang (2003, p.5) states that there are several advantages and limitations of using AWJ

for machining process.

1. High Machining Versatility

a. AWJ can cut almost any type material from pre hardened steel, alloy,

brass materials, even food.

2. Ability to produce contour

a. It is exceptionally good to cut very complicated shapes or bevels of any

angle because the process is unidirectional.

3. Almost no heat generated

a. Water behaves as the cutting tool, thus the heat generated is

instantaneously carried away by the water. As a result, no significant

temperatures rise in the work piece. This is very useful for cutting very

hard material that is sensitive to heat. Although laser cutting is one of the

16

non-conventional machining process, but laser produces heat at the

cutting region.

4. Small cutting force

a. Since the diameter of the orifice is very small, ~0.1 mm, thus the cutting

force generated is small. In most cases, no clamping is required to cut

flat surfaces and no tool changes are required.

5. Low cost

a. The cost of operation is relatively low. Nozzle wear does occur however

the cost of replacing the nozzle is cheaper than conventional cutting tool

like milling cutter or drilling bit.

2.5 Limitations in AWJ machining

1. High intial capital cost and operating cost

2. Noisy Operation

a. Motor pump produces mechanical noise while free jet travelling at high

velocities produces aerodynamic noises. When operating AWJ machine,

ear protector is mandatory.

3. Nozzle wear

a. The performance of AWJ system is also influenced by the nozzle.

Nozzle is subjected to mechanical wear as machining progresses and this

can cause uneven mixing between the water and abrasive particles which

result in deterioration in cutting ability, poor surface quality, and poor

part geometrical accuracy.

17

4. Striation Effects

a. The striation or curvature lines are produced on the cut surface, leading

to poor surface finish

5. Kerf Taper formation

a. Kerf Taper formation leads to inaccuracy of cutting

2.6 Erosion mechanism for ductile materials

2.6.1 Micro cutting

Finnie’s micro cutting model has developed number of significant investigations (Finnie

1958). The volume of material removed, defined as the volume swept out by the tip of

particle is purely a result of plastic deformation of the material by the impingement of

the particles. The cutting action of this particle causes plastic deformation on the target

material.

Figure 1.7: Single Particle Micro Cutting Mechanism

The volume of material removed (Q) can be quantified by equations

Q = �� 2� ��

∅ where tan � ≤ �≤ 6

Q = ��

� where tan α ≥ ψ/6

18

Where mp is the mass of an abrasive particle, Va is the abrasive particle velocity, γ is the

particle attack angle, is the flow stress of target material, � is the ratio of vertical

horizontal force, and � is 2.

2.6.2 Cutting-deformation

Bitter (1963) proposes that there are two material removal mechanisms that occur

simultaneously. Firstly is the wear deformation that is caused by collisions of particles

on the target material. Secondly is the cutting wear that is caused by the cutting action

of the free moving particles.

The volume of material removed by deformation wear (Qdw) can be quantified by :

Where Ԑd is the deformation wear factor, and Vc is the critical particle velocity above

which it can remove the material.

The volume of material removed by cutting wear (Qcw) can be quantified by

Where Ԑc is the cutting wear factor and C is an empirical parameter, and the sum of the

two formulas gives the total material removed.

19

2.7 Erosion mechanisms for brittle materials

There are two material removal mechanisms which are different from ductile material

are associated with brittle material:

2.7.1 Conical crack

This is based on the assumption that erosion occurs entirely by crack propagating and

chipping. The typical conical crack erosive mechanism is presented by Sheldon and

Finnie (1966). It is assumed that Hertzian contact stress during impact results erosion to

occur. The volume removed per impact is proportional to particle penetration depth and

cracked area.

The volume of material removed Q can be quantified by

Q = k1RaVa

b

R is the particle size (radius), Va is the particle velocity, and m is the Weibull constant.

k1 is constant determined by the target and particle materials. Exponents a and b are

respectively given by

a = �(��.��)

(��) (for a round particle)

a= �.�(��.��)

(��) (for an angular particle)

b = �.(��.��)

(��) (for either shape of particles)

20

2.7.2 Lateral crack

It is based on the assumption that plastic deformation contributes to the process of crack

formation and surface chipping. Evans and Gulden (1978) argued that the erosion

process during a single particle impact in terms of experimental crack behaviour and

considered lateral crack formation in establishing the material volume loss or removal

model.

The volume of material removal for lateral crack can be quantified by

Q = Va19/6R11/3pp

19/2 Kc

-4/3H-1/4

Figure 1.8: Lateral Crack Mechanism

Where pp is particle density, Kc is the critical stress intensity factor, and H is the

hardness of the target material.

21

2.8 Cutting stage processes

Figure 1.9: Three stages cutting process

Hashih (1988) conducted experiment to cut glass and plexiglass in the traverse direction

and found that the cutting process proceeds in three stages which are entry stage,

developed cutting stage and exit cutting stage as shown in figure 1.9.

In the entry stage, the waterjet impacts at the shallow angles and large angles.

Therefore, different cutting mechanisms occur until the cut reaches maximum depth. As

the depth of cut increases, the particle impact angle increases. The complete

development for penetration process occurs at maximum depth of cut. This leads to

large jet upward deflection.

In the developed cutting stage, a cyclic steady state cutting process occurs until it

reaches the end of the material.

In the exit stage, a jet forward deflection occurs that associated with the fprmation of

uncut triangle. This marks the end of cutting wear zone.

22

2.9 Machine process monitoring

Machine process monitoring focuses on microscopic and macroscopic behaviour of the

workpiece when subjected to machining. This mechanism can help researchers to

further understand the mechanics of machining and develop new knowledge for

optimization. Practical application like to the sense product quality in real-time so that

should there be any problems occur with the quality, it can be immediately rectified and

adjustment can be made. This may reduce the production cost and material waste.

Y.B Guo et al (2005) conducted an experiment to monitor surface damage in hard

machining. He uses a real-time acoustic emission monitoring system to investigate the

sensitivity of a broad AE signal parameters to white layer (surface damage) and the

corresponding tool wear and surface finish. He found that AErms , frequency, and count

rate are sensitive to the existence of a white layer and corresponding tool wear and

surface roughness, while AE amplitude and AE count rate are not sensitive to white

layer thickness.

23

2.9.1 Acoustic Emission as monitoring method

Acoustic Emission is a transient elastic waves due to the rapid energy release from a

localized source within a material when subjected to stress (Guo et al 2005).

Figure 2.1:AE generation and acquisition

General nature of Acoustic Emission can be summarised as:

• The propagation of AE wave can be along all directions inside the material. The

wave can be detected using AE sensors installed near or some distance away

from the source.

• It is sensitive to detect active microscopic event in the material, especially to the

relative moment and not the geometry of the defects.

• If no variable stress occurs, then no AE will be generated.

24

2.9.2 Acoustic Emission sources

It can result from the initiation and growth of crack, slip and dislocation movements, or

phase transformations in metals. The source is predominantly originated with stress at

which strain is induced in the material when stress is exerted. The object may return to

its original dimensions of permanently deformed after the stress is removed. These two

conditions are known as elastic and plastic deformation. When a loaded material

undergoes plastic deformation, or when a material is loaded at or near its yield stress, it

is the point where acoustic emission source is most detected. Kovacevic et al (1998)

state that the acoustic emission can associate with the amount of energy released during

the crack formation. Large, discrete crack jumps will produce larger AE signals than

slow propagation crack over the same distance.

2.9.3 Acoustic Emission signal and parameters

Figure 2.2: Typical AE signal

In general, the source motion is short and sharp while for the material under the sensor

is far more complex because wave propagates. Another observation is that the

amplitude of wave decreases when the sensor is far from the source. Attenuation

governs detectability at a distance.

25

Whenever in the rapid and continuous effect, the signal coming out of the sensor is

oscillatory. This is as expected because AE behaves as an elastic wave. The higher

intensity of the wave resembles that it is sampled in the high sampling frequency. The

signal is subjected to attenuation as the signal will decay and lose energy while

traveling. The higher frequency content, the faster is the decay rate than the low

frequency (Mathew 1983). The type of AE signal can be classified into two; Burst type

and Continuous type:

1. Burst type emission

a. Burst emission signal can be defined as signal that consists of pulses

detectable from background noise and well enough separated in time so

that there is not a lot of overlap.

b. Burst-type signals for machining are observed in unsteady processes

such as cracking materials (Schofield 1972), transgranular spalling

fracture and cavitation (Momber et al 1995).

2. Continuous type emission

a. Continuous emission signal can be defined whenever the resolution of

individual pulses is not possible.

b. Continuous-type signals for machining are often associated with plastic

deformation in ductile materials and erosion process in brittle materials

(Momber et al 1995).

26

Figure 2.3: AE parameters

2.9.4 Acoustic Emission sensors

In any acoustic emission experiment, the sensor used to detect acoustic events is the

most critical part. The output of the transducer is then being amplified, possibly filtered

and processed in any number of signal conditioning schemes. Since the first element in

any signal processing scheme or analysis will be the sensor, considerable care must be

exercised in the choice and placement of the sensor. For this project, the type of sensor

used is the KISTLER Model 8152B Piezotron® Acoustic Emission Sensor in figure 2.4.

Voltage (V)

Time (s)

Continuous emission

Event

Peak Amplitude

Threshold level Counts

Burst emission

27

Figure 2.4: KISTLER Acoustic Emission Senssor

Piezotron® Acoustic Emission Sensor with built-in impedance converter for measuring

Acoustic Emission (AE) above about 50 kHz in machine structures. With its small size,

it mounts easily near the source of emission and captures the signal optimally. John

(2007) states some of the advantages to use this sensor:

• High sensitivity and wide frequency range

• Inherent high pass-characteristics

• Insensitive to electric and magnet noise fields

• Robust, suitable for industrial use

2.9.5 Acoustic Emission source location techniques

One fundamental assumption in source location technique is that the AE waves travel at

a constant velocity in a material. However, the expected velocities may be influenced by

reflections or refractions and can affect the accuracy of this technique. Therefore, the

geometric effects of the structure being tested need to be considered to determine which

source location technique will give a reliable solution. In general, there are three types

of AE source location techniques:

1. Simple Linear Location Technique

2. Three Dimensional Location Technique

3. Spherical Location Technique

28

2.10 Acoustic Emission Root Mean Square (AErms) Value

Root Mean Square (RMS) is one of the techniques used for continuous AE processing.

It resembles an average derived from the linear voltage. It defined as the rectified, time

averaged AE signal which measures on the linear scale and converted in voltage. The

instantaneous value of RMS is collected and processed when AE signal is detected.

The resulting AE signal is highly dependent on the nature, surface characteristics and

condition of cutting tools and workpiece material. When the thrust force generating

stresses exceed the critical value, crack will initiate. Therefore, an additional quantum

of energy gets released due to this mechanism thus yielding a complex AE-signature.

Ravinshankar et al (2000) investigated the application of acoustic emission when

drilling composite laminates. They found that AErms value reveals a consistent

behaviour in relation to a process parameter like the thrust force under the complex

machining state. This helps them to give an overall understanding on the drilling state

by highlighting various events and damage mechanisms during the process.

Guo and Ammula (2005) stated that in monitoring surface damage for hard machining,

result shows that AErms value is sensitive to acoustic emission signals as compared to

amplitude and count rate. A significant trend is observed from the result. AErms

decreases with increasing flank wear and increases slowly until a thick white layer form.

This sensitivity is crucial to understand the complex event that occurs during waterjet

cutting. Single pass cutting will yield uneven depth of cut. Thus, acoustic emission

signals may vary so as frequency.

Tonshoff et al (2000) found that the AErms signal can be used to determine the influence

of the process on the sub-surface microstructure of the workpiece. Their aim of the

29

grinding monitoring tool is the detection of incorrect process states. The results show a

significant correlation between the root mean square signal and the residual stresses that

is dependent on the speed ratio which is related to the material removal rate.

Tonshoff et al (2000) proposed that measuring the energy content is one of the useful

methods to analyse the AE. They found that the rate of energy released in the form of

acoustic emission can be indicated from the AErms .

From these studies, AErms value is one of the useful parameters to use for this research

due to its sensitivity to the material removal mechanism. This is useful to detect any

small variation on the material removal mechanism during waterjet machining that is

complex in its nature.

30

Chapter 3: Development of AE Monitoring in

Water Jet Industry

31

3.1 Monitoring AWJ machining with Acoustic Emission method

Kovecevic et al (1998) investigated the effect of acoustic emission (AE) technique to

understand the mechanisms of abrasive water jet drilling on three types of different

materials which was magnesia chromite, sintered magnesia, and bauxite. Bauxite being

the hardest material, followed by sintered magnesia and magnesia chromite. In the

experiment, AE signals were monitored in three different cutting stages, namely the

pure waterjet impingement stage, the drilling stage, and the dwell stage. Based on the

result illustrate in figure 1, the highest AE signal amplitude is observed in the pure

waterjet impingement which is five times larger than drilling, then followed by the

dwell stage.

Figure 3.1: General time domain AE signals for different process stages

Pure Water Drilling Dwell

32

It should also be noted that the amplitude corresponding to pure waterjet impingement

for bauxite is higher than sintered magnesia and magnesia chromite. Part of energy is

used for the material removal mechanism while part of energy is dissipated in the fluid

damping phenomena. They argued that the effect of damping and the amount of energy

dissipated will increase when drilling depth increases. Pure waterjet penetrates less due

to the absence of abrasive particles; therefore less energy is used for penetration. Thus,

almost the entire energy is transmitted in the form of vibration to the sensor via the

workpiece, resulting higher AE value. This is also the reason for the higher signal

amplitude for bauxite since it is the hardest material compared to sintered magnesia and

magnesia chromite. It was found that the AErms decreases as the drilling time increases.

Drilling time influences the drilling depth. In waterjet drilling, the backflow of water

created turbulence in the pocket, which may generate damping effect on the AErms

signal. It is obvious that as the depth of cut increases, more abrasives, water and slurry

collected in the cavity which increases the damping effect. In figure 2, the relationship

between the AErms and the drilling depth is established.

Figure 3.2: Relationship between AErms and drilling depth

33

The AErms decreases linearly as the drilling depth increases. Kovacevic reasoned that it

is because of the non-linear trend of the drilling depth with respect to time and the

damping effect on the AErms due to the presence of turbulence and material debris in the

pocket. Higher AErms value observed in bauxite due to the presence of hard inclusions

that increases material toughness. In the experiment, the Power Spectral Density (PSD)

curve exhibits the same trend as penetration rate. Thus, it is capable to provide

information about the process. Burst signal observed in figure 3.1 justifies the cause of

material failure in brittle material to be intergranular crack as proposed by Ritter’s

model (1985). Zeng and Kim (1996) proposed mixed damage that included plastic

deformation in the brittle failure. Kovacevic et al (1998) concluded that the dominant

AE source until which the damping effect becomes significant is the impinging jet. He

also found from frequency decomposition of the Auto Regressive Moving Average

(ARMA) model, the impinging and rebounded jet correspond to the primary and

secondary frequency.

Later, Womber et al (1999) performed an on-line analysis of hydro-abrasive erosion for

five different concrete mixtures by acoustic emission. The mechanical properties of

each concrete mixture were influenced by varying the water-cement ratio and the

aggregate fineness. Traverse cut was performed on each specimen and they found the

time domain AE-signal for every mixture varies corresponding to different type of

mixture. Two signal trends were observed, predominantly of continuous type which is

typical to steady-state material removal mechanisms in concrete #1; whereas the other

observation was primarily burst emission which indicates the presence of unsteady

material removal mechanisms in concrete #5. Thus, AE signal is a promising tool to

differentiate between these two material removal mechanisms.

34

Figure 3.3: AE signal correspond to the depth of cut

Theoretical depth value calculated with brittle erosion model. The value was then

compared with the experimental depth value in figure 3.3. A reasonable agreement can

be noticed. It was noted the signal raw amplitude for harder concrete mixture is two

times higher than for the less harder mixture. The same observation also reported by

Kovacevic et al (1998) when drilling three materials with different properties using

waterjet. Womber et al (1999) suggested that kinetic energy dissipated during erosion

can be quantified by the AE signal and represented by the area enclosed by the Fast

Fourier Transform (FFT) curve or AErms . At long exposure time, the AE signal

decreases that may due to the damping effect as mentioned by Kovacecik et al (1998,

p.52).They also found that material with the highest water to cement ratio was not

sensitive to the damping effect because large cavities generated during the cutting

process create additional space so that the abrasive-water mixture can escape instead of

trapped that may cause backflow. Kerf wall may restrict the high-speed slurry which

creates the damping effect. Less energy is dissipated in the erosion process in the

samples with higher strength properties and therefore, decrement in the AErms and FFT

peak. They concluded that any changes in the process parameter which increases the

erosion depth, thus quantity of material removed will cause energy dissipated to

increase and this can be significantly detected by sensitive AE-signals.

35

Giving that the concept of acoustic emission is one of the promising tools to understand

material removal mechanism in hydro-erosive erosion, the study is then extended by

Momber et al (2002) when they later conducted an experiment to detect the formation of

fracture, caused by hydro-erosive erosion in concrete. In this study, AE is used to

distinguish between individual fracture types observed during AWJ cutting of different

concrete. They improvised their previous experiment (Momber et al 1999) by

performing regression analysis between the penetration depth and the FFT parameters;

FFT-peak frequency and FFT-peak amplitude. In FFT-peak amplitude analysis, the

depth of penetration increases as FFT-peak amplitude increases because more material

is removed. It is also observed that there are two range of extraordinarily high and low

amplitudes relative to the general trend, giving outliers.

Figure 3.4: FFT peak amplitude and frequency with the depth of cut

High amplitudes predominantly belong to concrete that single mode (transgranular

fracture) fashion as showed by optical microscopy photograph. Low amplitudes mostly

belong to concrete #4 that exhibit mixed mode (intergranular erosion and transgranular

fracture) fashion from the microscopic inspection. They concluded that FFT-peak

amplitude is recommended for on-line monitoring the depth of cut, whereas FFT-peak

36

frequency to monitor unusual conditions in the concrete structure (crack deflection,

branching by reinforcement bars, lost mountings, or aggregate agglomerations).

3.2 The needs for on-line monitoring

The on-line measurement of the erosion depth of cut is the key aspect in the

development of fully automated abrasive waterjet system. Current system is semi-

automated as the parameters are adjusted manually if changes are needed due to the

machining profile of the workpiece. For example, if different depth of cuts existed in a

cutting profile, the machine parameters (traverse speed, pressure, etc) need to be

readjusted. The machine needs to be stopped while machining. Furthermore, current

system is capable of cutting 2-D profile. Fewer researches have been made to cut 3-D

profile. Waterjet machine in UNSW advance manufacturing lab for instance is capable

of machining in 2-axis. Thus, further research needs to be done to develop fully

automated system that can machine in more than 2-axis, as well as monitoring and

controlling the machining process.

It is a challenge to produce a controlled cavity depth because it comprises of complex

functions including waterjet pressure, abrasive flow rate, abrasive grain size, stand-off

distance, traverse speed, etc. (Mohan 1994, p.649).

Present system also uses an “open-loop” approach, where off-line calibration

experiments are performed in order to find an optimal combination of the process input

variables (Mohan 1994, p.651). However, to control the uniformity of AWJ depth

penetration, especially when a disturbance is present in the process, it is necessary to be

able to monitor the depth of penetration on-line. Therefore, establishing a close-loop

mechanism that can provide a feedback to the system so that the system cans response

based on the real-time condition.

37

3.3 The development for on-line monitoring depth of cut

This thesis is aimed to use AErms as the main parameter to monitor the depth of cut in

AWJ cutting. Insofar, there have been fewer studies that discussed about the reliability

of AErms to monitor the depth of cut. Most studies used the approach of FFT, PSD, or

ARMA model which does not give much information, that is feasible, and can correlate

with the mechanism that occur during the cutting stages (entry, cutting, and exit) . This

may because AErms was not the parameter of interest during the signal processing

because it cannot be correlated in the frequency domain. Extensive reading has been

done to find studies that used the AErms as the main monitoring parameter in the time

domain.

Mohan et al (1994) first conducted experiment to monitor the depth of AWJ penetration

using acoustic emission technique. They found that the peak of Power Spectrum

Density (PSD) exhibits a linear relationship with kerf depth and propose a closed-loop

mechanism for on-line monitoring and controlling the depth of cut. Later Kovacevic et

al (1998) monitor the drilling process using AErms in the drilling process and explained

these phenomena as discussed in the previous sub chapter. The AErms signal decreases

linearly with increasing in the depth of cut as illustrated in figure 3.2.

Hassan et al (2003) have extended the research by Kovacevic to develop a new model in

monitoring the depth of cut. As Kovakecik et al (1998) were looking at the stationary

cutting which is drilling, Hassan et al (2003) performed dynamic cutting which is a

traverse cutting on carbon steel.

In the experiment, they discussed that the main drawback of the model developed by

Hashish (1989) is its suitability for only ductile materials, whereas Zeng and Kim

(1996) developed an experimental model for ceramics. Hassan also noted that vertical

38

cutting force determines the amount of material bering removed. Studies done by

Kovakecik (1992) shows that:

1. Vertical cutting force increases with increasing pressure, abrasive flow rate and

mixing tube diameter.

2. Vertical cutting force decreases with increasing stand-off distance and it is only

slightly affected by traverse rate.

3. A large increase in the magnitude of the vertical force indicates the presence of

the mixing tube wear

In the experiment done by Hassan et al (2003), two monitoring methods were used.

Namely the vertical cutting force method and acoustic emission method.

39

3.3.1 Vertical cutting force monitoring

Figure 3.5: AWJ vertical cutting force signal as a function of time at different pressures.

(a) P : 100 MPa, (b) P : 200 MPa

In figure 3.5(a), it is observed that at point B, where the cutting force starts to rise until

point C is the point where AWJ starts to contact the material. At point D to E, the

cutting force becomes stable in the cutting zone. In the exit stage, which is at point F, a

peak can be observed before the signal suddenly decreases. Indicating the waterjet has

completed the traverse cut. Figure 3.5(b) shows the signal when pressure increases. It is

apparent that increase in pressure will increase the cutting force. Therefore, cutting

force value at stable cutting stage in figure 3.5(b) is higher the in figure 3.5(a) due to the

increase in pressure. Hassan et al (2003, p.601) concluded that there is a linear

relationship between the vertical cutting force and the depth of cut.

40

3.3.2 Acoustic Emission monitoring

Figure 3.6: Variation of the acoustic emission band energy with time at 100 MPa

Figure 3.6 represents six scales of energy correspond to the frequency band widths.

Point B is where the impact between the workpiece and waterjet start to occur. The jet

overcomes entry zone from point B to C. Unstable acoustic emission response is

observed from point B to D. They argued this might due to the high dynamic interaction

between the waterjet and the workpiece. Although the trend may be able to resemble the

stages of cutting process (entry, cutting, and exit); the trend however, particularly at the

cutting stage is different from the vertical force trend. Mohan et al (1999, p.649) found

similar observation of the AE signal for the traverse cut with Hassan et al (2003), even

when the pressure and traverse speed are changed.

Figure 3.7: Frequency power spectrum of the traverse cut process

41

From the frequency power spectrum and the contour map, it is observed that the dark

spots in the middle of the map represent the higher energy level, indicate the high speed

impact during the steady state cut.

Figure 3.8: Cutting force and AErms with regards to the depth of cut

Figure 3.8 indicates the relationship between the AErms and the depth of cut. It can be

seen that the depth of cut increases linearly with the increase of AErms value and thus

can be used for its on-line monitoring. It is apparent from this figure that the generation

of the AE energy in AWJ cutting depends on the material removal rate or more

precisely the depth of cut. Moreover, the AErms response to the variation in the depth of

cut is more sensitive than cutting force response. Thus, a relationship can be expressed

as

AErms = C1 + C2h

Where C1 and C2 are constants and h is the depth of cut. According to Hassan (2003),

this equation represents the energy consumption in AWJ cutting in terms of AErms

energy to give a theoretical basis for the prediction of the depth of cut in AWJ cutting.

42

The result seems to be contrary to the one obtained by Kovacevic et al (1998) in figure

3.2 at which they found that the AErms value decreases when the depth of cut increases.

However, this result can be analysed from another perspective:

• The AErms value in Kovacevic et al (1998) experiment refers to the energy

dissipated from the crack formation whereas Hassan et al (2003) refers to the

energy consumed.

However, to assume AErms in this case is the utilised energy is quite difficult to

understand and comprehend due to the nature of acoustic emission that is dissipated

from the material under stress. Further research is needed in this area because from

terminology, acoustic emission sensor captures elastic waves that are propagated from

the material when fracture occurs when the material is under stressed, not energy wave

that causes material removal process.

Hassan et al (2003) argued that the more material is being removed, the more energy is

being used to remove the material. The deflection of particles on the microscopic level

and the increase of particle velocity as pressure increases lead to increase in AErms

value. Fluid dynamics of the AWJ process such as turbulence when the jet passes

through the workpiece can also be the contributing factor. It must be noted that at any

instant of time there could be more than one source generating the AE signal and hence

the general characteristics of the signal are determined by the dominant source.

Kovacevik et al (1998) commented that during the drilling process, part of energy is

used for penetration and part of the energy is lost in the fluid damping phenomena

owing to factors such as turbulence and cavitation. When the water jet penetrate less to

the workpiece, less energy is dissipated in the material removal rate and as a result,

43

almost the entire energy of the impinging jet is transmitted to the sensor through the

workpiece in the form of vibrations. This could be the reason for the higher value of

AErms as the depth of cut decreases. Another reason may be due to the backflow of the

water and abrasive particles. As the drilling is performed by a stationary jet on a

stationary work piece, the jet continuously removes the work piece material from the

drilled hole and the remove material is displaced by jet back flow. As a result, the back

flow of the jet reduces the particle velocity when the penetration depth increases as the

cavity increases.

In this model, Hassan et al (2003) concluded that there is a positive relationship

between the AErms and the depth of cut. The process can be monitored using both

vertical force and AE techniques since both will give identical results. However, AErms

can be the best alternative due to the high cost of the dynamometer and the rough

condition of AWJ cutting process that may damage the dynamometer, making it less

practical and less versatile.

3.4 Acoustic Emission to detect energy dissipation in AWJ

Based on Momber et al (1995), only a portion of input energy from the waterjet is

utilised during the process while others will be dissipated in various mechanisms like

wall friction, heat formation, plastic deformation, and damping effect. Raju and Ramulu

(1994) describe that the quality of cut can be determined by local kinetic energy of

abrasive particles due to abrasive particles are the dominant factor in erosion process.

Mohan et al (2002) then developed a study on the energy dissipation control in AWJ

using acoustic emission. They suggested a physical model and identified parameter that

is capable to determine the energy dissipated by implying the PSD and ARMA model.

44

Energy dissipation model has first been derived from the first principle of conversation

energy, giving equations for the input energy (EA) and dissipated energy (EDISS).

Using these equations, significant relationship between the depth of cut and energy

dissipated during erosion was established by Mohan et al (20002) .

EDISS= J1h2 + J2h + J3

Where J1,J2, and J3 are constants and h is the depth of cut. They found that the energy

dissipated to be estimated between 60% - 90% of the input energy. Using the PSD of

the ARMA model in time domain AE signal, they argued that the time domain is a

better parameter to use than the frequency domain in order to differentiate the various

energy dissipation levels since it is much more sensitive to any variation on line. An

upward shift in the PSD curve detects an increase in the energy dissipated caused by

changes in process parameters.

They also found that the area enclosed by the PSD-curve can be considered as a

quantitative measure of the total energy of the AE-signal. A relationship between the

dissipated energy and the AErms has been established. AErms is expressed in a function

of root square of the dissipated jet energy.

This trend as discussed by Mohan et al (2002) could be due to the damping effect or

turbulence which does not contribute to the material removal process.

45

3.5 Acoustic Emission in AWJ machine condition monitoring

Based on results in the previous studies by Mohan et al (1994) ,Kovacevic et al (1998),

Mohan et al (1999), Momber et al (2002), and Hassan et al (2003), the up to date

development to monitor AWJ machining using acoustic emission was conducted by

Axinte and Kong (2009).

They conducted an experiment to supervise AWJ linear cut on titanium alloy using

acoustic emission method. Although it is not purely focus to monitor the depth of cut,

the results of the experiment are essential and very useful to the thesis. In the

experiment, acoustic emission sensors were mounted at three different places which is

nozzle, workpiece, and dummy plate as illustrated in figure 3.9.

Figure 3.9: Location of the AE sensor

There are three reasons mentioned by Axinte and Kong (2009, p.304) for the decision to

mount sensors at selected areas:

• Nozzle: Is to monitor, if any variations of the input energy occur that leads to the

presence of anomalous event at nozzle.

46

• Work piece: Is to monitor, if any variations of the utilised energy occur for the

material removal process at the workpiece.

• Dummy plate: Is to monitor, if any variations of the idle energy occur to detect

anomalies in the cutting process.

The experiment set up and parameters were designed and selected so that three types of

process malfunctions can be observed. The expected process malfunctions are:

• Nozzle clogging; either it is partial or total blocking of the abrasive particles.

• Both uniform and non-uniform jet penetration during through cutting operations.

• Non-constant jet eroded footprint during cutting operations.

Figure 3.10: AErms signal in various process conditions

AE signals were captured in the AErms time domain as illustrated in figure 3.10. It is

noted that normal jet behaviour can be represented by a constant level and steady state

AErms signal value. Any deviation from this trend exhibits nozzle clogging whether it is

47

partial or total clogging. In total clogging, the AErms decreases significantly to zero as

compared to partial clogging and total mixing.

Constant AErms signal at the nozzle resembles stabilised mixing and normal jet

behaviour throughout the experiment. Once the waterjet starts to impinge on the

workpiece, the AErms value for dummy plate suddenly drops. Axinte and Kong (2009,

p.305) argued that this is because part of the energy is used for the material removal

process of the workpiece. They also concluded that AErms signal exhibit a reliable

correlation between the utilised, idle, and input jet energy.

Another result of interest from their research is the variation of AErms value at target part

when normal, incomplete, and no jet penetration occur as it gives additional information

to understand the effect of AErms signal as the depth of cut varies, in particular when

sampling at high frequency rate.

Figure 3.11: AErms in anomalous events.

Figure 3.11 shows the AErms patterns when three different events occur. In full

penetration (blue line), the AErms value is the lowest among three. Axinte and Kong

48

(2009, p.305) argued this is because in full penetration, most of the jet energy is being

utilised and therefore, less energy escapes that is detected by the sensor. In non-

penetration (red line), almost the entire jet strength is transferred to the workpiece, thus

the AE source is due to vibration of the workpiece. Dummy plate that detects escape jet

does not sense any AErms signal at this point. The incomplete jet penetration (green line)

is characterised by the AErms value which is in between of the full penetration and non-

penetration. Thus, it is observed as the deeper the depth of cut, represented by full

penetration graph, the lower the AErms captured by the sensor. This observation is

consistent with Kovacevic et al (1998) when monitoring AWJ drilling process using

acoustic emission. AErms here is defined as elastic energy dissipated due to cracking and

not utilised by the jet.

3.6 Summary of literature review

In short, reviewing related journals and articles provides in-depth understanding and

adequate information that are needed for this research. Some of the summaries are:

1. The development of AWJ and advantages of using AWJ to machine titanium

alloy as compared to conventional machining processes. These justify the needs

to expand the research on the performance of waterjet especially in optimizing

the operation process to machine titanium alloy in which could be implemented

to other hard-to-machine material.

2. State of the art for the on-line monitoring process for AWJ using acoustic

emission. The literature review justifies the acoustic emission sensor to have

better monitoring ability, more efficient, more cost effective, and more robust

against dynamometer. Thus, acoustic emission sensor will be used in this

research

49

3. In the past, there has been less research to monitor the depth of cut on ductile

material for AWJ cutting process. Therefore this leads to the need to investigate

and develop much mature concept to monitor AWJ cutting process using

acoustic emission sensor.

4. Discrepancies for the result in the relationship between AErms and the depth of

cut. Therefore this justifies the need to conduct the project.

5. The needs to understand the effects of process parameters on AErms so that

comprehensive understanding will be developed for future studies.

50

Chapter 4: Experiment Procedure

51

4.1 Experiment Setup

The experiment was conducted at the Abrasive Jet Research Laboratory in room L21F,

Willis Annexe bulding.

Figure 4.1: Schematic diagram for experiment setup

In the actual setup, workpiece is placed on the platform and secured using G-clamp. AE

sensor is placed some distance away from the workpiece to provide wave attenuation,

and clamped onto a metal plate. Two output channels were used (AE raw and AErms).

All electrical connections were wrapped using a cling film to insulate the cable and

components from the deflection of abrasive particles and water.

Polyethylene Cover

ON/OFF Valve

ABB Robot

Intensifier Pump

Booster pump & filter

Water supply

PC

DAQ system

Workpiece

AWJ Abrasive Tank “Hopper”

Metal Plate

Mixing ChambeAbrasive Feed

Catcher

AE Sensor

Clamps

52

4.1.1 Initial experiment setup

(a)

(b)

Figure 4.2: Initial experiment setup (a) sensor-workpiece (b) data acquisition

AE sensor Titanium work piece

KISTLER Coupler Power supply

PROSIG Data acquisition (DAQ)

Clamp

AE raw output

AErms output

53

4.1.2 Trial test

The initial equipment and experiment setup are shown in figure 4.2. The sensor was

directly attached to the work piece in order to get precise data. To confirm that

reasonable data will be obtained, a trial test was performed before starting the actual

experiment. The result for the trial test is represented in the diagram below:

Figure 4.3: Initial AErms signal for drilling trial test

The trial test conducted was drilling under the influence of 200 MPa and 25 seconds

drilling time. From figure 4.3, it shows that the AErms voltage exceeds the sensor

capacity to capture signals within its range (0 – 10 V). The maximum output voltage of

the sensor as described by the manufacturer is 5V with the error of ±5 V as described in

Appendix A. Theoretically the maximum output voltage value is 10 V. A horizontal

straight line at 10 V indicates the “saturation point”. Therefore, it can be seen that the

data from time 3 seconds to 10 seconds is “missing”. The sensor was capturing signals

at very high intensity.

54

To explain this occurrence, is by understanding the nature of water jet drilling process.

The process involves high velocity of water and abrasive particles impact on the very

hard material. Molecules in the alloy are stressed and vibrated. Some of the energy that

strikes the workpiece was used to cut the material, while some will transform other form

of energy like vibrations (Kovacevic et al 1998). Source of acoustic emission varies, for

instance turbulence, water jet back flow, vibrations, crack growth, and dislocation

movements. As the sensor was placed directly to the source of acoustic emission, it

received high intensity of AErms signal. The closer the sensor to the source, the higher

the intensity Moreover, the acoustic emission sensor is sensitive to the vibration as

explained in a water jet drilling test on ceramics conducted by Kovacevic (1998), High

amplitude of the acoustic emission signal that was captured by the acoustic emission

sensor during pure water jet drilling suggested that most of the energy of the

impingement jet is in the form of vibration. Pure water jet drilling penetrates less

therefore the damping effect also less. Therefore, the experiment set-up needs to be

modified so that a reasonable and useful trend can be observed.

Primarily, the experiment needs to involve material removal mechanism so that gradual

change in the depth of cut can be observed. Therefore, two type of process have been

chosen which is drilling and traverse cutting. Both experiments were designed so that

objectives for this project can be achieved.

55

4.1.3 New experiment setup

Figure 4.4: New experiment setup

According to Einstein’s inverse square law, a specified physical quantity or strength is

inversely proportional to the square of the distance from the source of that physical

quantity. In acoustic, this can be represented by equation:

Where I is the wave intensity, p is the wave pressure, v is the wave particle velocity and

r is the source distance. In order to reduce intensity is to increase the distance between

source and sensor. Therefore, to overcome the previous problem, the AE signal needs to

be attenuated so that a significant signal pattern for analysis could be observed. In the

new set up as illustrated in figure 4.4, the sensor was placed 35 cm further from the

work piece. A metal plate with thickness of 5 mm was placed underneath the sensor to

improve signal transmission to the sensor.

Acoustic Emission Sensor

Work piece

Metal Plate

AWJ Nozzle

G-Clamp

56

The thickness of the plate was 10 mm thick and considerably significant for the

experiment as larger plate thickness could result higher damping effect (Kovacevic

1998). A trial test with the same parameter was performed and the result is illustrated in

figure 4.5.

Figure 4.5: New AErms signal for drilling trial test

A complete pattern for the graph is shown in figure 4.5. The maximum amplitude of the

graph does not exceed 10 V. Furthermore, the pattern does have all the data needed

throughout the drilling time. No saturation point is observed. The graph represents the

actual drilling process within the drilling time. Therefore, this new experimental set up

was used for the actual drilling and traverse cut experiments to ensure consistency.

Since it is difficult to calibrate the acoustic emission sensor, it is proposed that further

research is needed to find the best method to calibrate the sensor.

57

4.2 Experiment procedure

The experiment was designed to obtain a significant depth of cut, both for drilling and

traverse cut. The traverse speed and the water jet pressure are more dominant in

affecting the material removal rate than the abrasive mass flow rate and standoff

distance (Siores et al 1996).

The cut is aimed is to have both a non through cut and a through cut .Three main

process parameters for the experiment are:

1. Water Pressure, Pw

2. Nozzle traverse speed, Vt

3. Drilling Time, Td

4.2.1 Drilling experiment

A matrix experiment design approach was used to establish the relationship between the

process parameters. In the drilling experiment, 4x6 matrixes were used and therefore a

set of 24 experiments were obtained. Table 4.1 depicts the parameters tested during the

drilling experiment.

Process Parameters Value

Water Pressure (MPa) 200,250,300,350

Drilling Time (seconds) 5,10,15,20,25,30

Separation distance (mm)

4

Table 4.1: List of independent parameters in drilling experiment

The maximum pressure capacity the pump can handle is 400 MPa. With regards to the

safety and current condition of the pump, the maximum pressure that was used for the

58

experiment was 350 MPa (safety factor of 0.875) while minimum pressure was 200

MPa. 5 second time interval drilling times was used during the drilling process.

The separation distance between each cut was set to be at 4 mm to optimize the usage of

the work piece economically. It also help to maintain the hole profile without distorting

the shape if they were too close to each other due to the erratic nature of deflecting

abrasive particles.

Each hole was drilled at approximately 4 mm from the edge of the work piece and the

nozzle moves at a rapid speed of 15 mm/s from the edge before it stops and starts to

drill the work piece. This will eliminate the effect of traverse speed in this experiment.

Positioning the point to drill at the edge of 4 mm from the work piece allows flexibility

on the machining process. The nozzle does not have to move across the span of the

work piece which may damage the hole profile or may generate unnecessary acoustic

emission signal as figure 4.6 tries to explain.

Figure 4.6: Movement of nozzle for drilling experiment

Nozzle direction

Hole profile

59

As illustrated in figure 4.6, before the cut starts, the nozzle will be placed approximately

1 mm from the edge of the workpiece. The nozzle then moves inward before it stops for

drilling process and retracts in the same axis before proceeding to the next cut. The

result from the cut will be shown in the next chapter.

4.2.2 Traverse cut experiment

In the drilling experiment, 4x6 matrixes were used and therefore a set of 24 experiments

were obtained. However due to time constraint and limitation of the machine, 4x5

matrixes were used to establish relationship between parameters in the traverse cut

experiment. Table 4.2 depicts the parameters tested for traverse cut.

Process Parameters Value

Water Pressure (MPa) 200,250,300,350

Traverse Cut (mm/s) 2,4,6,8,10

Separation distance (mm) 4 Table 4.2: List of independent parameters in traverse cut experiment

In the drilling experiment, the exposure time for the abrasive particles impinged onto

the work piece was denoted by the drilling time, Td. In the traverse cut, the traverse

speed can be best represented by the exposure time for abrasive particles to impinge

onto the work piece. This is because the nozzle moves slower at low traverse speed than

at high traverse speed. At low traverse speed, more particles are impacting the work

piece and therefore long exposure time than at high traverse speed which gives short

exposure time.

Identical to the drilling experiment, the separation distance between each cut was set to

be at 4 mm to optimize the usage of the work piece economically. It also helps to

maintain the kerf profile without distorting it if they are too close to each other.

60

The nozzle cuts in the traverse direction and it stops after it has moved 30 mm from the

starting point as figure 4.7 tries to explain.

Figure 4.7: Movement of nozzle for traverse cut experiment

As illustrated in figure 4.7, before the cut starts, the nozzle will be placed approximately

1 mm from the edge of the work piece. The nozzle will then move inwards for cut to

occur until it reaches the end of the work piece before the water jet stops. Then, it will

retract at high speed in the same axis before proceeds to the next cut. The result from

the cut will be shown in the next chapter.

Nozzle direction

Kerf profile

61

4.2.3 Constant experiment parameters

Parameter Value Abrasive material Garnet Abrasive mesh size #80 Abrasive particle shape Angular (random) AWJ orifice material Sapphire Mixing nozzle diameter (mm) 0.762 Mixing nozzle length (mm) 76.2 Method of feed Suction Condition of abrasive Dry Angle of jet 90˚ Abrasive flow rate 5.5 g/s Stand-off distance 3 mm

Table 4.3: List of constant parameters

Table 4.3 depicts the constant parameters that were used for both experiments. The

abrasive material used was garnet with mesh size of #80 which has an average particle

size diameter of 180µm .Ojmertz (1997) reported that milling steel with garnet resulted

in the highest material removal rate, followed by aluminium oxide which was 40%

lower and glass beads which was 50% lower. The stand-off distance is defined as the

distance from the nozzle tip to the work piece. It is set at 3 mm because Shipway et al

(2004, p.124) suggested that the stand-off distance between 2 – 5 mm gives the material

removal rate to be relatively insensitive, and is at maximum in this region

Increase in abrasive flow rate may increase in material removal rate. Fowler et al (2009)

stated that abrasive flow rate has an optimum value which when exceed it will have a

decline effect on the material removal rate. Thus is caused by the crowdedness of

abrasive particles in the cut that may cause ineffectiveness of material removal process.

Moreover, high abrasive flow rate may cause the abrasive tube to be blocked by the

62

cumulated abrasive particles. 5.5 g/s was selected to minimize these effects and to have

a linear relationship with the material removal rate.

4.2.4 Material properties

Parameter Value Material Titanium Alloy (Ti-Al4-V6) Material width (mm) 30 Material thickness (mm) 30 Material length (mm) 100 mm Tensile strength (MPa) 1000 Elastic Modulus (GPa) 114 Density (g/cm3) 4.42 Specific Heat ( J/kg.°C ) 560 Vickers hardness 330 kgf mm-2

Table 4.4: List of material properties for titanium alloy

Table 4.4 depicts the material properties used for the experiment.

4.3 Methods to measure physical quantities

In the experiments, several physical quantities were interested to be measured. The

physical quantities are:

1. Acoustic Emission Root Mean Square Value, AErms

2. Drilling depth, Hd

3. Drilling hole diameter, Dh

4. Depth of cut for traverse cut, Htc

63

4.3.1 AErms Value

To measure the AErms value, the Acoustic Emission (AE) data acquisition instruments

were used. Table 4.5 depicts the parameter and equipment used:

Parameter Value Sampling frequency 2500 Hz Sensor type KISTLER AE-Piezotron Type

8152B Coupler type KISTLER AE-Piezotron Type

5125B Table 4.5: List of data acquisition specifications

AErms is a low transient wave and thus, low sampling frequency is sufficient. From the

manufacturer itself (KISTLER), the sampling frequency of the measuring instruments

can be calculated as follows:

fg = 1/(3*τ)

The sampling frequency fa is then fa > 2*fg

Where τ is the time constant and fg is gross frequency. Time constant set for the sensor

is 1.2 ms. Therefore, sampling frequency > 278 Hz. 2500 Hz was selected to acquire

more data than 278Hz. This will improve data accuracy because the each data is

captured in every 0.4 millisecond rather than 3.6 millisecond. (John 2007)

4.3.2 Drilling hole diameter

The diameter of the drilling profile was measured by a high technology shadowgraph

VERTEX 420 machine. It can magnify the image of the hole profile and has a

measuring software to measure the hole with the precision of micrometre. The

instrument can also capture photo of the profile diameter and save it in JPEG format.

64

Figure 4.8:VERTEX 420 profile machine

4.3.3 Drilling depth

The drilling depth was measured using a modified Mitutoyo digital calliper attached

with fine steel needle as shown in figure 4.9. The measurement was repeated 3 times to

find the average and minimize random error.

4.3.4 Traverse cut depth of cut

The depth of cut was measured using a modified Mitutoyo digital calliper attached with

fine steel needle in figure 4.9. Measurements were taken at 5mm spacings along the

track. The workpiece width is 30 mm and therefore, the depth of cut was measured at 5,

10,15,20,25 and 30 mm length. The average of these measurements represents the depth

of cut.

65

Figure 4.9: Mitutoyo digital calliper

Figure 4.10: Acoustic Emission Piezotron Coupler type 5125B

The coupler has integrated RMS converter and limit switch for processing high-

frequency AE-signals from KISTLER AE-sensors (John 2006).

66

4.4 Problems encountered during the experiment

Problems that encountered might have had influenced the results. However, once

identified, actions have been taken to minimize these errors for data reliability and

accuracy. Some of the problems were random while some were experimental.

1. High abrasive particles flow rate might have blocked the output tube, leading to

no abrasive-water mixture that caused no material removal process during the

cutting due to the absence of abrasive particles. The abrasive flow rate was

therefore lowered down from 9.8 g/s to 5.5 g/s. The abrasive tube was checked

and cleaned regularly (once after 5 runs) to see if any blockage in the abrasive

line present.

2. There were two operators; one operated the water jet machine and the data

acquisition. Therefore, it was difficult to have identical start up time since both

processes could not be done simultaneously. Although both operators have tried

to start simultaneously, from the data, the start-up time for the data capturing

varies. To minimize this effect, the equations have been modified which then

will be discussed in the next chapter.

3. There was no proper tool to measure the depth of cut due to small hole diameter

and kerf width. This is due to the orifice diameter that was only available in the

lab. The smallest hole diameter obtained is 1.2mm and the back end of

conventional vernier calliper could not fit the hole. Thus, the digital vernier

calliper was modified by fitting with a small fine needle.

4. The waterjet pressure was difficult to keep constant during the whole experiment

due to the sudden relief of the pressure pump. It was observed that after each

experiment, the pressure gauge decreases from the initial value.

67

Chapter 5: Results and Discussion on Cutting

Experiments

68

5.1 Drilling result

Figure 5.1: Drilling result

Figure 5.1 illustrates the result on the titanium alloy after drilling experiment. From this

result, they are two physical quantities that are interested to be measured which is the

hole diameter and the drilling depth.

69

5.1.1 Drilling profile

200 MPa

(a) (b)

(c) (d)

(e) (f)

Figure 5.2: Drilling profile in various drilling time. (a) 5 sec (b)10 sec (c)15 sec (d)20 sec (e)25 sec (f)30 sec

70

Pressure Figure Drilling Time

(s)

Hole Diameter

(mm)

Depth (mm)

Roundness

200 MPa 1 (a) 5 1.2849 3.11 0.0612

1 (b) 10 1.4645 4.88 0.0311

1 (c) 15 1.5237 5.74 0.0266

1 (d) 20 1.5576 6.96 0.0325

1 (e) 25 1.6007 8.13 0.0405

1 (f) 30 1.6311 9.06 0.0221


(s)

Hole Diameter

(mm)

Depth (mm)

Roundness

250 MPa 2 (a) 5 1.3378 4.1 0.015

2 (b) 10 1.4858 5.66 0.0491

2 (c) 15 1.5674 7.85 0.0433

2 (d) 20 1.6131 8.75 0.0432

2 (e) 25 1.6264 10.55 0.0412

2 (f) 30 1.6471 11.26 0.0324


(s)

Hole Diameter

(mm)

Depth (mm)

Roundness

300 MPa 3 (a) 5 1.3552 4.46 0.0487

3 (b) 10 1.5123 6.7 0.0434

3 (c) 15 1.5875 8.89 0.0412

3 (d) 20 1.6354 10.89 0.0432

3 (e) 25 1.6654 12.77 0.0507

3 (f) 30 1.7049 13.45 0.0427


(s)

Hole Diameter

(mm)

Depth (mm)

Roundness

350 MPa 4 (a) 5 1.4102 4.92 0.0822

4 (b) 10 1.5327 7.42 0.0431

4 (c) 15 1.6097 10.22 0.0448

4 (d) 20 1.6441 12.83 0.0365

4 (e) 25 1.6964 13.91 0.0313

4 (f) 30 1.7343 14.92 0.0389

Table 5.1: Measurement for drilling results

71

5.1.2 Effects of drilling time on hole diameter

Figure 5.3: Hole diameter versus drilling time (200 MPa)


5 10 15 20 25 30

1.25

1.3

1.35

1.4

1.45

1.5

1.55

1.6

1.65

1.7

Drilling Time (seconds)

Hol

e D

iam

eter

(m

m)

Hole Diameter vs Drilling Time (200 MPa)

200 MPafit 1Prediction bounds (fit 1)

5 10 15 20 25 30

1.3

1.35

1.4

1.45

1.5

1.55

1.6

1.65

1.7

1.75


Hol

e D

iam

eter

(m

m)



Dh = 1.132Td0.1145

R-square: 0.9671 Bounds:±0.0722 mm

Dh = 1.072Td0.1255


72



5 10 15 20 25 30

1.35

1.4

1.45

1.5

1.55

1.6

1.65

1.7

1.75


Hol

e D

iam

eter

(m

m)



5 10 15 20 25 30

1.4

1.45

1.5

1.55

1.6

1.65

1.7

1.75


Hol

e D

iam

eter

(m

m)



Dh = 1.125Td0.1238

R-square: 0.9876 Bounds:±0.0479mm

Dh = 1.177Td0.1135

R-square: 0.9971 Bounds:±0.0219mm

73

Figure 5.7: Hole diameter versus drilling Time in all pressures

Figure 5.2 shows the drilling profile at 200 MPa. The drilling profile for other pressures

are shown in the Appendix B

Based on the visual observation in figure 5.2, as the drilling time increases, the hole

diameter increases. All profiles are captured in in an identical scale of 0.5mm.

To validate these observations, the actual hole diameter has been measured for each

respective time. The data in Table 5.1 illustrate the result and graphs have been plotted

to verify the observation. The relationship between the hole diameter and drilling time

is then developed.

Figure 5.3-5.6 displays the relationship between hole diameter and drilling time at each

respective pressure. Where Dh is hole diameter and Td is drilling time. The graphs

exhibit consistent trend which is a non-linear relationship. The data has been fitted with

power equation and the confidence interval is set to be at 95%. The best fit line shows a

0 5 10 15 20 25 30 351.3

1.35

1.4

1.45

1.5

1.55

1.6

1.65

1.7

1.75Overall Drilling Hole Diameter vs Drilling Time


Hol

e D

iam

eter

(m

m)

200 MPa250 MPa300 MPa350 MPa

74

regression value of 0.9702, 0.9671, 0.9876 and 0.9971. The hole diameter increases as

time increases but in the decreasing rate.

Figure 5.7 compares the hole diameter with regards to the pressure. The result obtained

is expected. Increase in the pressure will increase the hole diameter. This result is

consistent with the result obtained by the previous studies done by H Orbanic and

Junkar (2004, p.506) in drilling blind hole using water jet.

Figure 5.8: Hole formation in AWJ drilling

Abrasive particles are responsible for the erosion process. During the drilling process, a

cavity is formed. A non-through cut resembles the energy to be sufficient to cut at a

certain depth.

High speed abrasive particles will be rebounded when the particles hit the bottom of the

cut, along with water and work piece particles to produce rebounded slurry. This

phenomenon is called the backflow. Due to the size of small cavity and pressure

variation in the hole, turbulence is formed at the bottom region of the hole.

Impinging Jet. (Abrasive + Water)

Rebounded slurry (Abrasive + water + work piece particles)

Turbulence region

75

Figure 5.8 displays the formation of the hole in AWJ drilling.The deflection of abrasive

particles and workpiece material may remove some of the material to provide the

secondary cutting process. Therefore, as drilling time increases, the exposure time for

the rebounded slurry to remove the material at the hole wall increases. Thus increasing

the hole diameter.

The hole profile resembles a conical shape due to the backflow of the rebounded slurry

caused by deflection. Less material is removed on the hole wall at the bottom of the cut

due to the presence of turbulence.

If it is a through cut, then the entry and exit hole will have a similar diameter, indicating

that no deflection is occurred that may cause the tapered shape of the drilling profile.

Increase the pressure will increase the kinetic energy for waterjet stream. This will

therefore, increases the speed of the abrasive particles. Thus, as pressure increases, hole

diameter increases significantly as displayed in figure 5.7

The next sub-chapter will discuss about the effect of drilling time and pump pressure to

the drilling depth

76

5.1.3 Effects of drilling time on drilling depth

Figure 5.9: Drilling depth versus drilling time (200 MPa)


5 10 15 20 25 30

3

4

5

6

7

8

9


Dril

ling

Dep

th (

mm

)

Drilling Depth vs Drilling Time (200 MPa)

200 MPafit 1Prediction bounds (exponential fit)

5 10 15 20 25 303

4

5

6

7

8

9

10

11

12


Dril

ling

Dep

th (

mm

)



Hd = 1.188Td0.6332


Hd = 1.538Td0.6189


77



5 10 15 20 25 30

4

6

8

10

12

14


Dril

ling

Dep

th (

mm

)



5 10 15 20 25 305

6

7

8

9

10

11

12

13

14

15


Dril

ling

Dep

th (

mm

)



Hd = 1.607Td0.5951


Hd = 1.884Td0.5892


78

Figure 5.13: Drilling depth versus drilling time in all pressures

Figure 5.9 – 5.12 displays the relationship between the drilling depth and the drilling

time. The data is best fitted with power equation, thus giving a regression value of

0.9956, 0.9908, 0.9932, and 0.9848 respectively. The power equation used to fit this

data is given by:

Hd = a(Td )k

Where Hd is the drilling depth, a and k are constants, while Td is the drilling time. The

value for a increases when pressure increases. On the other hand, the value for k

decreases when pressure increases. This can be observed in table 5.2.

Pressure (MPa) a-value k-value 200 1.188 0.6332 250 1.538 0.6189 300 1.607 0.5951 350 1.884 0.5892

Table 5.2: Drilling time constants

0 5 10 15 20 25 30 352

4

6

8

10

12

14

16Overall Drilling Depth vs Drilling Time


Dril

ling

Dep

th (

mm

)


79

5.1.4 Threshold pressure for drilling experiment

Figure 5.14: Threshold pressure for drilling

Figure 5.14 shows a relationship between the drilling depth and the applied pressure in

various traverse speed. The depth of cut increases as pressure increases and the

relationship is best fitted with second order polynomial. Threshold pressure is observed

from the intersection of the polynomial to the pressure axis. Range of pressure is

estimated between 80-90 MPa. The observation of threshold pressure is supported by

previous study done by Momber (2001, p.29). He suggested that the threshold pressure

describes a critical particle velocity to introduce the material removal process.

0 25 50 75 100 125 150 175 200 225 250 275 300 325 3503500

2

4

6

8

10

12

14

16

Applied Pump Pressure (MPa)

Dril

ling

Dep

th (

mm

)

20 sec25 sec30 sec

Threshold pressure

80

From table 5.1, it is observed that when drilling time increases, the drilling depth

increases but in a decreasing rate. This may due to three reasons. The first reason may

be due to the backflow of the rebounded slurry. Rebounded slurry has less energy than

the impact abrasive waterjet mixture. Therefore, the rate of material removal process of

rebounded slurry is less than the impact waterjet. The second reason may be due to the

turbulence at the bottom of the hole cavity. This turbulence might cause abrasive

particles to be inefficient in removing the material. Not all particles will impact the

bottom of the hole as some might disperse in the turbulence. The third reason as

suggested by Kovacevic (1998), which is due to the turbulence and deflection the newly

exposed work piece material in the hole is subjected to fresh AWJ mixture impingement

and therefore the local energy of the jet is almost the same.

Figure 5.13 compares the results with regards to different pressures. As expected, for

each respective time, the drilling depth increases as pressure increases. This shows

similar trend like hole diameter. The reason for this phenomena is identical to the hole

diameter. As pressure increases, kinetic energy of water jet increases and therefore

increases abrasive velocity and abrasive impact rate. More abrasive particles are

impacting the work piece. Therefore, material removal rate increases.

81

5.2 Traverse Cut Result

Figure 5.15: Traverse cut result

Figure 5.15 illustrates the result for traverse cut. From this result, two interested

physical quantities to be observed and measured are kerf profile and the depth of cut.

82

5.2.1 Kerf profile

Figure 5.16: Kerf profile for selected traverse speed (a) Front (b) Back

Figure 5.16 shows the kerf profile for the traverse cut at the entry stage for 200 MPa.

Visually, three distinct observations are found. Firstly the shape of kerf profile is almost

identical to one another. Secondly the depth of cut increases as traverse speed decreases.

10 mm/s

6 mm/s

4 mm/s

8 mm/s

10 mm/s

8 mm/s

6 mm/s

83

Thirdly, the depth of cut for the back profile (exit stage) is shallower than the front

profile (entry stage)

The kerf profiles have some unique characteristics. It has a wider entry and the width

decreases as jet cuts into the material so that a kerf taper is produced. At the bottom of

the cut, a pocket is generated. This may due to the jet deflection and the plastic

deformation, therefore the cutting direction is deflecting upward and sideways in this

region. The slower the traverse speed, the longer the exposure time, and the more

distinct is the shape of the pocket. This pocket is called the jet upward deflecting zone.

Hashish (1988) observed the cutting process along the jet traverse direction. He found

that there are three stages in the process; an entry stage, a cutting stage, and an exit

stage. In each stage, different cutting mechanisms are developed until the maximum

depth of is reached. The penetration process will come to an optimum level especially in

the cutting stage and a large jet upward deflection occurs, therefore the variation of kerf

depths is formed along the cut.

This theory is then validated by measuring the depth of cut of the workpiece. It is

expected that the depth of cut varies along the cut; therefore 6 measurements were taken

to observe the trend. The sampling distance used for the measurement was 5 mm and

the initial measurement was taken from the entry point, denoted at 0 mm.

84

5.2.2 Relationship between cut-off distance and depth of cut

Figure 5.17: Depth of Cut vs Cut-Off Distance (200 MPa)


0 5 10 15 20 25 301

2

3

4

5

6

7

8

9

10

11Depth of Cut vs Cut-Off Distance (200 MPa)

Cut-Off Distance (mm)

Dep

th o

f Cut

(m

m)

2 mm/s4 mm/s6 mm/s8 mm/s10 mm/s

0 5 10 15 20 25 300

2

4

6

8

10

12

14

16



Dep

th o

f Cut

(m

m)


85

Figure 5.19 : Depth of Cut vs Cut-Off Distance (300 MPa)


From figure 5.17-5.20, it is observed that there is a slight variation in the depth of cut

along the cut. It is interesting to note that for most experiment, the depth of cut is

approximately constant from the entry level to cut-off distance of 20 mm. However, it is

0 5 10 15 20 25 302

4

6

8

10

12

14

16

18

20



Dep

th o

f Cut

(m

m)


0 5 10 15 20 25 300

5

10

15

20

25

Depth of Cut vs Cut-Off Distance (350 MPa)


Dep

th o

f Cut

(m

m)


86

distinct that the depth of cut at the exit stage is the shallowest. This phenomenon can be

explained by Hashish (1988) three cutting stages model in figure 1.9.

At the exit stage where the process comes to an end, an occurrence of jet forward

deflection is due to the transition in the cutting media; from solid to air. Therefore, this

phenomenon is associated with the formation of uncut triangle. The top of the triangle

marks the depth at which the cutting wear zone ends.

Figure 5.21: Kerf bottom profile

Visual observation based on figure 5.21 justifies that the depth of cut varies along the

cut. High depth of cut reflects less light than small depth of cut. This can be observed

through the formation of bumps and shadows at the bottom of the cut. Some of the

abrasive particles are also embed at the bottom of the cut.

Another interesting observation is the depth of cut variation is minimal as traverse cut

increases. In figure 5.20, for traverse speed 2 mm/s, the difference between depth of cut

at the exit level and entry level is 13.38 mm. On the other hand, for traverse speed 10

mm/s, the difference is 0.96. It can be illustrated in figure 5.22.

87

(a) (b)

Figure 5.22: Kerf profile for different traverse speed under constant pressure

To explain this phenomenon, Fowler (2004, p.412) commented that deep cut is formed

at low traverse speed and much of the cutting is performed at low angles of attack on

the leading edge of the kerf face. Small depth of cut is observed at high speeds and the

leading edge of the kerf face makes a very small proportion of cutting area.

The rate of erosion process is caused by the number of abrasive particles impinging the

surface. Since less abrasive particles are impacting the surface at high traverse speed,

the energy that is dissipated for material removal process is low, resulting small depth

of cut. It is expected in this region, the variation of depth is less significant due to low

energy dissipated to remove the material.

Because of the nature of water jet cutting that it deflects when it hits the workpiece, the

energy may vary along the cutting profile. High energy leads to high variation in the

depth of cut while low energy leads to less variation in the depth of cut.

This explanation is justified by the result. In 10mm/s traverse speed, the depth of cut

from the initial cut to 25 mm is reasonably constant with a slight variation especially at

cutting length of 25 mm as compared to the depth of cut in 2mm/s traverse speed.

Jet Direction

Kerf Profile Kerf Profile

Va > Vb

88

Another observation is the influence of waterjet pressure to the depth of cut. Increase in

the waterjet pressure will increase the depth of cut.

The justification is similar to the drilling experiment. When then waterjet pressure

increases, it increases the water jet velocity, therefore increasing abrave particles

velocity that were carried by water. Combination of high kinetic energy and momentum

will cause a massive impact rate. Thus, these combinations will increase the material

removal rate; therefore the depth of cut.

From the experiment, it is evident that at cut-off distance of 30 mm, the depth of cut is

significantly the lowest due to the forward deflection of waterjet. Thus, it may not

represent the actual effect of the waterjet energy in material removal mechanism. It is

assumed that the effective depth of cut may be associated by taking the average depth of

cut, excluding the value at the exit stage.

89

5.2.3 Effects of traverse speed on the depth of cut

Figure 5.23: Average depth of cut versus traverse speed (200 MPa)


2 3 4 5 6 7 8 9 102

3

4

5

6

7

8

9

10

Traverse Speed (mm/s)

Ave

rage

Dep

th o

f Cut

(m

m)

Average Depth of Cut vs Traverse Speed (200 MPa)

200 MPafit 1

2 3 4 5 6 7 8 9 10

4

6

8

10

12

14

16


Ave

rage

Dep

th o

f Cut

(m

m)


250 MPafit 1

Htc = 15.5 e-0.2047 Vt

R-square: 0.9664

Htc = 23.42e-0.1988Vt

R-square: 0.9922

90



2 3 4 5 6 7 8 9 104

6

8

10

12

14

16

18

20


Ave

rage

Dep

th o

f Cut

(m

m)


300 MPafit 1

2 3 4 5 6 7 8 9 10

6

8

10

12

14

16

18

20

22


Ave

rage

Dep

th o

f Cut

(m

m)


350 MPafit 1

Htc = 29.4e-0.1951Vt

R-square: 0.9771

Htc = 33.19e-0.1876Vt

R-square: 0.9893

91

Figure 5.27: Average depth of cut versus traverse speed in all pressures

Based on the previous sub chapter, it was observed that there is a significant variation in

the depth of cut especially at the end of the workpiece. Therefore, to correlate between

the effective AErms and the depth of cut, the measurement at 30 mm cut-off distance will

be excluded. Effective AErms here is referring to the AErms when actual drilling happens.

Figure 5.23-5.26 depicts the effects of traverse speed on the depth of cut. Each figure

shows an identical pattern at which the depth of cut decreases as traverse speed

increases.

An exponential fit has been used to get the line of best fit and the regression value for

each relationship is 0.9664, 0.9922, 0.9771, and 0.9893. These values show how well

future outcome are likely to be predicted.

1 2 3 4 5 6 7 8 9 10 110

5

10

15

20

25Overall Average Depth of Cut vs Traverse Speed


Ave

rage

Dep

th o

f Cut

(m

m)


92

The exponential equation used to fit this data is

H tc = ae-kVt

Where Htc is the average depth of cut, Vt is the traverse speed, a and k are both

constants. The value for a increases when pressure increases. On the other hand, the

value for k decreases when pressure increases. This can be observed in table 5.3.

Pressure (MPa) a-value k-value 200 15.5 0.2047 250 23.42 0.1988 300 29.4 0.1951 350 33.19 0.1876

Table 5.3: Traverse speed constants

These expected results can be explained by the mechanics of abrasive waterjet particle

in waterjet stream. Traverse speed can influence the exposure time for abrasive particles

impingement onto the material. Waterjet stream carries the abrasive particles, thus at

low traverse speed, the exposure time is longer than at high traverse speed. Long

exposure time causes more abrasive particles to impact the workpiece, therefore

increase material removal rate and the depth of cut. This is the reason why the depth of

cut decreases when the traverse speed increases.

Figure 5.27 explains the effect of waterjet pressure to the depth of cut. The results are

identical with the drilling experiment at which the average depth of cut increases when

the pressure increases.

93

5.2.4 Threshold pressure for traverse cut

Figure 5.28: Threshold pressure for traverse cut

Figure 5.28 shows a relationship between the depth of cut and the applied pressure in

various traverse speed. The depth of cut increases as pressure increases and the

relationship is best fitted with second order polynomial. Threshold pressure is observed

from the intersection of the polynomial to the pressure axis. Range of pressure is

estimated between 125 – 140 MPa. The observation of threshold pressure is supported

by previous study done by Momber (2001, p.29). He suggested that the threshold

pressure describes a critical particle velocity to introduce the material removal process.

However in the drilling process as explained in the previous chapter, the threshold

pressure ranges from 80-90 MPa which is less than the threshold pressure for traverse

cut.

0 25 50 75 100 125 150 175 200 225 250 275 300 325 35035002468

101214161820222426

Applied Pump Pressure (MPa)

Dep

th o

f C

ut (

mm

)

2 mm/s4 mm/s6 mm/s

Threshold pressure

94

5.3 Discussion

5.3.1 Effects of pressure on drilling depth and depth of Cut

The Bernoulli’s law of pressure constancy explains the influence of waterjet pressure

and water jet velocity (Momber et al 1999).

Vw = ��

Where Vw is the water jet velocity, μ is the momentum transfer coefficient, Pw is pump

pressure and �� is water density. The waterjet velocity is influenced by the pump

pressure whereas abrasive particles are affected by the waterjet velocity. Based on

Mohan et al (2002, p.399), the abrasive particle velocity can be approximated by

assuming a simple momentum balance in the mixing chamber. Neglecting the mass

flow rate of the air, the abrasive particle velocity can be expressed as:

Va = φ��

��(ṁ�ṁ�)

Where φ is the momentum transfer coefficient,ṁ� is abrasive mass flow rate and ṁ�

is water mass flow rate. Increase in the waterjet velocity will increase the abrasive

particles velocity. Therefore, applying the theory of kinetic energy, as abrasive particles

velocity increases, kinetic energy increases. This is the reason why increase in waterjet

pressure will have a significant increase in the depth of cut, as well as the AErms value.

95

The kinetic energy of the abrasive particles can be expressed as:

Ep = ��M pVA

2

Whre Mp is the abrasive mass .Nadeu et al. (1991) reported that the depth of cut is

directly proportional to the kinetic energy of the abrasive particles.

5.3.2 Effects of traverse speed on the depth of cut

Local exposure time is the duration taken for water jet to impact the work piece. It also

determines the duration for abrasive particles impingement on the work piece. Increase

in the local exposure time will increase the rate of abrasive particles impact because

more particles will impinge the work piece. Therefore, more materials will be removed

since abrasive particles are the dominant factor in the erosion process.

In the traverse cut experiment, the local exposure time was estimated by (Momber et al

1999). :

te = ��

Where tE is local exposure time, DF is focus diameter, VT is traverse speed. Increase in

the traverse speed will reduce the local exposure time; therefore less abrasive particles

will impact on the work piece. From the traverse cut experiment, it can be observed that

as the traverse speed increases, the depth of cut decreases due to short local exposure

time.

96

5.3.3 Effects of drilling time on drilling depth

From figure 5.9-5.12, the drilling depth increases when drilling time increases but in a

decreasing rate. Orbanic and Junkar (2004, p.506) state that high removal rate in the

early stage of drilling because material erode more when there are large impact angle of

abrasive grains. Drilling time can represent the local exposure time. Assuming that the

abrasive flow rate and water jet pressure are constant throughout the experiment, it is

observed that the depth of cut increases as the drilling time increases until it reaches a

maximum depth. This assumption is verified by Kovacecik et al (1998, p.51) when they

found constant material removal rate with progressive drilling time. They explain that

this could be due to the constant local energy of the impinging jet throughout the

drilling depth.

5.3.4 Effects of drilling time on hole diameter

From figure 5.3-5.6, hole diameter increases as drilling time increases. This may due to

the secondary cut provides by the water je slurry deflection once it impacts the bottom

of the hole. The deflected slurry is therefore has sufficient energy to remove material

from the hole wall as suggested by Kovacevic et al (1998, p.51).

97

Chapter 6: Results on Acoustic Emission

Monitoring

98

6.1 Acoustic Emission signal for drilling and traverse cut

(a)

(b)

Figure 6.1 : Raw AE signal for (a) drilling (b) traverse cut

In figure 6.1, the AE signal that was generated during the drilling and traverse cut is

primarily of the continuous type. This indicates that the material removal is due to the

trans granular fracture and plastic deformation, which is usually observed in

comparatively high strength and ductile material. This result is consistent with results

obtained by Kovacevic et al (1998, p.49) and Mohan et al (2002, p.402).

99

6.2 Acoustic Emission monitoring for drilling

(a)

(b)

Figure 6.2: AErms general pattern in drilling (a) 5 sec (b) 20 sec

Figure 6.2 shows the general trend of AErms signal under drilling condition. In both

experiments, the value for water jet pressure was 200 MPa. As soon as the water jet

moves rapidly to the drilling point, the AErms value increases. Then when the drilling

process occurs, AErms value decreases non-linearly with increasing drilling time. Graphs

for all pressures and drilling are displayed in the Appendix B.

100

From figure 6.2 there is one distinct observation in which the signal is captured at

different time level. In figure 6.2(a), the data was captured at time 5 seconds while in

figure 6.2(b), the data was captured at time 9 seconds. Thus, it is difficult to compare

and understand both graphs in a consistent time frame, making the analysis process to

be less accurate. The reason for this is because it is difficult to operate the water jet

machine and to start the data capturing simultaneously as two operators were involved

in the experiment. The experiment set up discussed in the previous chapter may explain

this problem. Further research is needed to find ways how to execute these operations

simultaneously.

To minimize this problem therefore, the raw data has been processed in the MATLAB

so that every process will be having a similar start time by shifting the graph so that we

can assume at time 0 second is where the water hits the workpiece and signal is

captured. This is crucial for further analysis in this chapter.

The processed data in MATLAB is displayed and discussed in the next sub chapter.

101

6.2.1 Effects of drilling time on AErms signal

(a)

(b)

Figure 6.3: AErms in various drilling time (a) 5, 10, 15 sec (b) 20, 25, 30 sec

0 5 10 15 20 25 30 350

1

2

3AErms vs Drilling Time (200 MPa)

0 5 10 15 20 25 30 350

0.5

1

1.5

2

2.5

AE

rms

(vol

tage

)

0 5 10 15 20 25 30 350

0.5

1

1.5

2


5 sec

10 sec

15 sec

0 5 10 15 20 25 30 350

0.5

1

1.5

2AErms vs Drilling Time (200 MPa)

0 5 10 15 20 25 30 350

0.5

1

1.5

AE

rms

(vol

tage

)

0 5 10 15 20 25 30 350

0.5

1

1.5


20 sec

25 sec

30 sec

102

Figure 6.3 illustrates the relationship between the AErms value and the drilling time for

200 MPa. It shows that when the drilling time increases, the value for AErms decreases

exponentially with a decreasing rate. However, it can be seen that for each set of drilling

time, when it reaches 25 seconds the decreasing rate is minimal and the graph starts to

become ‘flat’ horizontally. This may because the process has reached an optimal depth.

Figure 6.4: Effects of drilling time on AErms

The effects of drilling time on AErms are represented in figure 6.4. It is expected the

longer the drilling time, the longer is the extension of the graph. It is observed that the

AErms value varies with the drilling time. As the drilling time increases, the AErms value

decreases. AErms variation is between 1 – 1.5 V which may be significant due to the

sensitivity of the sensor. The depth of cut is therefore increases. Thus, from the result

the drilling time slightly affects the AErms value.

0 5 10 15 20 25 30 350

0.5

1

1.5

2

2.5Overall AErms vs Drilling time (200 MPa)

Drilling time

AE

rms

(vol

tage

)

5 sec10 sec15 sec20 sec25 sec30 sec

103

6.2.2 Effects of Pressure on AErms

In order to compare the influence of waterjet pressure to the AErms value in the drilling

experiment, the graph has been modified so that a set of data with identical time but

different pressures can be represented in one axis as illustrated in figure 6.5.

(a)

(b)

Figure 6.5: Effects of pressure on AErms

0 1 2 3 4 5 60

0.5

1

1.5

2

2.5Overall AErms vs Drilling time (5 sec)

Drilling time (seconds)

AE

rms

(vol

tage

)


0 2 4 6 8 10 120

0.5

1

1.5

2



AE

rms

(vol

tage

)


104

In both graphs, it is observed that pressure 200 MPa gives the highest amplitude of

AErms signal followed by 350 MPa, 250 MPa, and finally 300 MPa which is not in a

conventional order. AErms variation ranges between 1 – 2 V which is significant due to

the sensor sensitivity. This irregularity may because to the experimental set up or the

water jet backflow. However, it is obvious that pressure will also affect the AErms.

6.2.3 AErms rise time

Another critical observation to note from the graph is that the AErms value increases

rapidly to a certain maximum level and then decreases in a decreasing rate. This peak

was observed when the nozzle travels 4mm from the edge to the drill point. The signal

then decreases when the waterjet started to drill the workpiece. To validate this

hypothesis, further analyses were done on the graphs and correspond to the actual result.

Figure 6.6: Drilling point and traverse cut length

Traverse Cut Length

Drill Point

105

Figure 6.7: Graph magnification at AErms maximum amplitude

Using MATLAB, the average time taken for the signal to reach its maximum amplitude

is found to be at 0.4 seconds as explained in figure 6.7. This verifies the hypothesis

since the traverse speed that was used to move the nozzle to the drill point from the

edge was 10 mm/s. The traverse cut length is 4 mm and it takes approximately 0.4

seconds until it reaches the drill point. Another analysis to support this hypothesis is

illustrated in figure 6.8.

-6 -4 -2 0 2 4 6 8 100.8

1

1.2

1.4

1.6

1.8

2

X: 0.4436Y: 1.662

Overall AErms vs Drilling time (200 MPa)

Drilling time

AE

rms

(vol

tage

)


106

(a)

(b)

Figure 6.8: Time to rise for each drilling operation

Based on figure 6.8 (a), the time taken for AErms to reach its maximum amplitude (see x-

axis value) is 0.406 seconds. The drilling time is 5 seconds. Identically in figure 6.8 (b)

4 5 6 7 8 9 10 110

0.5

1

1.5

2

2.5

X: 5.376Y: 1.797


AE

rms

(vol

tage

)

X: 4.97Y: 0.0134

AErms vs Drilling time200 MPa

4 6 8 10 12 14 160

0.5

1

1.5

2

2.5

X: 4.883Y: 0.005571

X: 5.276Y: 1.564


AE

rms

(vol

tage

)

AErms vs Drilling time

200 MPa

107

where the drilling time is 10 seconds, the time taken for AErms to reach its maximum

amplitude is 0.393 seconds which is approximately 0.40 seconds.

6.3 Effective AErms for drilling

From this result, it is still uncertain why the traverse cut caused the AErms value to

increase rapidly and then decreases as drilling process starts. To understand the

phenomena from the microscopic and physics point of view, a relationship needs to

establish between the AErms value and the drilling depth.

It can be assumed that the actual drilling started when the AErms starts to decrease from

its maximum peak. The occurrence of a high impulse at the end of the drilling time in

the graph can be reasoned by the sudden stop of the waterjet after drilling time has

elapsed. Therefore, supported by previous hypothesis, it can be assumed that the

‘effective’ AErms signal; representing the actual drilling process is the curve of the graph

itself.

In the next analysis, the rise of the AErms to the highest amplitude at the beginning of

the drilling process and at the end of the drilling process will be removed as we are

interested only at the AErms during the material removal process. This is important to

understand the drilling phenomena and how AErms will affect the depth of cut.

As it provides effective measurement that represents the actual drilling process,

therefore the new graph will be denoted as the Effective Drilling AErms Graph.

108

6.3.1 Effects of drilling time on effective AErms

(a)

(b)

(c)

5 6 7 8 9 10 11 12 13 14 15

0.4

0.6

0.8

1

1.2

1.4

1.6


AE

rms

(vol

tage

)

Effective AErms vs Drilling Time (10 sec)


4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

0.5

1

1.5


AE

rms

(vol

tage

)



4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0.2

0.4

0.6

0.8

1

1.2


AE

rms

(vol

tage

)



Yd = 5.502e-0.3244Td+0.714e-0.04201Td R-square: 0.989 Bounds:±0.0631 v

Yd = 3.622e-0.3526Td+0.6722e-0.04195Td R-square: 0.989 Bounds:±0.0568 v

Yd =2.623e-0.3544Td +0.8825e-0.06Td R-square: 0.989 Bounds:±0.0521 v

109

(d)

(e) Figure 6.9: Effective AErms in various drilling time for 200 MPa (10 – 30 sec)

Figure 6.9 represents the effective AErms graph for pressure 200 MPa. The exponential

equation used to fit the data is:

Yd = ae-k(Td)+ be-j(Td)

Where Yd is the AErms, Td is drilling time, while a,b,k and j are constants. However,

there is one major problem with this equation. If we refer to the figure 6.9, the initial

start-up time varies from 4 to 5 seconds. This may due to the difficulties in operating

both water jet and data acquisition machine simultaneously. Although time is relative, it

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

0.2

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5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

0.1

0.2

0.3

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0.5

0.6

0.7

0.8

0.9

1

1.1


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Yd =2.774e-0.3021Td+0.7705e-0.05484Td R-square: 0.989 Bounds:±0.0483 v

Yd =2.587e-0.2719Td +0.6103e-0.05013Td R-square: 0.991 Bounds:±0.0417 v

110

is unfeasible to analyse the effect of drilling time as the magnitude for relative time of

drilling may be higher than the drilling time itself!

6.3.2 Modified MATLAB equations

For example, in figure 6.9 (f), although the drilling time for the operation is 30 seconds

but since the data acquisition starts at 5 seconds, to get the AErms value at time 30

seconds we need to refer at time 35 seconds.

Therefore, x values from the time range between 0<x<30 seconds in the equation cannot

be used because each equation is fitted in various time range.

Thus, the equation needs to be modified so that the equation may represent the initial

cut to be at time 0 when the water jet starts to drill. This allows us to standardize the

graph and use the time range between 0<x<∞ seconds. In other words, the graph has to

be shifted so that at time 0, the amplitude is at maximum. Since less material is removed

at this point, the highest amplitude of the graph may be the perfect representation at

time 0.

To do this, the equation needs to be written in a shifting form:

Yd = ae-f(Td+C1)

Where a is constant, f is the rate of rupture and C is the time shift. The value of C can be

determined from the exponential fit graph when AErms value is at maximum.

111

The equation can be expanded into:

Yd = (ae-fC)e-f(Td)

For equation that has two terms:

Yd = ae-f(Td+C1) + de-g(Td+C

2)

The equation then can be expanded into:

Yd = (ae-fC1)e

-fTd + (de-gTd)e-gC2

Where Yd is AErms, a and d are constants, f and g are rate of rupture while C1and C2 are

time shift. List of equations are displayed in Appendix C. The modified equation now is

more feasible to be used for calculation. All of the equations are fitted for value x>0.

This new equation is used to calculate the AErms value at selected drilling time. Drilling

depth has been measured and data are tabulated.

112


(s) Min AErms (V)

Depth (mm)

200 MPa 18 (a) 5 0.668 3.11

18 (b) 10 0.417 4.88

18 (c) 15 0.307 5.74

18 (d) 20 0.208 6.96

18 (e) 25 0.149 8.13

18 (f) 30 0.105 9.06

Pressure

Figure Drilling Time

(s) Min AErms (V)

Depth (mm)

250 MPa 19 (a) 5 0.516 4.1

19 (b) 10 0.332 5.66

19 (c) 15 0.227 7.85

19 (d) 20 0.172 8.75

19 (e) 25 0.157 10.55

19 (f) 30 0.081 11.26


(s) Min AErms (V)

Depth (mm)

300 MPa 20 (a) 5 0.588 4.46

20 (b) 10 0.378 6.7

20 (c) 15 0.236 8.89

20 (d) 20 0.191 10.89

20 (e) 25 0.178 12.77

20 (f) 30 0.161 13.45


(s) Min AErms (V)

Depth (mm)

350 MPa 21 (a) 5 0.695 4.92

21 (b) 10 0.377 7.42

21 (c) 15 0.282 10.22

21 (d) 20 0.22 12.83

21 (e) 25 0.173 13.91

21 (f) 30 0.156 14.92

Table 6.1: Drilling depth and AErms measurement results

113

6.4 Effects of drilling depth on AErms

Figure 6.10: AErms versus drilling depth (200MPa)


4 5 6 7 8 9

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Drilling Depth (mm)

AE

rms

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AErms vs Drilling Depth (200 MPa)

200 MPaexponential fitPrediction bounds (exponential fit)

5 6 7 8 9 10 11

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0.6

Drilling Depth (mm)

AE

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Yd = 1.707e-0.2984Hd R-square: 0.9975 Bounds : ±0.0356v


114



5 6 7 8 9 10 11 12 13

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0.2

0.3

0.4

0.5

0.6

0.7

Drilling Depth (mm)

AE

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AErms vs Driling Depth (mm)


5 6 7 8 9 10 11 12 13 14 150

0.1

0.2

0.3

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Drilling Depth (mm)

AE

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115

Figure 6.14: AErms versus drilling depth in all pressures

Figure 6.10 – 6.13 illustrate the relationship between AErms value and the drilling depth.

The AErms decreases with increasing depth. An exponential fit has been used to get the

line of best fit. Regression values show how well future outcome are likely to be

predicted. The fit used is:

Yd = ae-k(Hd)

Where Yd is the AErms value, a and k are constants, while Hd is the depth of cut. The

value for k decreases when pressure increases. On the other hand, the value for constant

a decreases from 200 to 300 MPa then increases at 350 MPa. There might be an

optimum pressure between these ranges.

Pressure (MPa) a-value k-value 200 1.707 -0.2984 250 1.255 -0.2225 300 1.196 -0.1658 350 1.43 -0.1569

Table 6.2: Value for constants in AErms drilling equation

4 6 8 10 12 14 160

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8Overall AErms vs Depth of Cut

Drilling Depth (mm)

AE

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116

6.5 Acoustic Emission monitoring for traverse cut

Figure 6.15: AErms general pattern in traverse cut experiment.

6.5.1 Effects of traverse speed on AErms

Figure 6.16: Effects of traverse speed on AErms

Figure 6.16 illustrates effects of traverse speed on AErms. It can be observed that the

trend is almost identical to another except that when the traverse speed increases, the

cutting time range decreases and the general shape becomes more distinct. This can be

represented by the sudden rise of AErms when water jet hits the material. Roughly, the

AErms value in the steady state condition seems to be identical. Only a slight difference

0 2 4 6 8 10 12 14 160

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Cutting Time (seconds)

AE

rms

(V)

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1

1.2

1.4Overall AErms vs Time (300 MPa)

Time (seconds)

AE

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2mm/s4mm/s6mm/s8mm/s10mm/s

117

is observed between 0.3 – 0.6 V. Thus, traverse speed does not significantly influence

the AErms value.

200 MPa

(a)

(b)

Figure 6.17: AErms versus cutting time

0 2 4 6 8 10 12 14 16 180

0.5

1

1.5AErms vs Cutting Time (200 MPa)

0 2 4 6 8 10 12 14 16 180

0.5

1

1.5

AE

rms

(vol

tage

)

0 2 4 6 8 10 12 14 16 180

0.5

1

1.5


2 mm/s

4 mm/s

6 mm/s

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1

1.2


AE

rms

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0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1

1.2


8 mm/s

10 mm/s

10 mm/s

8 mm/s

6 mm/s

4 mm/s

2 mm/s

118

Figure 6.17 displays the trend of AErms voltage for 200 MPa when traverse speed varies.

Using the same method in drilling, the raw data has been modified in MATLAB so that

all results may have identical start time. It is assumed that at time 0 second is time

where the waterjet starts to cut the workpiece.

6.5.2 AErms to identify three stages cutting process

In figure 6.18, the signal rises at the beginning of the cut before starts to maintain in a

steady state value. There is a large increase at the end of the graph before it decreases

rapidly to nearly 0. Similar results in graph trend have also been observed by Axinte

and Kong (2009, p.305) to monitor AWJ cutting process of titanium alloy.

Figure 6.18: Three stages of cutting represented by AErms

This shows that the AErms value can be a promising tool to associate Hashish’s three

stages of cutting process in the traverse cut (Hashish 1988). This result agrees very well

0 2 4 6 8 10 12 14 16 180

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Entry stage

Developed cutting stage

Exit stage

119

with the results obtained by Mohan et al (1994) when monitoring the depth of cut using

acoustic emission technique. They also stated that the result agrees with the result of

high speed camera observation in cutting transparent materials by Hashish (1988).

During the entry stage, the AErms starts to build up to reach a certain value before it

starts to stabilize. In this stage, when the jet enters the workpiece, the cutting is

accomplished by erosion at shallow angle of impact, therefore causing an increase in

depth. Acoustic Emission is a transient elastic wave that is caused by sudden material

stress. The formation of crack when water hits the material releases the elastic transient

wave in a certain rate until it reaches a stability point, where at this stage the depth of

cut reaches its maximum. In the developed cutting stage, Hashish (1988) argues that the

penetration process is fully developed and therefore; there high possibility for a steady

state interface in the cutting wear zone. The stable trend of AErms signal may associate

with this steady state process. No significant variation from the graph is observed within

this stage.

At the exit stage, Hashish (1988) stated that a forward waterjet deflection will occur to

form the uncut triangle. This interference causes instability in the waterjet condition.

Thus, a significant peak is observed at the end of the cutting process, indicating that the

waterjet may have exit completely from the workpiece. At time 6 seconds, the graph

shows a unique characteristic at which an upward curve trend is observed. This may

associate with the formation of the waterjet forward deflection that causes the

disturbance in the AErms signal.

The rise of AErms at the beginning of cut supported the observation made in the previous

drilling experiment at which the AErms increases until it reaches the drilling point before

start to decrease when drilling process takes place.

120

6.5.3 Effects of pressure on AErms

(a)

(b)

Figure 6.19: Effects of pressure on AErms (a) 6 mm/s (b) 10 mm/s

Figure 6.19 displays the effects of waterjet pressure on AErms. As the pressure increases,

the AErms value decreases. The occurrence is consistent with the results obtained in the

drilling experiment. In the drilling experiment, when pressure increases the AErms value

also decreases.

0 1 2 3 4 5 6 7 80

0.2

0.4

0.6

0.8

1

1.2

1.4Overall AErms vs Time (6 mm/s)

Time (seconds)

AE

rms

(Vol

tage

)


0 1 2 3 40

0.2

0.4

0.6

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1

1.2


Time (seconds)

AE

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121

Increase in the waterjet pressure will increase the energy for the material removal

mechanism. Thus, this will increase in the depth of cut. It is expected that the depth of

cut increases as the pressure increases; therefore we can observe the AErms value

decreases as waterjet pressure increases.

Another observation to note is the profile of AErms graph when traverse speed increases.

It is observed that for 200 MPa at high traverse speed, there is a sudden rise in the signal

and it drops drastically before it starts to stabilize at the entry stage.

Further analysis has been done to understand the association between AErms signal and

the traverse cut process. In the next analysis, the cutting time has been converted into

cutting length.

6.6 AErms with respect to the cutting length

(a)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 300

0.2

0.4

0.6

0.8

1

Cutting Length (mm)

AE

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AErms vs Cutting Length (4 mm/s)


122

(b)

Figure 6.20: AErms versus cutting length

Figure 6.20 illustrates the relationship between the AErms voltage and the cutting length.

The cutting length can be obtained by the equation:

Cutting length (mm) = nozzle traverse speed (mm/s) x cutting time(s)

It is assumed that the initial cut occurs when the AErms signal starts to rise (entry stage),

indicating that the water jet penetrates the work piece. The exit level can be represented

by the high peak at the end of the graph which consistent and occurs in all experiments.

The cutting length was set to 30 mm for all experiments.

From the drilling experiment, we have established a concept that when the depth of cut

decreases, the AErms value increases. Therefore, theoretically this phenomena leads to a

hypothesis that at cutting length of 25 mm, the depth of cut is shallower than the

previous cutting length. In other words, the material removal rate starts to decrease at 25

mm cutting length. To validate this hypothesis, the depth of cut for the cutting process is

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 300

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Cutting Length (mm)

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123

measured at a distance interval of 5 mm that spans the width of the work piece. The

results are then tabulated in the table 6.2.

Pressure Cut-off Distance

(mm) Traverse Speed (mm/s)

2 4 6 8 10

5 10.79 5.82 4.85 3.20 2.47 D

epth of Cut (m

m)

10 10.70 5.84 4.71 3.18 2.46

200 MPa

15 10.67 5.85 4.64 3.2 2.42

20 10.75 5.85 4.72 3.17 2.41

25 7.58 4.96 3.64 2.92 2.36

30 4.77 2.63 2.15 1.75 1.52

Table 6.3: Depth of cut measurements

From table 6.2, the results show that the depth of cut is considerably identical from the

entry zone to 20 mm cut-off distance. In fact, the depth of cut is similar at 24 mm cut-

off distance. However, at 25 mm cut-off distance the depth of cut gets shallower and at

the exit zone, the depth of cut is the shallowest. Thus, this result verifies the previous

hypothesis. The AErms signal can be a good representation of the depth of cut.

The AErms signal for traverse speed of 10mm/s shows a constant trend from 5 mm

length to 26 mm length which may represent the depth of cut. At 30 mm length, the

signal increases indication that the rate of material removal decreases, which then

therefore proven by the measured depth of cut.

From this result, it can be validated quantitatively that AErms is a promising tool to

monitor the cutting stages and depth of cut for traverse cut process. Given that the

cutting time can be converted into the cutting length, the result shows that the deviation

in the AErms signal is consistent with the decrement in the depth of cut at particular

distance. It also supports Hashish’s three stages of cutting process.

124

. 6.7 Effective AErms for traverse cut

Previously, it was argued that due to the large variation in the depth of cut between the

entry and the exit stage, therefore it cannot be included to represent the average depth

value for the experiment. This excludes the depth at the exit stage. Another reason is

due to the fact that the variation occurs due to the deflection of the waterjet when it exits

the workpiece. To understand how AErms behaves in the steady material removal state,

only data obtained in the developed cutting process will be used in this analysis.

Thus, in the analysis, MATLAB has been used to remove the signal at the exit cutting

stage to observe only the entry stage and the developed cutting stage. The data was

fitted with an exponential equation to get the line of best fit. This equation is then can

be used to calculate the effective average AErms value for the traverse cut process.

It is obvious that the variation of the AErms value is going to be a challenge in order to

correlate the correct AErms value with respect to the depth of cut. This challenge might

due to the high frequency sampling rate of 2500 Hz that will cause rapid oscillating

trend.

To overcome this problem, the modified graph will be fitted with line of the best fit to

find the equation. From the equation, calculation is then performed to find the AErms

value which then corresponds to the depth of cut.

125

6.7.1 Effective AErms in various pressures

2 mm/s

(a)

(b)

0 2 4 6 8 10 120

0.1

0.2

0.3

0.4

0.5

0.6


AE

rms

(vol

tage

)AErms vs Cutting Time (2 mm/s)


0 2 4 6 8 10 120

0.1

0.2

0.3

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0.7

0.8


AE

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AErms vs Cutting Time (2 mm/s)


126

(c)

(d)

Figure 6.21: Effective AErms vs cutting time at 2 mm/s (a) 200 MPa (b) 250 MPa (c) 300 MPa (d) 350 MPa

0 2 4 6 8 10 12 140

0.1

0.2

0.3

0.4

0.5

0.6


AE

rms(

volta

ge)



0 2 4 6 8 10 12 140

0.1

0.2

0.3

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0.5

0.6

0.7

0.8


AE

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AErms voltage vs Cutting Time (2 mm/s)


127

10 mm/s

(a)

(b)

0 0.5 1 1.5 2 2.50

0.1

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0 0.5 1 1.5 20

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Arm

s (v

olta

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128

(c)

(d)

Figure 6.22: Effective AErms versus cutting time at 10 mm/s (a) 200 MPa (b) 250 MPa (c) 300 MPa (d) 350 MPa

0 0.5 1 1.5 20

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129

Measurement Results

Table 6.4: Average depth of cut and AErms measurement results

Pressure Figure Traverse Speed (mm/s)

Min AErms (V)

Average Depth (mm)


Min AErms (V)

Average Depth (mm)

200 MPa (a) 2 0.403 10.73 250 MPa (a) 2 0.335 16.14 (b) 4 0.445 5.84 (b) 4 0.352 10.12

(c) 6 0.502 4.73 (c) 6 0.371 7.31 (d) 8 0.523 3.18 (d) 8 0.392 5.02

(e) 10 0.545 2.44 (e) 10 0.412 3.65


Min AErms (V)

Average Depth (mm)


Min AErms (V)

Average Depth (mm)

300 MPa (a) 2 0.317 20.38 350 MPa (a) 2 0.2191 23.15

(b) 4 0.338 12.31 (b) 4 0.2398 15.33 (c) 6 0.351 8.10 (c) 6 0.2452 10.12

(d) 8 0.369 6.54 (d) 8 0.2527 7.19

(e) 10 0.381 5.11 (e) 10 0.2675 6.26

130

6.8 Effects of AErms on depth of cut.

Figure 6.23: AErms versus depth of cut (200 MPa)


3 4 5 6 7 8 9 10

0.42

0.44

0.46

0.48

0.5

0.52

0.54

Depth of Cut (mm)

AE

rms

(vol

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AErms vs Depth of Cut (200 MPa)

200 MPafit 2

4 6 8 10 12 14 16

0.34

0.35

0.36

0.37

0.38

0.39

0.4

0.41

Depth of Cut (mm)

AE

rms

(vol

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)


250 MPafit 1

Yt = 0.664Htc-0.2084

R-square: 0.9464

Yt = 0.4936Htc-0.1423

R-square: 0.9947

131



5 10 15 20

0.32

0.33

0.34

0.35

0.36

0.37

0.38

Depth of Cut (mm)

AE

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300 MPafit 1

8 10 12 14 16 18 20 220.22

0.225

0.23

0.235

0.24

0.245

0.25

0.255

0.26

0.265

Depth of Cut (mm)

AE

rms

(vol

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)


350 MPafit 1

Yt = 0.4708Htc-0.1326

R-square: 0.9834

Yt = 0.3341Htc-0.1304

R-square: 0.9244

132

Figure 6.27: AErms versus depth of cut in all pressures

Figure 6.23-6.26 illustrates the relationship between AErms and the depth of cut. In

general, the trend for each figure is identical. When the depth of cut increases, the value

for AErms decreases. This result is conflicting with the previous result obtained by

Hassan et al (2003) at which they found positive relationship between AErms and the

depth of cut. However, results conducted by Kovacevic et al (1998) states that AErms

decreases as depth of cut increases.

The data has been fitted with a power equation that gives a promising regression value

of 0.9464, 0.9947, 0.9834, and 0.9244. The power equation can be denoted as :

Yt = aHtck

Where Yt is AErms voltage, Htc is the depth of cut, a and k are both constants. The value

for constant a and k depends on the pressure. It is obvious that as waterjet pressure

increases, a and k value decreases in terms of magnitude. This may indicate that

waterjet pressure to have a dominant effect in AErms value.

0 5 10 15 20 250.2

0.3

0.4

0.5

0.6

0.7Overall AErms vs Depth of Cut

Depth of Cut (mm)

AE

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133

Pressure (MPa) a-value k-value 200 0.664 -0.2084 250 0.4936 -0.1423 300 0.4708 -0.1326 350 0.3341 -0.1304

Table 6.5: Value for constants in traverse cut AErms equation

Another observation to note is from figure 6.27. It is obvious that as the waterjet

pressure increases, the AErms value decreases. This trend is also observed in the drilling

experiment at which increase in the drilling depth decreases the AErms value.

Figure 6.27 shows a minimal variation in the AErms value between 250 MPa and 300

MPa. Large variation in AErms value is observed between 200 MPa and 350 MPa.

Further research is needed to investigate the cause for this occurrence.

So far, the cutting process for drilling and traverse cut is restrained from through cut.

This is because in the previous experiments, it was aimed to observe the gradual change

in terms of the depth of cut. Therefore, the cutting parameters were selected to achieve

the objective.

The next sub chapter will discuss about the AErms monitoring result when through cut

was performed, both for drilling and traverse cut. In the drilling, two experiments were

done while in the traverse cut, only one experiment was done.

134

Chapter 7: AErms in Through Cut and Process

Anomalies

135

7.1 Drilling

(a)

(b)

Figure 7.1: AErms in through cut for drilling (a) Trial 1 (b) Trial 2

0 10 20 30 40 50 60 70 80 900

0.2

0.4

0.6

0.8

1


AE

rms

(vol

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AErms vs Drilling Time (Trial 1)

350 MPa

0 10 20 30 40 50 60 700

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


AE

rms

(vol

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AErms vs Drilling Time (Trial 2)

350 MPa

Waterjet stops

Through cut occur

Through cut occur

Waterjet stops

136

(a)

(b)

Figure 7.2: Through cut drilling point and traverse length (a) Trial 1 (b) Trial 2

Traverse length

Drilling point

Drilling point

Traverse length

137

(a)

(b)

(a)

(b)

Figure 7.3: Hole profile for through cut (a) Trial 1 (b) Trial 2

Entry Exit

Entry

Exit

1 mm scale 0.5 mm scale

0.5 mm scale 0.5 mm scale

138

(a)

(a)

Figure 7.4: Effective AErms versus drilling time for through cut (a) Trial 1 (b) Trial 2

10 20 30 40 50 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9


AE

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Effective AErms vs Drilling Time (Trial 1)


5 10 15 20 25 30 35 40 45

0.1

0.2

0.3

0.4

0.5

0.6

0.7


AE

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Effective AErms vs Drilling Time (Trial 2)


139

7.1.1 Drilling observation

For both drilling experiments, the waterjet pressure used was 350 MPa. However the

difference is the distance of drilling point from the edge of the workpiece. In figure

7.2(a), the hole was drilled 2mm from the edge of the workpiece, indicated by the

traverse length and the traverse speed used to move the nozzle to the drilling point was

100 mm/s.

In figure 7.2(b), the hole was drilled 24 mm from the edge of the workpiece, indicated

by the traverse length and the traverse speed used to move the nozzle to the drilling

point was 10 mm/s

Figure 7.3 shows the drilling profile when the through cut was performed,

corresponding to trial 1 and trial 2 respectively. The hole profile at the entry face and

exit face is observed to be identical in trial 1 but in trial 2, the hole profile at exit face

has an ellipse shape which is slightly different from the entry face.

This irregularity may due to the retraction of waterjet after the drill through was

performed. Since it was moving in a slow traverse speed, the deflection might occur at

the hole wall, thus creating turbulence and formed the ellipse shape.

140

7.2 Traverse cut

(a)

(b)

Figure 7.5: AErms in through cut for traverse cut

0 20 40 60 80 100 120 140 160 1800

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08


AE

rms

(vol

tage

)

AErms vs Cutting Time (350 MPa)

0 20 40 60 80 100 1200

0.01

0.02

0.03

0.04

0.05


AE

rms

(vol

tage

)

Effective AErms vs Cutting Time (350 MPa)

350 MPafit 1Pred bnds (fit 1)

Exit stage Waterjet after exit stage

Waterjet stops

Entry stage

141

7.2.1 Traverse cut observation

Figure 7.5 displays the AErms trend when through cut was performed in the traverse cut.

The parameters are 0.25 mm/s and 350 MPa. It can be seen that when the water jet hit

the workpiece, through cut was performed. This can be identified by the small value of

AErms which is 0.0094 v. If we compare to the previous traverse cut experiment, at 2

mm/s and 350 MPa which produce the deepest depth of cut , the AErms value is 0.2191

v. The value for AErms when through cut is 24 times smaller than the AErms for the

deepest cut in the previous experiment. The value is so small and can be regarded as 0.

However, from figure 7.5 (a) it can be seen that when the waterjet stops at 160 seconds,

the AErms value reaches 0 completely.

A high peak is obtained at time 140 seconds and it can be seen that after 120 seconds

the signal increases in a parabolic function. This observation is similar to the previous

traverse cut experiment, indicating that the waterjet has reached the exit stage. The high

peak corresponds to the forward deflection of waterjet that normally occurs at the exit

stage.

7.3 AErms for machine condition monitoring

The ability of AErms to associate with the drilling and traverse cut operation leads to

another area of study, which is machine condition monitoring. In reality, to achieve

100% of machine efficiency for all operations is difficult. Wear and tear, fatigue, both

are natural phenomena that may affect the performance of the waterjet machine. One of

the challenges is to monitor the machine performance during the machining process.

Inability to detect any anomalies during the machining process may cause a lot of

damages to the industry especially if the workpiece is valuable in terms of cost or

142

integrity. Online condition monitoring is a method to monitor machining process in the

real time and AErms provides a promising result in this area.

Two experiments to observe the effect of AErms monitoring were done for drilling and

traverse cut. The only modification made in these experiments was the abrasive flow

rate was shut in the middle of machining process. Therefore, no abrasive particle

impingement was occurred in the middle of the cutting process. This is to simulate the

abnormalities that might exist in the abrasive waterjet cutting process.

One of the major problems in the abrasive waterjet machining is the blockage of the

abrasive flow tube that hinders the flow of abrasive. Abrasive particles are responsible

for the erosion and material removal process. Without the abrasive particles, erosion

rate is minimal. Therefore waterjet is unable to cut through difficult-to-cut material and

may cause damage onto the surface integrity. This may cause losses to the industry in

values of money, time, and efficiency.

7.3.1 Drilling Anomalies

Figure 7.6: AErms for drilling anomalies

0 5 10 15 20 25 30 35 400

0.5

1

1.5

2

2.5

3

3.5

4

4.5


AE

rms

(vol

tage

)

AErms vs Drilling Time

Abrasive flow stops

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7.3.2 Traverse Cut Anomalies

Figure 7.7: AErms for traverse cut anomalies

Figure 7.6 depicts the anomalies in drilling process. In this process the abrasive flow

rate was stopped at drilling time 23 seconds. It can be seen that there is a sudden jump

for AErms value. The waterjet was stopped at time 34 seconds

Figure 7.7 illustrates the anomalies in traverse cut process. In this process the abrasive

flow rate was stopped at cutting time 40 seconds. From the graph, it is obvious that as

soon as the abrasive flow stopped, the AErms value increases to a certain stabilizing

point. The waterjet was stopped at time 80 seconds

From the visual results for traverse cut, there was minimal penetration that occurs at the

point where the abrasive flow was shut due to the absence of abrasive particles.

On the other hand, for drilling the AErms trend increases then slightly decreases even

after the abrasive flow rate was shut. This phenomenon indicates as if the material was

being cut. This may be due to the presence of small volume of abrasive particles that

left in the cavity from the previous process. Some of the abrasive particles might have

0 10 20 30 40 50 60 70 80 90 1000

0.2

0.4

0.6

0.8

1


AE

rms

(vol

tage

)

AErms vs Cutting Time

Abrasive flow stops

144

already presence in the hole. However, due to the small volume of abrasive particles,

the material removal rate is very minimal. These phenomena are consistent with the

previous observations in which as less material is being removed, the higher value for

AErms signal obtained.

Figure 7.8: Through cut profile

Figure 7.8 illustrates the result when through cut was performed on the material. From

the result, striation mark is observed at the end of the cut indicating the forward

deflection of the water during cutting process.

Striation mark

145

Chapter 8: Discussion on AErms monitoring

146

8.1 The relationship between AErms and the depth of cut

For traverse cut, the relationship is best fitted with the power equation. On the other

hand for drilling, it is best fitted with the exponential equation. In general, both

equations show that AErms decreases when depth of cut increases in the decreasing rate.

The impingement of abrasive particles onto the titanium causes the molecules in the

titanium alloy to vibrate due to the applied stress. According to the Hooke’s law,

material under stress will undergo elastic first and later plastic deformation before it

enters into the fracture region as illustrated in figure 61(Physical Acoustic Corporation

2007). Momber (2001, p.23) states that there is a similar stress-strain relationship in

relation to the hydro abrasive erosion process.

Figure 8.1: Hooke’s law for ductile material

As explained in the literature review, the material removal process for ductile material is

dominated by plastic deformation, caused by the erosion of the material by the abrasive

particles.

147

At the point of fracture, the material is experiencing a high magnitude of stress and

strain. When titanium can no longer withstand maximum stress that is higher than the

Ultimate Tensile Stress (UTS), plastic deformation ends with fracture that will release

elastic strain energy. When a loaded material undergoes plastic deformation, atomic

planes slip past each other through dislocation. The deformations will therefore, release

energy in the form of elastic wave that travels through the material. The stress levels

present in the front of the crack tip will be several times higher than surrounding area

when cracks occur (Physical Acoustic Corporation 2007).

The plastic deformation at the surface of the material is at the maximum, therefore there

must be a threshold energy that is needed to cause the fracture to occur (Momber 2001).

The presence of threshold pressure in drilling and traverse cut experiment may support

this hypothesis. This may be the reason why at the beginning of cut, or at point when

the material is about to cut, high AErms was observed due to the release in high elastic

strain energy. Once the fracture was formed, the titanium has losses its toughness

because fracture reduces material strength.

Kovacevic el al (1998, p.52) found that material removal rate and the depth penetration

are related to the energy absorbed during the impingement of water jet. The amount of

energy absorbed increases as the depth of cut increases. Mohan et al (2002, p.399)

suggested that AWJ input energy is the sum of dissipated energy and the exit energy.

Dissipated energy is energy dissipated through mechanisms of material fracture and

damping effect. Exit energy is energy carried by the slurry after erosion.

148

Matsuoka et al (1993) found experimentally a square root relationship between the

AErms and the dissipated energy as described in the equation.

AErms α ��

The reason for high AE amplitude for small depth of cut is because less energy is used

to remove the material. Therefore most of the energy is dissipated to the sensor in the

form of vibration as suggested by Kovacevic et al (1998). Studies done by Kong and

Axinte (2009) shows that AErms can be a representation to detect the input energy from

the nozzle, the utilised energy for material removal process, and idle energy that escapes

from the process

Therefore, based on these studies, it may be associated to the result for the decrease in

AErms signal when the depth of cut increases, both in drilling and traverse cut. Most

results are quite consistent with previous studies despite Hassan et al (2003) found that

the AErms increases as the depth of cut increases.

The limitation in this project is that, there is no proper experiment done to quantify the

amount of energy absorbed and released during the material removal process. No

further signal processing was done on the AE signal like PSD, FFT, or ARMA analysis.

Therefore, further investigation is needed especially to correlate between AErms and the

energy dissipated as Mohan et al (2002) had perform studies to control energy

dissipation in AWJ using quantitative acoustic emission technique.

149

8.2 AErms in the drilling Process

In the drilling process, high energy is observed when the water jet starts to impinge the

material at the drilling point due to the formation of the fracture that releases high

elastic strain energy caused by the plastic deformation. Because drilling involves a

stationary cutting process, the rate of depth of cut decreases as drilling time increases

until it reaches a maximum depth. The drilling has reached its maximum depth when the

steady state AErms is observed with minimal variation. The backflow can also be the

reason for the exponential decrease in AErms value as drilling time increases as reported

by Kovacevic et al (1998).

When the drilling reaches a through cut as explained in figure 59 (a), the AErms

decreases significantly. Only small value of AErms is detected and the acoustic emission

may come from other sources like the vibration or the surroundings, not from the elastic

strain energy from the material removal process.

8.2.1 Effect of drilling parameters on the AErms

8.2.1.1 Constant pressure, various drilling time

Result shows that the AErms increases as drilling time decreases. The variation is

however minimal. The value for AErms variation is between 1 – 1.5 under pump

pressure of 200 MPa. Thus, drilling time slightly affects the AErms

8.2.1.2 Constant drilling time, various pressures

Result shows that the AErms varies when pressure changes. However it is not in a

conventional order. The trend shows that pressure 200 MPa gives the highest amplitude

of AErms signal followed by 350 MPa, 250 MPa, and finally 300 MPa. This irregularity

150

may be due to the presence of optimum pressure or due to any disturbance that occur

during the experimental set up.

8.3 Traverse Cut Process

In the traverse cut process, three stages of cut suggested by Hashish (1988) have been

observed both visually and by AErms; the entry, developed, and exit stages. It is obvious

that the formation of peak at the entry stage is significant when the traverse cut

increases. This may due to high elastic strain energy released when depth of cut is low

at the beginning of the cut. In the developed stage, a much more stable AErms value is

observed because of the steady state cutting process.

However, at the exit stage, a high peak is observed. This may due to the forward

deflection of the waterjet, hence cause less material removed that result in shallow depth

of cut. Furthermore, when water jet moves from titanium alloy to air the stress is

suddenly released. This may also contribute to the high peak formation at the exit stage.

When the traverse cut reaches a through cut, it is observed that there is no significant

change in the trend before the waterjet hits the workpiece and when it hits the

workpiece. The abrasive material may have had cut the material through as they

managed to overcome the ultimate tensile strength when it hits the workpiece.

Therefore, virtually minimal strain energy is released.

151

8.3.1 Effect of traverse cut parameters on AErms

8.3.1.1 Constant traverse speed, various pressures

Result shows that AErms value decreases as the pressure increases. High pressure will

cause high depth of cut. Therefore based on the observation, AErms decreases when the

depth of cut increases which is consistent with previous studies. It is also observed that

the signal pattern is more distinct when the traverse speed increases.

8.3.1.2 Constant pressure, various traverse speed

Result shows that there is no significant difference in terms of AErms amplitude between

graphs. The trend looks almost identical to each other. However the signal pattern is

extended due to the increase in the exposure time.

8.3.2 Relationship between AErms and cutting length

Result shows that, when the signal in time domain is converted into the length domain

by multiplying sampling time with traverse speed, the graph can represent the pattern

for the AErms with respect to the actual cutting length. This shows a representation for

the kerf depth along cut. In the developed cutting stage, constant signal indicates

constant depth of cut which is in fact validated by the actual measurement in table 6.2.

In the exit cutting stage where jet forward deflection is expected to occur, the AErms

increases drastically. Thus this indicates that less material removal process and therefore

shallow depth of cut. It is validated by the actual measurement in table 6.2. The cutting

distance at which the entry stage, the developed cutting stage, and the exit stage occur

during the cut can be identified from this concept which is phenomenal.

152

8.4 Anomalies events

Figure 7.6 and figure 7.7 illustrate the trend of AErms during the anomalies events. The

abrasive waterjet was stopped in the middle of cutting process. One identical

observation from both figures is the increment in the AErms value once the abrasive

particles were stopped from impacting the workpiece, causing only pure waterjet to

impact. The same observation has been made by Kovacevic et al (1998).

Abrasive particles are responsible for the erosion mechanism. Once the flow of the

abrasive particles was stopped, the material removal rate now became minimal. Due to

this, the rate of depth of cut decreases and therefore, the area of impact was in a very

high stress and strain state. From the observation, it was found that there was presence

of left over abrasive particles in the drilling hole and the cut that may have cause

material removal process to occur although the abrasive particles flow was stopped.

This may be the reason why the AErms signal increases then slightly decreases after the

time when the abrasive particles flow was stopped.

153

Chapter 9: Conclusion and Future Work

154

From the results, all objectives for this project have been successfully achieved. Useful

conclusions from the investigation are explained below.

9.1 Depth of cut profile

In both experiments, two main parameters that have dominant influence in the depth of

cut were investigated which are pressure and exposure time. For drilling, the exposure

time is represented by drilling time while for traverse cut the exposure time is

represented by traverse speed.

Both experiments exhibit that, the depth of cut increases as the pressure increases. This

is because abrasive particles gain more kinetic energy; therefore when the particles hit

the workpiece, more material will be removed. Abrasive particles are dominant in the

erosion process. Another identical observation is, the longer the exposure time, the

higher the depth of cut. This is because more abrasive particles are impacting the work

piece per time, causing high impact rate. Thus more energy is used for cutting process.

9.1.1 Drilling

In the drilling process, the penetration rate decreases when the drilling time increases.

This is probably due to the effect of water back flow that reduces the water velocity,

thus slowing the abrasive particle velocity and therefore minimizing the secondary

cutting process. The trend shows that the slurry deflection removes more material from

the wall of the hole, rather than the hole itself, producing a taper hole. The hole

diameter increases as drilling time increases. This suggests that the waterjet backflow

has sufficient energy to remove material from titanium alloy. This is also known as

secondary cutting.

155

9.1.2 Traverse cut

In traverse cut, the depth of cut obtained at the exit is shallower than the one at the

entry. This is due to the water jet forward deflection that creates uncut triangle (Hashish

1988). The variance in the depth of cut justifies the existence of three stages of cutting

process. Round pocket observed at the end of the kerf profile is due to water jet

deflection and plastic deformation. Thus, titanium alloy undergoes trans granular

fracture. This is as expected since titanium alloy is a ductile material. The presence of

threshold pressure in the drilling process is supported by similar findings from Momber

(2001). The range is between 80 – 90 MPa which is smaller than threshold pressure for

traverse cut which is ranged between 125 – 140 MPa.

9.2 AErms Monitoring

AErms is a promising tool for online monitoring the depth of cut for AWJ drilling and

traverse cut process. In the drilling process, AErms is able to detect the maximum depth

of cut obtained by certain parameter settings. This can be represented when the AErms

signal starts to level and not continuously decreasing. Steady state AErms signal within a

constant range is observed. Furthermore, AErms is also able to detect if through cut is

occurred. This can be represented when there is a sudden drop of AErms to zero value.

As the drilling time increases, it will slightly decrease the AErms value. Pressure also

affects the AErms value. Results shows that 200 MPa gives the highest AErms value

follows by 350 MPa, 250 MPa, and 300 MPa.

In the traverse cut, AErms is able to represent the three stages of cutting suggested by

Hashish (1988) .Sudden AErms rise at the beginning of cut resembles the entry stage.

Steady state AErms resembles the developed cutting stage while the increase in AErms at

the end of the graph represents the exit stage. The trend for AErms in traverse cut is

156

different from drilling because in traverse cut, dynamic cutting is performed at which

the nozzle moves in the traverse direction while in drilling, stationary cutting is

performed. Maximum depth of cut is represented by the steady state AErms that occurs

in the developed cutting stage. The AErms value decreases significantly as the pressure

increases. Traverse speed however does not significantly affect the AErms value. The

higher the traverse speed, the more distinct is the pattern of the graph.

The relationship between AErms and the depth of cut is established for each type of the

experiment as shown in table 9.1. The AErms value decreases when the depth of cut

increases.

Pressure Drilling Traverse Cut

200 MPa Yd = 1.707e-0.2984Hd Yt = 0.664Htc-0.2084

250 MPa Yd = 1.255e-0.2225Hd Yt = 0.4936Htc-0.1423

300 MPa Yd = 1.196e-0.1658Hd Yt = 0.4708Htc-0.1326

350 MPa Yd = 1.43e-0.1569Hd Yt = 0.3341Htc-0.1304

Table 9.1: Summarised equations for AErms drilling and traverse cut

Regression value for each equation shows a significant relationship between two

variables that fit the data. The trend does not show a linear trend as obtained by Hassan

et al (2003) because the rate of penetration decreases as exposure time increases.

Moreover, another factor that might explain the decreasing rate in penetration is the

damping effect and the jet backflow as suggested by Kovacevic (1998)

9.2.1 Through Cut

In the through cut process, both for drilling and traverse cut, the AErms signal decreases

rapidly to relatively zero when the through cut occur. Thus, AErms can be a promising

tool to detect any through cut events.

157

9.2.2 Anomalous Events

Whenever the abrasive flow is stopped during the cutting process, a sudden increase in

AErms is observed in both experiments which supported from the observation in the

previous studies. Thus, AErms is also a promising tool to detect anomalous events in

AWJ process.

9.3 Future Work

All thesis objectives have been achieved in the experiment. However there are still

opportunities and room for improvement in this research due to time constraints while

conducting this project.

1. Current relationship only exhibit two parameters which are AErms and depth of

cut. Moreover, the equation is corresponded to specific pressure, thus it is not

comprehensive. In the experiment, parameters such as pressure, drilling time,

and traverse speed were used to influence the depth of cut. Thus in future, it is

proposed that stochastic modelling and verifying need to be performed in order

to incorporate all the parameters into one function.

2. An online monitoring model and method with feedback control to be developed

based on the comprehensive model earlier. This will lead to the development of

fully automated AWJ process.

3. Extensive studies from the material point of view especially in the crack and

fracture mechanics need to be done to gain comprehensive understanding

between AErms and material removal process.

4. Current study only focuses on titanium alloy which is a ductile material. Thus in

future, more materials with different properties are recommended to investigate

158

so that comparison can be made as different material properties may have

different material removal mechanisms.

5. The study can be expanded by varying other parameters like the stand-off

distance, abrasive type, or nozzle impacting angle that may affect AErms value.

6. Further studies using acoustic emission are needed to quantify the energy input

from the nozzle, energy utilized for the material removal process, and energy

dissipated due to complex mechanisms in the process.

159

References

1. Arrazola,P.J., Garay,A.,Iriarte,L.M., Armendia.M., Marya,S., Maitre,F.L., 2008.

Journal of Materials Processing Technology: Machinability of Titanium Alloys

(Ti6AlV4 and Ti555.3)

2. Akkurt, A., Kulekci, M.K., Seker,U.,Ercan,F., 2004. Effect of feed rate on

surface roughness in abrasive waterjet cutting applications. In: Journal of

Materials Processing Technology 147, pp. 389 – 396

3. Axinte, D.A. and Kong, M.C., 2009. An integrated monitoring method to

supervise waterjet machining. In: CIRO Annals-Manufacturing Technology 58,

pp.303-306.

4. Bound,F.,Carpenter,C.,Folkes,J.,Shipway,P.H.,2010. Abrasive waterjet cutting

of titanium alloy : The influence of abrasive morphology and mechanical

properties on workpiece grit embedment and cut quality. In: Journal of Materials

Processing Technology 210 pp.2197 – 2205.

5. Blickwedel H., Guo N.S, Haferkamp H. , Louis H., 1990. Prediction of abrasive

jet cutting efficiency and quality. In: Proceedings of 10th International

Symposium on Jet Cutting Technology, the Netherlands, pp. 163-180

6. Bitter J.G.A, 1963. A study of erosion phenomena. In: Wear 6, pp. 5-21.

7. Basics of Acoustic Emission 2007. Physical Acoustic Corporation, MISTRAS

Holdings Company.

8. Dudzinski,D., Devillez,A., Moufki.A., Larrouquere,D., Zerrouki,V.,

Vigneau,J,2003. International Journal of Machine Tools and Manufacturer: A

review of developments towards dry and high speed machining of Inconel 718

alloy, 2003, pp.439-456.

160

9. Davis, J.R. 1990. ASM Metals Handbook, vol.2, 10th edition, Materials Park,

Ohio, USA, pp. 586-591.

10. Evans A.G.and Gulden W.E., 1978.Impact damage in brittle materials in the

elastic-plastic response regime.In: Proceedings. R. Soc. Lond. Series. A.

11. Finnie,I.,1958. The mechanism of erosion of ductile material. Proc. 3rd national

Congress of Applied Mechanics, ASME. In: Wang,J.,2003. Abrasive Water Jet

Machining of Engineering Materials. Trans Tech Publications Ltd, Switzerland.

12. Fowler, G., Pashby, I.R., Shipway, P.H., 2009. The effect of particle hardness

and shape when abrasive waterjet milling titanium alloy Ti6Al4V. In: Wear 266,

pp.613-620.

13. Fowler, G., Shipway, P.H., Pashby, I.R., 2004. Abrasive water-jet controlled

depth milling of Ti6Al4V alloy-an investigation of the role of jet-workpiece

traverse speed and abrasive grit size on the characteristics of the milled material.

In: Journal of Materials Processing Technology 161, pp.407-414.

14. Gent,M.,Menendez,M.,Torno,S.,Torano,J.,Schenk,A.,2012. Experimental

ecaluation of the physical properties required of abrasives for optimizing

waterjet cutting of ductile materials. In:WEAR 284-285, pp. 43-51.

15. Guo, Y.B.,Ammula, S.C.,2005. Real time acoustic monitoring foe surface

damage in hard machining. In: International Journal of Machine Tools &

Manufacture 45, pp.1622-1627.

16. Hashih, M.,1989. A model for abrasive-waterjet (AWJ) cutting, Trans. ASME,

In: Journal Engineering Material Technology.111, p.154.

17. Hassan, A.I., Chen, C.,Kovacevic, R., 2003. On-line monitoring of depth of cut

in AWJ cutting. In: International Journal of Machine Tools & Manufacture 44,

pp.595-605.

161

18. Hashihs, M., 1988. Visualization of the abrasive waterjet cutting process .

Engineering Mechanics, pp. 159-169.

19. Hlavac,M.L.,2008. Investigation of the abrasive waterjet trajectory curvature

inside the kerf, Journal of Materials Processing Technology 209 (2009) pp.4154-

4161.

20. Hascalik,A.,Caydas,U.,Gurun,H.,2007. Effect of traverse speed on abrasive

waterjet machining of Ti-6Al-4V alloy, Materials and Design 28,pp.1953-1957.

21. John, G., Dr.,2007. Piezotron® Acoustic Emission Sensor type 8152B. Kistler

Instrument Corporation.

22. John, G., Dr.,2006. Piezotron® Acoustic Emission Coupler type 5125B. Kistler

Instrument Corporation.

23. Kovacevic, R.,1992. Monitoring the depth of abrasive waterjet penetration.

In:International Journal Machining Tools Manufacturing. Vol. 32, pp.725-736.

24. Kovacevic, R.,Kwak, H-S.,Mohan, R.S.,1998. Acoustic emission sensing as a

tool for understanding the mechanisms of abrasive water jet drilling of difficult-

to-machine materials. In: Proceedings of Institution of Mechanical Engineers

Vol 212 Part B, pp.45 – 58.

25. Kulekei, M.K., 2002. Processes and apparatus developments in industrial

waterjet applications. In: international Journal of Machine Tools &

Manufacture.42, pp.1297-1306.

26. Momber, A., Mohan, R. and Kovacevic, R., 1995. Acoustic emission

measurements on brittle materials during abrasive water jet cutting. In : First

International Machining and Grinding Conference, paper MR 95-184, pp. 439-

458.

162

27. Momber, A.W.,Mohan, R.S.,Kovacevic, R., 1999. On-line analysis of hydro

abrasive erosion of pre-cracked materials by acoustic emission. In: Theoretical

and Applied Fracture Mechanics 31, pp.1-17.

28. Momber, A.W.,Mohan, R.S.,Kovacevic, R., 2002. Fracture range detection in

hydro-abrasive erosion of concrete. In: Wear 253, pp.1156-1164.

29. Momber, A.W. and Kovacevic, R.,1995. Quantification of energy absorption

capability in abrasive water jet machining. In: Institution of Mechanical

Engineers, Journal of Engineering Manufacture,209,pp.491-498.

30. Momber, A.W.,2001. Stress-strain relation for water-driven particle erosion of

quasi-brittle materials. In: Theoretical and Applied Fracture Mechanics 35, pp.

19-37.

31. Mohan, R.S., Momber, A.W., Kovacecic, R., 2002. Energy dissipation control in

hydro-abrasive machining using quantitative acoustic emission. Int J Adv Manuf

Technol 20, pp.397 – 406.

32. Mohan, R.S., Momber, A.W.,Kovacevic,R., 1994. Online Monitoring of Depth

AWK Penetration using Acoustic Emission Technique. In : The12th International

Conference on Jet Cutting Technology edited by Allen,N.G., BHR Group

Conference Series.13, pp.649 – 663.

33. Matsuoka, K.,Forrest,D.,Tse,M.K.,1993. On-line wear monitoring using acoustic

emission. In: Wear.162, pp.605-610.

34. Moiseyev, V.N., 2006. Titanium Alloys:Russian Aircraft and Aerospace

Application, Taylor & Francis, USA.

35. Matthew J.R and Hay D.R, Acoustic Emission Evaluation, Gordon and Breach

Science Publishers, Edited by James R. Matthews, 1983

163

36. Orbanic, H. and Junkar, M.,2004. An experimental study of drilling small and

deep blind holes with an abrasive water jet. In: Proc.Instn Mech, Engrs Vol.218

Part B:J. Engineering Manufacture, pp.503-508.

37. Ojmertz, K.M.C.,1997. A study on abrasive water jet milling, Ph.D. Thesis,

Chalmers University of Technology, Gotenborg, Sweeden.

38. Poondla, N., Srivatsan, T.S., Patnaik, A., Petraroli, M., 2009. Journal of Alloys

and Compounds; A study of micro scructure and hardness of two titanium

alloys: Commercially pure and Ti-6Al-4V, Journal of Alloys and Compounds

486, pp.162-167.

39. Ravinshankar, S.R.,Murthy, C.R.L.,2000. Application of acoustic emission in

drilling of composite laminates. In: NDT&E International, pp. 429 – 435.

40. Raju, S.P. and Ramulu, M.,1994. Predicting hydro-abrasive erosive wear during

abrasive water jet cutting: Part 1 in Alzheimeir, W.E.,(ed), Manufacturing

Science and Engineering, vol.1. ASME, New York, pp.191-202.

41. Ritter, E., 1985. Erosion damage in structural ceramics. In: Material Science

Engineering 71, pp. 303-330.

42. Seo, Y.W., Ramulu.M., Kim,D.,2003. Machinability of titanium alloy (Ti-6Al-

4V) by abrasive waterjets, Proceedings of the Institution of Mechanical

Engineers, Part B: Journal of Engineering Manufacture 2003 217: pp.1709-

1721.

43. Singh P.J.,Chen W.L., Munoz, F., 1991. Comprehensive evaluation fo abrasive

waterjet cut surface quality, in 6th American Waterjet Conference, USA, 1991,

pp. 139 – 156.

44. Sheldon, G.L. and Finnie, I.1966. The mechanism of material removal in the

erosion cutting brittle material. In: Wear 67, pp. 393-400

164

45. Schofield, B.H.,1972. Research on the sources and characteristics of acoustic

emission. ASTM STP 505, pp. 11-19.

46. Siores, E.,Wong, W.C.K., Chen, L.,Wager, J.G.,1996. Enhancing abrasive

waterjet cutting of ceramics by head oscillation techniques, Ann.CIRP,45(1),

pp.215-218.

47. Shipway, P., Fowler, G., Pashby, I.R., 2004. Characteristics of the surface of a

titanium alloy following milling with abrasive waterjet. In: Wear 258, pp.123 –

132.

48. Tonshoff, H.K., Jung, M., Mannel, S., Rietz, W., 2000. Using acoustic emission

signals for monitoring of production processes. In: Ultrasonic 37 (2000), pp. 681

– 686.

49. Tonshoff ,H.K., Kross, F., Marzenell, C.,1996. High pressure water peening a

new mechanical surface strengthening process, Annals of CIRP 46, pp. 113-116.

50. Viera,J.M., Machado,A.R., Ezugwu,E.O., 2001.Performance of cutting fluids

during face milling of steels, Journal of Materials Processing technology 16,

pp.244-251

51. Wang,J.,2003. Abrasive Water Jet Machining of Engineering Materials. Volume

19 of Materials Sciences Foundations ISSN 1422-3597. Trans Tech Publications

Ltd, Switzerland.

52. Wang,J.,2007. Predictive depth of jet penetration models for abrasive waterjet

cutting of alumina ceramics, International Journal of Mechanical Sciences

49,pp.306-316.

53. Wessel, J.K., 2004.The Handbook of Advanced Materials, Willey Interscience,

Oak Ridge, Tennessee, USA, pp. 272-319.

165

Appendix A: AE Sensor and Coupler Specifications

Sensor

Coupler

166

Appendix B: Drilling and Traverse Cut Profile

Water Jet Drilling

250 MPa

300 MPa

167

350 MPa

(a

*Scale: 0.5 mm

168

Water Jet Traverse Cut

300 MPa

350 MPa

10 mm/s

8 mm/s

6 mm/s

10 mm/s

4 mm/s

8 mm/s 6 mm/s

169

Appendix C: Measurement Result



2 4 6 8 10

0 10.78 5.81 4.86 3.21 2.48

5 10.79 5.82 4.85 3.20 2.47 Depth of C

ut (mm

)

10 10.70 5.84 4.71 3.18 2.46 200 MPa

15 10.67 5.85 4.64 3.2 2.42

20 10.75 5.85 4.72 3.17 2.41

25 7.58 4.96 3.64 2.92 2.36

30 4.77 2.63 2.15 1.75 1.52



2 4 6 8 10

0 16.12 10.10 7.04 4.95 3.65

5 16.11 10.12 7.07 4.94 3.66 Depth of C

ut (mm

)

10 16.16 10.15 7.14 5.09 3.63 250 MPa

15 16.23 10.2 7.1 5.03 3.65

20 16.05 10.09 7.09 5.01 3.66 25 12.56 8.56 6.63 4.29 3.51

30 6.35 4.2 3.15 2.39 1.99



2 4 6 8 10

0 21.36 12.41 8.04 6.62 5.08

5 21.08 12.45 8.05 6.61 5.07 Depth of C

ut (mm

)

10 20.14 12.76 8.06 6.53 5.16 300 MPa

15 21.26 11.98 8.24 6.45 5.20

20 20.76 12.04 8.16 6.58 5.11 25 15.21 9.99 7.25 5.91 4.95

30 7.98 5.17 3.66 3.08 2.39

170



2 4 6 8 10

0 23.56 15.24 10.26 7.28 6.41

5 23.59 15.27 10.25 7.25 6.31 Depth of C

ut (mm

)

10 23.54 15.43 10.18 7.14 6.29 350 MPa

15 22.56 15.32 10.05 7.21 6.26

20 22.89 15.29 9.98 7.17 6.19

25 20.31 12.25 8.12 6.95 6.11

30 8.87 5.09 4.14 3.74 3.24

171

Appendix D: MATLAB Equation for Process Parameters

Drilling Equations

Pressure (MPa)

Drilling Time (s)

MATLAB Equation f(x)

200 5 4.126e-0.1763x 200 10 5.502e-0.3244x+0.714e-0.04201x 200 15 3.622e-0.3526x+0.6722e-0.04195x 200 20 2.623e-0.3544x+0.8825e-0.06x 200 25 2.774e-0.3021x+0.7705e-0.05484x 200 30 2.587e-0.2719x+0.6103e-0.05013x 250 5 2.464e-0.1498 x 250 10 4.239e-0.6046x+ 1.215e -0.0923x 250 15 2.472e-0.3087x + 0.8067e-0.06366x 250 20 2.778e-0.4145 x + 0.9486e-0.07174 x 250 25 2.364e-0.3528x+ 0.8457e -0.06871 x 250 30 1.763e-0.2492x+ 0.6298e -0.05828 x 300 5 3.707e-0.1279x 300 10 9.982e-0.7061x + 1.358e-0.08769x 300 15 5.045e-0.3137x + 0.9435e-0.06336x 300 20 2.69e-0.2229x + 0.6036e-0.04481x 300 25 4.402e-0.1973x + 0.7502e-0.04348x 300 30 2.208e-0.2398x + 0.705e-0.05053x 350 5 2.558e-0.1225x 350 10 264e-1.427x + 1.698e-0.09283x 350 15 64.32e-1.044x + 1.455e-0.08119x 350 20 3.694e-0.4402x + 1.209e-0.06819x 350 25 2.861e-0.4403x + 1.313e-0.07134x 350 30 2.073e-0.1233x + 0.2181e-0.01158x

*f(x) represents the AErms while x is the drilling time

172

Modified Drilling Equations

Pressure (MPa)

Drilling Time (s)

Modified Equation g(x)

200 5 1.612e-0.1763x 200 10 1.024e-0.3244x+ 0.574e-0.04201x 200 15 0.871e-0.3526x+ 0.567e-0.04195x 200 20 0.604e-0.3544x+ 0.688e-0.06x 200 25 0.608e-0.3021x+ 0.585e-0.05484x 200 30 0.650e-0.2719x+ 0.473e-0.01158x 250 5 1.092e-0.1498x 250 10 0.359e-0.6046x+ 0.834e-0.0923x 250 15 0.491e-0.3087x+ 0.578e-0.06366x 250 20 0.332e-0.4145x+ 0.657e-0.07174x 250 25 0.365e-0.3528x+ 0.953e-0.588x 250 30 0.484e-0.2492x+ 0.465e-0.05828x 300 5 1.114e-0.1279x 300 10 0.244e-0.7061x+ 0.857e-0.08769x 300 15 0.527e-0.3137x+ 0.598e-0.06336x 300 20 0.624e-0.2229x+ 0.450e-0.04481x 300 25 0.773e-0.1973x+ 0.511e-0.004348x 300 30 0.700e-0.2398x+ 0.554e-0.05053x 350 5 1.282e-0.1225x 350 10 0.215e-1.427x+ 1.069e-0.09283x 350 15 0.282e-1.044x+ 0.954e-0.08119x 350 20 0.413e-0.4402x+ 0.861e-0.06819x 350 25 0.345e-0.4403x+ 0.932e-0.07134x 350 30 1.127e-0.1233x+ 0.206e-0.01158x

*f(x) represents the AErms while x is the drilling time

173

Modified Traverse Cut Equations

Pressure (MPa)


MATLAB Equation f(x)

200 2 0.4124e-0.001849t -0.4062e-1.057t 200 4 0.4124e-0.001849t -0.4062e-1.057t 200 6 0.6034e-0.06874t -0.6038e-9.689t 200 8 0.5901e-0.04644t-0.65 e-9.043t 200 10 -0.5317e-25.66t+ 0.5315e-0.04285t

250 2 417.4e-0.1046t -417.4e-0.1048t 250 4 0.3853e-0.02156t -0.3764e-2.94t 250 6 0.4721e-0.1319t-0.4649e-5.369t 250 8 0.4404e-0.09806t-0.4405e-6.078t 250 10 0.427e-0.06993t-0.4201e-16.12t

300 2 0.2562e-(t-14.38/3.722)^2 +0.2932e-(x-7.952/4.998)^2 300 4 0.526e-0.1029t-0.5256e-1.444t 300 6 0.439e-0.08422t -0.4389e-2.987t 300 8 0.32e0.02262t -0.3188e-4.165t 300 10 0.2995e0.0551t -0.2995e-17.52t

350 2 2.307e-0.0913t-2.307e-0.09131t 350 4 0.2888e0.008571t -0.2877e-1.13t 350 6 0.3698e-0.09892t -0.3699e-2.318t 350 8 0.2454e-0.04594t -0.2452e-3.829t 350 10 0.2734e-0.01374t-0.2738e-5.946t

*f(x) is the AErms while t is the cutting time

174

Appendix E: AErms Graph for Drilling

0 5 10 15 20 25 30 350

0.5

1

1.5AErms vs Drilling Time (250 MPa)

0 5 10 15 20 25 30 350

0.5

1

1.5

AE

rms

(vol

tage

)

0 5 10 15 20 25 30 350

0.5

1

1.5


15 sec

10 sec

5 sec

0 5 10 15 20 25 30 350

0.5

1


0 5 10 15 20 25 30 350

0.5

1

1.5

AE

rms

(vol

tage

)

0 5 10 15 20 25 30 350

0.5

1


20 sec

25 sec

30 sec

175

0 5 10 15 20 25 30 350

0.5

1


0 5 10 15 20 25 30 350

0.5

1

1.5A

Erm

s (v

olta

ge)

0 5 10 15 20 25 30 350

0.5

1

1.5


5 sec

10 sec

15 sec

0 5 10 15 20 25 30 350

0.5

1


0 5 10 15 20 25 30 35 400

0.5

1

1.5

AE

rms

(vol

tage

)

0 5 10 15 20 25 30 350

0.5

1

1.5


20 sec

25 sec

30 sec

176

0 5 10 15 20 25 30 350

0.5

1


0 5 10 15 20 25 30 350

0.5

1

1.5A

Erm

s (v

olta

ge)

0 5 10 15 20 25 30 350

0.5

1

1.5


5 sec

10 sec

15 sec

0 5 10 15 20 25 30 350

0.5

1


0 5 10 15 20 25 30 350

0.5

1

1.5

AE

rms

(vol

tage

)

0 5 10 15 20 25 30 350

0.5

1

1.5


20 sec

25 sec

30 sec

177

Appendix F: Effective AErms for Drilling

5.5 6 6.5 7 7.5 8 8.5 9 9.50.5

0.6

0.7

0.8

0.9

1

1.1


AE

rms

(vol

tage

)

Effective AErms vs Drilling Time(5 sec)


5 6 7 8 9 10 11 12 130.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2


AE

rms

(vol

tage

)


250fit 1Prediction bounds (fit 1)

6 8 10 12 14 16 180.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1


AE

rms

(vol

tage

)



178

6 8 10 12 14 16 18 20 22 24

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


AE

rms

(vol

tage

)



6 8 10 12 14 16 18 20 22

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


AE

rms

(vol

tage

)



10 15 20 25 30 350

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


AE

rms

(vol

tage

)



179

9.5 10 10.5 11 11.5 12 12.5 13 13.5

0.6

0.7

0.8

0.9

1

1.1


AE

rms

(vol

tage

)



6 7 8 9 10 11 12 13 14

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1


AE

rms

(vol

tage

)



8 10 12 14 16 18 200.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1


AE

rms

(vol

tage

)



180

8 10 12 14 16 18 20 22 24 26

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1


AE

rms

(vol

tage

)



10 15 20 25 30

0.2

0.4

0.6

0.8

1

1.2


AE

rms

(vol

tage

)



5 10 15 20 25 30

0.2

0.4

0.6

0.8

1

1.2


AE

rms

(vol

tage

)



181

6 6.5 7 7.5 8 8.5 9 9.5 10

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4


AE

rms

(vol

tage

)



5 6 7 8 9 10 11 12 13 140.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3


AE

rms

(vol

tage

)



6 8 10 12 14 16 18

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3


AE

rms

(vol

tage

)

Effective AErms vs DrillingTime (15 sec)


182

6 8 10 12 14 16 18 20 22 24

0.2

0.4

0.6

0.8

1

1.2


AE

rms

(vol

tage

)

Effective AErms vs DrillingTime (20 sec)


5 10 15 20 25

0.2

0.4

0.6

0.8

1

1.2


AE

rms

(vol

tage

)



5 10 15 20 25 30

0.2

0.4

0.6

0.8

1

1.2

1.4


AE

rms

(vol

tage

)



183

Appendix G: AErms Graph for Traverse Cut

250 MPa

0 2 4 6 8 10 12 14 16 180

0.5

1


0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1

AE

rms

(vol

tage

)

0 2 4 6 8 10 12 14 16 180

0.5

1


10 mm/s

8 mm/s

6 mm/s

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1

1.2


AE

rms

(vol

tage

)

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1

1.2


4 mm/s

2 mm/s

184

300 MPa

0 2 4 6 8 10 12 14 16 180

0.5

1AErms vs Cutting Time (300 MPa)

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1

AE

rms

(vol

tage

)

0 2 4 6 8 10 12 14 16 180

0.5

1


10 mm/s

8 mm/s

6 mm/s

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1AErms vs Cutting Time (300 MPa)

AE

rms

(vol

tage

)

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1

1.2


4 mm/s

2 mm/s

185

350 MPa

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6


0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

AE

rms

(vol

tage

)

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8


10 mm/s

8 mm/s

6 mm/s

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6


AE

rms

(vol

tage

)

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1

1.2


2 mm/s

4 mm/s

186

Appendix H: Effective AErms Graph for Traverse Cut

4 mm/s

0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


AE

rms

(vol

tage

)AErms vs Cutting Time (4 mm/s)


0 1 2 3 4 50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


AE

rms

(vol

tage

)



0 0.5 1 1.5 2 2.5 3 3.5 4 4.50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


AE

rms

(vol

tage

)



0 1 2 3 4 5 60

0.1

0.2

0.3

0.4

0.5

0.6

Cutting Time (seconds)A

Erm

s (v

olta

ge)



187

6 mm/s

0 0.5 1 1.5 2 2.5 3 3.5 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


AE

rms

(vol

tage

)



0 0.5 1 1.5 2 2.5 30

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


AE

rms

(vol

tage

)



0 0.5 1 1.5 2 2.5 3 3.5 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


AE

rms

(vol

tage

)



0 0.5 1 1.5 2 2.5 3 3.5 40

0.1

0.2

0.3

0.4

0.5

0.6


AE

rms

(Vol

tage

)



188

8 mm/s

0 0.5 1 1.5 2 2.5 30

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

CuttingTime (seconds)

AE

rms

(vol

tage

)



0 0.5 1 1.5 2 2.5 30

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8


AE

rms

(vol

tage

)



0 0.5 1 1.5 2 2.5 30

0.1

0.2

0.3

0.4

0.5

0.6

CuttingTime (seconds)

AE

rms

(vol

tage

)



0 0.5 1 1.5 2 2.50

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Cutting Time (seconds)A

Erm

s (v

olta

ge)



189

Appendix I: Effects of Pressure on AErms (Traverse Cut)

0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1

1.2


Time (seconds)

AE

rms

(Vol

tage

)


0 1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

1.2


Time (seconds)

AE

rms

(Vol

tage

)

200cMPa250 MPa300 MPa350 MPa

190

Appendix J: Effects of Pressure on AErms (Drilling)

0 5 10 15 200

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6



AE

rms

(vol

tage

)


0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 300

0.2

0.4

0.6

0.8

1

1.2



AE

rms

(vol

tage

)


0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 360

0.2

0.4

0.6

0.8

1

1.2



AE

rms

(vol

tage

)


191

Appendix K: Relationship between AErms and Cutting Length

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Cutting Length (mm)

AE

rm (

volta

ge)



0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 300

0.2

0.4

0.6

0.8

1

Cutting Length (mm)

AE

rms

(vol

tage

)


200 MPa

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 300

0.2

0.4

0.6

0.8

1

Cutting Length (mm)

AE

rms

(vol

tage

)


200 MPa

192

Appendix L: Effects of Traverse Speed on AErms

0 2 4 6 8 10 12 14 16 18 200

0.2

0.4

0.6

0.8

1

1.2

1.4200 MPa


AE

rms

(vol

tage

)


0 2 4 6 8 10 12 14 16 180

0.2

0.4

0.6

0.8

1

1.2

1.4250 MPa

Time (seconds)

AE

rms

(vol

tage

)


0 2 4 6 8 10 12 14 16 180

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8350 MPa

Time (seconds)

AE

rms

(vol

tage

)


193

Appendix M: Effects of Drilling Time on AE rms

0 5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

1

1.2

1.4250 MPa


AE

rms

(vol

tage

)


0 5 10 15 20 25 30 350

0.2

0.4

0.6

0.8

1

1.2

1.4300 MPa


AE

rms

(vol

tage

)


0 5 10 15 20 25 30 350

0.5

1

1.5350 MPa


AE

rms

(vol

tage

)


afiq_thesis

Documents

depth of cut monitoring

performance of awj machining

aspects of machining

machining difficult

conventional machining

offline monitoring method

drilling process

high machining versatility