multi response optimisation of die sinker...

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International Journal of Mechanical Engineering and Technology (IJMET)

Volume 7, Issue 3, May–June 2016, pp.63–77, Article ID: IJMET_07_03_006

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MULTI RESPONSE OPTIMISATION OF DIE

SINKER EDM FOR ALSIC COMPOSITE

Mukesh Regmi

Asst. Professor, Nepal College of Information Technology, Kathmandu

Anil Pol

Asst. Professor, Department of PG Studies, VTU, Belgaum

Sachin Kulkarni

Asst. Professor, Gogte Institute of Technology, Belgaum

ABSTRACT

One of the important aspects that should be taken into consideration in the

majority of manufacturing processes and, particularly, in processes related to

Electrical Discharge Machining (EDM) is the correct selection of

manufacturing conditions. Appropriate choice of the machining parameters

and electrode material during electric discharge machining is fundamental to

its performance and accuracy.

This paper presents a fundamental study of EDM by using two different

electrode and aims at investigating the effect of EDM parameters on material

removal rate (MRR) and tool wear rate (TWR) as an alternative method for

machining Aluminium Silicon Carbide (AlSiC ) metal matrix composite

produced with stir casting method. The primary aim of this research is to

determine the optimal machining parameter conditions of intensity of current,

pulse on time and pulse off time and proper electrode material for machining

AlSiC workpiece using EDM.

The concept of response surface methodology, with a well-designed

experimental scheme named central composite design was used and a second

order model capable of predicting the responses is developed. This model was

further checked for its adequacy by using ANOVA analysis and the results was

further validated by the justification from the related literature.

Key words: EDM (Electric Discharge Machining), AlSiC MMC (Aluminium

Silicon Carbide Metal Matrix Composite), MRR(Material Removal Rate),

TWR(Tool Wear Rate), ANOVA(Analysis of Variance), RSM(Response

Surface Methodology)

Mukesh Regmi, Anil Pol and Sachin Kulkarni


Cite this Article Mukesh Regmi, Anil Pol, Sachin Kulkarni, Multi Response

Optimisation of Die Sinker EDM for Alsic Composite. International Journal

of Mechanical Engineering and Technology, 7(3), 2016, pp. 63–77.

http://www.iaeme.com/currentissue.asp?JType=IJMET&VType=7&IType=3

1. INTRODUCTION

Parts manufactured by casting, forming, and various shaping processes often require

further operations before they are ready for use or assembly. So a proper machining

need to be done which involves the removal of some material from the work piece

(machining allowance) in order to produce a specific geometry at a certain degree of

accuracy and surface quality in a cheap cost.

In modern machining practice, harder, stronger, and tougher materials that are

more difficult to cut are frequently used. More attention is, therefore, directed toward

machining processes where the mechanical properties of the work piece material are

not imposing any limits on the material removal process. In this regard, the

nonconventional machining techniques like Electric Discharge Machining (EDM)

came into practice [1].

Electric Discharge Machining (EDM), sometimes colloquially also referred to

as spark machining, spark eroding, burning, die sinking or wire erosion, is an electro-

thermal non-traditional machining process, where electrical energy is used to generate

electrical spark and material removal mainly occurs due to thermal energy of the

spark. Material is removed from the work piece by a series of rapidly recurring

current discharges between two electrodes which are separated by

a dielectric liquid and subject to an electric voltage.

As shown in the Fig. (1) DC power supply provides power to the configuration i.e.

tool and the workpiece. The tool is generally given negative polarity and workpiece is

given positive polarity. When the voltage across the gap becomes sufficiently high it

discharges through the gap in the form of the spark producing very high temperature

and thus melting and eroding the material.

Figure. 1 Setup of EDM [2]

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Multi Response Optimisation of Die Sinker EDM for Alsic Composite


2. LITERATURE REVIEW

P. Janmanee et al. [3] evaluated the performance of different electrode materials like

graphite, copper-graphite and copper-tungsten in EDM of tungsten carbide. The

important parameters were discharge current, pulse on time, pulse off time, open-

circuit voltage and electrode polarity. Their investigation concluded that MRR

increases with the discharge current intensity and graphite electrode gives the most

MRR but it gives high electrode wear ratio.

B.Mohan et al. [4] had studied EDM of AlSiC composites with 20-25 vol. % SiC

taking the following like Current, electrode material polarity, pulse duration and

rotation of electrode on MRR, TWR, and SR. It was observed that the increase in

volume percentage of SiC has resulted in decrease in MRR, SR and increase in EWR

and the increase in rotational speed of the tube electrode has produced higher MRR,

EWR and better SR.

Harish K.Garg et al. [5] studied about the machining of the hybrid Aluminium Metal

Matrix composite (Al/SiC/Gr and Al/Si10Mg/Fly ash/Gr). They investigated about

the problems encountered during machining of hybrid MMCs and concluded that

machining of Al/SiC-MMC is one of the major problem, which resist its wide

spread application in industry and the problems faced were rapid TWR, irregular

MRR, requirement of large pulse current values, difficult to cut very complex and

complicated shape or geometrical profile etc.

S.L.Chen et al. [6] studied about various parameters of EDM like electrode material,

pulse duration, discharge current and polarity using two materials namely silicon

carbide and tungsten carbide as work piece and copper and copper tungsten as a tool

material. They concluded that MRR is directly proportional to current and pulse

duration, Electrode wear increased up to 80 µs then started decreasing with increase

in pulse duration. They came into conclusion that Copper is better than copper

tungsten as an electrode material due to homogeneous wear ratio.

2.1. Objective of the Present Work

The objective of the present work is an attempt to finding feasibility of machining

Al/SiC composite material using brass as well as copper tungsten electrode. In

summary the objective of the project is;-

Selection of process variables such as Intensity of current, Pulse on time & Pulse off

time in Die-Sink-EDM for machining of Al/SiC Metal Matrix Composite.

To investigate the Effect of different tool Electrodes such as brass & CuW on MRR

and TWR

To develop an empirical model for Intensity of current, Pulse on time & Pulse off

time for machining of Al/Sic Metal Matrix Composite using RSM.

To verify the lack of fit of the proposed model using analysis of variance

(ANOVA).

3. MATERIALS AND METHODOLOGY

a) Tool material

In this experiment brass and copper tungsten both having 7mm.machining diameter

was used as a tool electrode. The important factors in selecting brass and cooper

tungsten are their high strength-to-weight ratio, resistance to corrosion by many

chemicals, high thermal and electrical conductivity, non-toxicity, reflectivity,

appearance and ease of formability and of machinability; they are also nonmagnetic.



b) Workpiece material

The workpiece material chosen was aluminium silicon carbide (AlSiC) metal matrix

composite produced with stir casting method consisting of aluminum matrix with 3%

silicon carbide particles. It has high thermal conductivity (180–200 W/m K), and

its thermal expansion can be adjusted to match other materials,

e.g. silicon and gallium arsenide chips and various ceramics. It is chiefly used

in microelectronics as substrate for power semiconductor devices and high

density multi-chip modules, where it aids with removal of waste heat.

c) Response Surface Methodology

Response surface methodology (RSM) is a collection of mathematical and statistical

techniques that are useful for modeling and analysis of problems in which output or

response is influenced by several variables and the goal of RSM is to find the

correlation between the response and the variables. It is used in the development of an

adequate functional relationship between a response of interest, y, and a number of

associated control (or input) variables denoted by x1, x2, xk.

Suppose X1 and X2 are the factors or parameters of interest of the process and ‘Yi’

is the maximum yield of the process then the yield is a function of levels of X1 and X2

i.e.

Yi= f (X1, X2) + ei (1)

where ‘ei’ represents the noise or error observed in the response Yi. If we denote

the expected response by

E (Yi) = f (X1, X2) = η (2)

Then the surface represented by

η = f (X1, X2) (3)

is called response surface.

If there is curvature in the system, then a polynomial of higher degree must be

used, such as the second-order model given in equation 4

(4)

Central composite design is an experimental design, useful in response surface

methodology, for building a second order (quadratic) model for the response variable

without needing to use a complete three-level factorial experiment.[7] The CCD used

in our experiment is shown below-

Table 1 Design Insight

Design Central Composite Design

Factors 3 Replicates 1 Total runs 20

Base blocks 1 Base runs 20 Total

blocks

1

Cube points 8 Center points in cube 6 Axial

Points

6



d) Design Matrix

In this study, three machining parameters were selected as control factors and each

parameter was designed to have three levels denoted by 1, 2 and 3 respectively. The

experiments are planned using CCD in the design of experiments (DOE), which helps

in reducing the number of experiments. The total 20 experimental runs were

conducted with each tool on the workpiece and results of MRR and TWR was

calculated. The process variables with their units and notations are shown in the Table

1 below-

Table 2 Parameters and Levels

MACHINING

PARAMETER

UNIT

RANGE AND LEVELS

-1 0 +1

Pulse on time µs 20 40 60

Pulse off time µs 2 5 8

Current A 4 6 8

e) Macine used

The experimentation was conducted using EDM, model SAVITA 4631l (Die sinking

type) having following specifications-

Table 3 Machine Specifications

S.N. TYPES Unit 46311

1 Work tank dimensions mm3

600*370*250

2 Table size mm2

350*200

3 X-Axis Travel mm 200

4 Y-Axis Travel mm 120

5 Z-Axis Travel mm 150

6 Maximum Job Height mm 140

7 Maximum Electrode Weight kg 50

8 Maximum Job Weight kg. 150

9 Dielectric Tank Capacity Lt. 100

10 Machine Weight kg 450

11 Gross weight kg 600

12 Day Light mm 410

13 Throat mm 250

14 Overall Dimensions m3

0.8*0.8*1.8



4. EXPERIMENTATION

a) Experimental Procedure

Experiment was conducted with negative polarity of electrode. The electrode brass

was taken. The diameter of electrode is measured with a micrometer. It was made

sure its dimension is according to specification.

An initial mass is measured with precision balance. The electrode mass value and the

work piece mass value were jotted.

The work material (Aluminium Silicon Carbide) was mounted on the T-slot table and

positioned at the desired place and clamped. The electrode was clamped on the tool

holder, and its alignment was checked.

The parameters of the experiment were set regarding Table (3.1) and Table (3.2).

The time was set as 2 minutes for the machining of all work materials. Finally,

switches ‘ON’ for operating the desire discharge current values.

After machining operation, the electrode was taken out and weighed again on

weighing balance. Also the mass value of work piece was taken after machining.

The same experiment was repeated with copper tungsten electrode. This experiment

is done 20 times for each electrode. The data was fed to the MINITAB where

calculation and analysis of results is done.

b) MRR and TWR Evaluation

The material MRR has been calculated by taking the difference between the weight of

the work piece before and after machining to the machining time.

MRR=

(1)

Where,

Wb = Weight of work piece before machining in gm

Wa = Weight of work piece after machining in gm

t = Machining time in minutes

TWR is calculated in the same fashion by taking the difference of weight of

the tool before and after machining to the machining time.

TWR=

(1.2)

Where,

Wbt = Weight of the tool before machining in gm.

Wat = Weight of the tool after machining in gm.

t = Machining time in minutes



Figure. 2 MRR with CuW Versus Brass

Figure 3 TWR with CuW Versus Brass

The MRR and TWR values were calculated for both the copper tungsten and brass

tool experimental run and then plotted on the graph with their corresponding values.

5. RESULTS AND DISCUSSION

For interpreting the significant effect of the parameters, a statistical software program

called MINITAB version 16 has been used. The experimental results from the tables

were analyzed using ANOVA, which is used for identifying the factors significantly

affecting the performance measures. The analysis was carried out for the significance

level of α=0.1 i.e. for a confidence level of 90%. The sources with the p value less

than 0.1are considered to have a statistically significant.

0

0.05

0.1

0.15

0.2

0.25

0.3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

MRR with CuW Versus Brass

MRR with CuW MRR with Brass

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

TWR with CuW Versus Brass

TWR with CuW TWR with Brass



Results for MRR with CuW Versus Brass

Figure. (2) shows the MRR for both the electrodes where it has been conspicuous that

CuW gives more MRR than brass for the same machining conditions.

Results for TWR with CuW Versus Brass

Figure (3) shows the TWR for both the electrodes where it has been clearly seen that

Brass has far higher tool wear than that of CuW.

Results of MRR with CuW

Table 4 Estimated Regression Coefficients for MRR

Terms Coeff. SE Coeff. T test P value

C -0.276914 0.102577 -2.700 0.022 *

I 0.037469 0.036520 1.026 0.329

Ton 0.007907 0.002650 2.984 0.014 *

Toff 0.032999 0.015603 2.115 0.061 *

I* I -0.001465 0.002939 -0.499 0.629

Ton *Ton -0.000076 0.000029 -2.600 0.027 *

Toff *Toff -0.001665 0.001306 -1.275 0.231

I*Ton 0.000081 0.000172 0.472 0.647

I*Toff -0.000098 0.001149 -0.085 0.934

Ton*Toff -0.000233 0.000115 -2.029 0.070 *

S = 0.0194943, R-Sq = 91.55%, R-Sq(pred) = 19.63% , R-Sq(adj) = 83.95%

From the ANOVA table, the main effects of pulse on time and pulse off time can

be deduced as having significant effect. Thus, the final model correlating Material

Removal Rate with cutting parameters is found as follows:

The effectiveness of the model is checked by using the ‘R2’ value i.e. 0.91 which

is very close to 1 and hence the model is found to be very effective. The validity of

the model is reconsidered with the adjusted correlation coefficient i.e. ‘R2

(adj.)’value

= 0.83, which is a measure of the variability of the observed output and can be

explained by the factors along their factor interactions.



864

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Current

Me

an

Pulse on time

Pulse off time

Main Effects Plot for MRRData Means

Figure. 4 Main Effect for MRR with CuW tool

The graph shows that increase in the value of current leads to the significant

increase in MRR. This increase of MRR with current is due to the fact that with the

increase in amount of pulse current generates strong spark which creates higher

temperature, due to which more material is melted and eroded from the workpiece [8].

The graph reveals that on increasing the pulse on time, MRR goes on increasing

up to half the way and it goes on decreasing from 40μs to 60μs. This event has been

attributed to the increase of input energy in high pulse on time duration, which results

in more chopping on the gap between workpiece and tool electrode, creating a short

circuit which decreases the efficiency of electrical spark erosion. In other words short

pulse on time duration causes less vaporization, whereas long pulse on time duration

causes the plasma channel to expand, resulting in less energy density on workpiece,

which is insufficient to melt and/or vaporize the workpiece material [9].

It is also evident that on increasing the pulse off time, MRR goes on increasing

from 2μs to 5μs. It is because of correct flushing of the debris with sufficient pulse off

time duration; otherwise the debris could make the spark contaminated and unstable,

thus decreasing MRR [9]. However it goes on decreasing from 5μs to 8μs. This is

because when pulse off increases, there will be an undesirable heat loss which does

not contribute to MRR. This will lead to drop in the temperature of the workpiece

before the next spark starts and therefore MRR decreases [8].



a) Results of TWR with CuW

Table 5 Estimated Regression Coefficients for TWR


C -0.053817 0.016443 -3.273 0.008 *

I 0.018497 0.005854 3.160 0.010 *

Ton -0.000539 0.000425 -1.269 0.233

Toff 0.007907 0.002501 3.161 0.010 *

I* I -0.000916 0.000471 -1.944 0.081 *

Ton *Ton 0.000014 0.000005 3.018 0.013 *

Toff *Toff -0.000813 0.000209 -3.881 0.003 *

I*Ton -0.000113 0.000028 -4.084 0.002 *

I*Toff 0.000081 0.000184 0.441 0.668

Ton*Toff -0.000004 0.000018 -0.215 0.834

S = 0.00312493, R-Sq = 90.68% , R-Sq(pred) = 36.49%, R-Sq(adj) = 82.29%

Table shows that effect of current and pulse off time terms are found to be

statistically significant while except current*pulse on time all interaction terms

contributed less significantly to the TWR at 90 % confidence level. Thus, the final

model correlating TWR with machining parameters is found as follows:

R2 value of 90.68 % indicates that, the variation in the response can be predicted

90 % correctly by using the above model developed for 90 % confidence interval.

864

0.020

0.015

0.010

604020

852

0.020

0.015

0.010

Current

Me

an

Pulse on time

Pulse off time

Main Effects Plot for TWRData Means

Figure. 5 Main Effect for TWR with CuW tool



The graph shows that at the current of 8A, TWR is found to be the highest than

the rest of 2 levels. TWR kept on increasing with the current intensity. The graph

reveals that on increasing the pulse on time, TWR goes on decreasing. The reasons for

low tool wear rate at longer pulse on time duration settings are mainly due to

decreasing spatial current density of discharge channel with increasing discharge

pulse on time duration, longer time for heat transfer from the molten crater to the

body of tool, which results in less MRR from the crater and higher wear resistance of

the tool due to carbon attached to the surface [9]. It is also evident that on increasing

the pulse off time, TWR goes on increasing from 2μs to 5μs. However it goes on

decreasing from 5μs to 8μs.

b) Results of MRR with Brass

Table 6 Estimated Regression Coefficients for MRR


C 0.1333896 0.072320 1.851 0.094 *

I 0.009196 0.025748 0.357 0.728

Ton -0.000118 0.001868 -0.063 0.951

Toff -0.032872 0.011000 -2.988 0.014 *

I* I -0.002344 0.002072 -1.131 0.284

Ton *Ton -0.000028 0.000021 -1.355 0.205

Toff *Toff 0.000250 0.000921 0.271 0.792

I*Ton 0.000208 0.000121 1.711 0.118

I*Toff 0.006094 0.000810 7.524 0.000 *

Ton*Toff 0.000119 0.000081 1.466 0.173

S=0.0137441, R-Sq=95.57%, R-Sq(pred)=65.23%, R-Sq(adj)=91.58%

From ANOVA Table below, the main effects of pulse off time can be deduced as

having significant effect. Thus, the final model correlating MRR with cutting

parameters is found as follows:

From Table, it is evident that the model is adequate at 90% confidence level. The

effectiveness of the model is checked by using the ‘R2’ value i.e. 0.95 which is very

close to 1 and hence the model is found to be very effective. The validity of the model

is reconsidered with the adjusted correlation coefficient i.e. ‘R2

(adj.)’value = 0.91,

which is a measure of the variability of the observed output and can be explained by

the factors along their factor interactions.



864

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604020

852

0.18

0.16

0.14

0.12

0.10

CurrentM

ean

Pulse on time

Pulse off time

Main Effects Plot for MRRData Means

Figure. 6 Main Effect plot for MRR with Brass electrode

The graph shows that MRR increase proportionately with the current. The graph

reveals that on increasing the pulse on time, MRR goes on increasing up to half the

way and it goes on decreasing. This event has been attributed to the increase of the

discharge energy of the plasma channel leading to the formation of a bigger molten

material crater on the workpiece resulting higher MRR. But consequently by the

dispersion of more heat from the spark stricken position and increasing the amount of

heat transferred, the plasma channel’s efficiency in removing molten material from

the crater at the end decreases [10]. It is also evident that on increasing the pulse off

time, MRR goes on increasing from. It is because of correct flushing of the debris

with sufficient pulse off time duration; which would otherwise make the spark

contaminated and unstable, thus decreasing MRR [9].

c) Results of TWR with Brass

Table 7 Estimated Regression Coefficients for TWR


C 0.053029 0.016021 3.310 0.008 *

I -0.021541 0.005704 -3.377 0.004 *

Ton 0.000019 0.000414 0.046 0.964

Toff 0.015188 0.002437 6.233 0.000 *

I* I 0.002042 0.000459 4.448 0.001 *

Ton *Ton 0.00002 0.000005 0.513 0.619

Toff *Toff -0.001729 0.000204 -8.474 0.000 *

I*Ton -0.000041 0.000027 -1.513 0.161

I*Toff 0.000898 0.000179 5.004 0.001 *

Ton*Toff -0.000002 0.000018 -0.136 0.895

S = 0.00304471, R-Sq = 97.25%,

R-Sq(pred) = 79.50%, R-Sq(adj) = 94.77%



From the ANOVA table above, effect of current and pulse off time terms are

found to be statistically significant. Thus, the final model correlating TWR with

machining parameters is found as follows:

(6)

R2 value of 97.25 % indicates that, the variation in the response can be predicted

97 % correctly by using the above model developed for 90 % confidence interval.

864

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0.06

0.05

0.04

0.03

Current

Mean

Pulse on time

Pulse off time

Main Effects Plot for TWRData Means

Figure. 7 Main Effect plot for TWR with Brass tool

The graph shows that at the current of 4A, TWR is found to be minimum. Graph

depicted the linear increase in the value of TWR with current. The reason is that, at

low current a small quantity of heat is generated and a substantial portion of it is

absorbed by the surroundings, as a result, the amount of utilized energy in melting and

vaporizing the electrodes is not so intense. But by the increase in pulse current a

substantial quantity of heat will be transferred into the electrodes. Furthermore as the

pulse current increases, the discharge strikes the surface of the electrode more

intensely and creates an impact force on the molten material in the crater and causes

more molten material to be ejected out of the electrode [10].

The graph reveals that on increasing the pulse on time, TWR increases half the

way and goes on decreasing. Moreover longer pulse on time can provide enough time

for heavier positive ions attacking the cathode workpiece and hence removing more

material from the work than the tool [11].

It is also evident that on increasing the pulse off time, TWR goes on increasing

from 2μs to 5μs and almost showed a constant TWR after that. This is due to the fact

that the long pulse duration provides a better heat removal around the surface of brass



electrode which is normally a good thermal conductor. The decrease in temperature

on the surface of electrode causes less wear on the electrode [4].

Thus the individual effect of pulse on time and pulse off time along with the

interaction effect between pulse on and pulse off time have the significant

contributions in MRR empirical response models for copper tungsten tool. Whereas

the individual effect of pulse off time along with the interaction effect between

current and pulse off time have the significant contributions in MRR empirical

response models for brass tool.

In TWR empirical response models, the individual effect of current and pulse off

time have the significant contributions in MRR empirical response models for copper

tungsten tool as well as brass tool but interaction effect between current and pulse on

time have the significant contributions in TWR with copper tungsten electrode and

interaction effect between current and pulse off time have the significant contributions

in TWR for brass tool.

6. CONCLUSION

Summarizing the main features the following conclusions can be drawn-

1. The predicted machining performance values match the experimental values

reasonably well; with R2 of 91.55% and 90.68% respectively for MRR and TWR

using copper tungsten as tool electrode and 95.57% and 97.25% respectively for

the MRR and TWR using brass as the tool electrode.

2. It has been observed that MRR as well as TWR goes on increasing with the

current.

3. It was observed that MRR goes on increasing with pulse on time till halfway and

goes on decreasing for both electrodes while TWR follows the same trend for

brass tool but TWR goes on decreasing with pulse on time for copper tungsten.

4. It has also been seen that MRR as well as TWR goes on increasing half the way

and decreases with pulse off time using copper tungsten as an electrode but for the

brass electrode MRR kept on increasing all the way with pulse off time and TWR

kept on increasing half the way and almost remained constant after that.

5. It has been observed that TWR goes on increasing with the current and MRR goes

on increasing with pulse on time till halfway and goes on decreasing.

6. Copper Tungsten electrode gave the higher MRR than the brass electrode. Not

only that TWR was also very less in CuW as compared to the brass electrode with

the same values of the machining conditions. So it is concluded that copper

tungsten is better than brass electrode.

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multi response optimisation of die sinker...

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