30120140507011

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),

ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp. 113-124 © IAEME

113

OPTIMIZATION OF ELECTRODE WEAR RATE ON ELECTRICAL

DISCHARGE MACHINING AISI 304 SS WITH MULTI HOLE ELECTRODE

Dr. Maan Aabid Tawfiq, Saad Hameed Najem

Department of Production Eng. and Metallurgy, University of Technology, Baghdad, Iraq

ABSTRACT

AISI 304 stainless steel have a wide range of applications in the industrial field. The need for

machining AISI 304 SS has not been eliminated fully. The electrode wear rate (EWR) is an

important aspect during electrical discharge machining (EDM). In this investigation an attempt has

been made to assess the factors influencing electrode wear rate on the machining of AISI 304 SS.

Design of experiments (full factorial design) concept has been used for experimentation. The

machining experiments were conducted on a die sinking EDM machine using two levels of factors.

The factors considered were electrode shape, pulse current, pulse on time and pulse off time. A

procedure has been developed to assess and optimize the chosen factors to attain minimum electrode

wear rate by incorporating: (i) response table and response graph; (ii) normal probability plot; (iii)

interaction graphs; (iv) analysis of variance (ANOVA) technique. The results indicated that

electrode shape is a factor, which has greater influence on EWR, followed by pulse off time. Also the

determined optimal conditions really reduce the EWR on the machining of AISI 304 SS within the

ranges of parameters studied.

Keywords: EDM; AISI 304 SS; EWR; Response Table; Response Graph; ANOVA; Normal

Probability Plot.

1. INTRODUCTION

In today’s manufacturing scenario, electrical discharge machining (EDM) contributes a prime

share in the manufacture of complex- shaped dies, molds, and critical parts used in automobile,

aerospace, and surgical components with high precision [1]. The material is removed from the work

piece by the thermal erosion process, i.e., by a series of recurring electrical discharges between a

cutting tool acting as an electrode and a conductive workpiece in the presence of a dielectric fluid

[2]. The performance of EDM is usually evaluated by the output parameters namely material

removal rate (MRR), electrode wear rate (EWR), wear ratio (WR), machined surface roughness, etc.

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING

AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)

ISSN 0976 – 6359 (Online)

Volume 5, Issue 7, July (2014), pp. 113-124

© IAEME: www.iaeme.com/IJMET.asp

Journal Impact Factor (2014): 7.5377 (Calculated by GISI)

www.jifactor.com

IJMET

© I A E M E



114

[3-5]. It is always desirable to obtain higher MRR with lower EWR, WR, and Ra .With the electrical

sparks, more material is removed from the work material because of its positive polarity [6].

However, Due to high temperature of the sparks not only work material is melted and vaporised but

the electrode material is also melted and vaporised which is expressed by EWR [7].Electrode Wear is

an important factor because it affects dimensional accuracy and the shape produced [8]. The selected

AISI 304 stainless steel material for the present investigation is having a wide range of applications

in the industrial field: Chemical, Pharmaceutical, Cryogenic, Food, Dairy, Paper industries etc. [9].

This experimental scheme is almost similar to the L16 orthogonal array. A procedure has

been introduced to optimize the chosen factors to attain minimum tool wear by incorporating (i)

response table and effect graph; (ii) normal probability plot; (iii) interaction graphs; (iv) analysis of

variance (ANOVA) technique.

2. ACHIEVE THE DESIRED EWR ON THE AISI 304 SS WORKPIECE

In order to achieve the desired EWR on the AISI 304 SS workpiece, the present investigation

has been planned in the following steps:

A: identifying the important factors Based on the previous published results [10, 11], the machining parameters, which are having

significant effect on EWR. The machining parameters identified are: (1) electrode shape; (A), (2)

pulse current; (B), (3) pulse on time; (C), (4) pulse off time; (D). Out of which electrode shape has

been specially applied.

B: finding the upper and lower limits of the factors identified For finding the upper and lower limits of the machining parameters, a detailed analysis has

been carried out. The limits identified are discussed below:

i. The studies related to EDM indicate that electrode shape is the factor, which highly influences the

EWR, the increase of number of holes inside the electrode (to certain limit) decreases the EWR [12].

For maintaining the proper EWR, the electrode shape has been set at reasonable level of being multi

4 -channels and multi 7 -channels.

ii. Pulse current is another important factor which influences EWR. With an increase of pulse

current, EWR increases [10]. In the present study, the Pulse current is 30 and 36 Amp.

iii. The EWR increases with increase in pulse duration at all value of peak current. [13].The pulse on

time chosen between 100 and 150 µsec.

iv. The EWR decreases when pulse-off time is increased [14] and hence the pulse-off time has been

selected at reasonable level and is 50 and 75 µsec.

C: developing the experimental design matrix using design of experiments

An experiment is a series of trials or tests, which produces quantifiable outcomes. The

experiment may be random or deterministic. For experimentation, design of experiment in statistics

has been used. The merit of this experimental scheme is that the cost of experimentation is reduced

considerably as compared to one factor at a time type experiment. The identified factors and its

lower and upper limits are discussed in sections 2.A and 2.B. In this experimental scheme, all

possible combinations of levels are included so that there are 2n (where n refers to the number of



115

factors, i.e., 24 = 16) trials in the experiment [15]. The notations, units and their levels chosen are

summarized in Table 1.

For easy recording and processing of experimental data, the parameters levels are coded as

+1 and −1. The intermediate coded value of any levels can be calculated by using the following

expression [16]:

��=��.�� .�/��

��. �� .�/�� …………………………... (1)

Where Xmax is the upper level of the parameter, Xmin is the lower level of the parameter and

Xiis the required coded values of the parameter of any value of X from Xmin to Xmax.

D: conducting the experiments as per the design matrix The Electrical Discharge Machining experiments were conducted at the Center of Training

and Workshop at University of Technology, Iraq. The experiments were performed on a die sinking

EDM machine (CHMER EDM CNC) which operates with an iso-pulse.

The workpiece material used in this study was AISI 304 SS. Prior to EDM processing, the

workpiece was square specimens 35 × 35 mm and 3 mm thick with a roughness Ra of 2 µm on the

surface to be machined. The chemical composition of the workpiece material is given in Table 2.

Electrolytic copper cylindrical bars 100 mm long with a diameter of 30 mm were mounted axially in

line with workpieces and used as tool electrode at positive polarity, through hole was drilled as

shown in Fig. 1.

The gap between the electrode and the workpiece is 0.20 mm. The chemical composition of

the electrode material is given in Table 3.

Commercial grade EDM oil was used as dielectric fluid. Pressure flushing with 0.3 Kg/cm2

was used. A digital balance (DENVER INSTRUMENT) with a resolution of 0.1 mg was used for

weighing the electrode before and after the machining process. The EWR (mm3/min.) is defined as

the mass of metal removed M2 from the original massM1 (g) divided by the density of electrode ��

(g/mm3), and T is the machining time (min). Eq. (2) show the calculations used for assessing the

values of EWR. The design matrix and the corresponding responses are given in Table 4.

EWR ��

��……………………………….….. (2)

Table 1: Important parameters and their levels

S.

no Parameter Notation Unit

Levels

Actual factors Coded

factors

Low high Low Low

1 Electrode

shape A -

Multi -

4channels

Multi – 7

channels -1 +1

2 Pulse current B Amp 30 36 -1 +1

3 Pulse on time C µsec. 100 150 -1 +1

4 Pulse off time D µsec. 50 75 -1 +1

Table 2: Chemical composition of 304 stainless steel workpiece

Material C% Si% Mn% Cr% Ni% Mo% Cu% Fe%

% weight 0.07 0.64 1.4 18.5 10.2 0.3 0.1 Balance



116

Table 3: Chemical composition of tool electrode

Material Fe% Si% Sb% Zn% Sn% Al% P% Cu%

% weight 0.015 0.01 0.002 0.004 0.004 0.004 0.002 Balance

Fig. 1: Geometry of the tool electrode and the workpiece

Table 4: Design matrix and corresponding output response Exp.

No

Coded factors Actual factors EWR

mm3/min. A B C D A B C D

1 -1 -1 -1 -1 Multi 4 channels 30 100 50 1.115

2 +1 -1 -1 -1 Multi 7 channels 30 100 50 0.885

3 -1 +1 -1 -1 Multi 4 channels 36 100 50 1.152

4 +1 +1 -1 -1 Multi 7 channels 36 100 50 0.923

5 -1 -1 +1 -1 Multi 4 channels 30 150 50 1.232

6 +1 -1 +1 -1 Multi 7 channels 30 150 50 0.985

7 -1 +1 +1 -1 Multi 4 channels 36 150 50 1.267

8 +1 +1 +1 -1 Multi 7 channels 36 150 50 1.012

9 -1 -1 -1 +1 Multi 4 channels 30 100 75 0.970

10 +1 -1 -1 +1 Multi 7 channels 30 100 75 0.785

11 -1 +1 -1 +1 Multi 4 channels 36 100 75 1.005

12 +1 +1 -1 +1 Multi 7 channels 36 100 75 0.813

13 -1 -1 +1 +1 Multi 4 channels 30 150 75 1.082

14 +1 -1 +1 +1 Multi 7 channels 30 150 75 0.865

15 -1 +1 +1 +1 Multi 4 channels 36 150 75 1.102

16 +1 +1 +1 +1 Multi 7 channels 36 150 75 1.012

3. INFLUENCES OF MACHINING PARAMETERS AND THEIR EFFECTS ON EWR

The effect of factors which influences the EWR on AISI 304 SS has been analyzed through:

A. Response table and effect graph

The influence of machining parameters on EWR has been performed using response table.

Response tables are used to simplify the calculations needed to analyze the experimental data. In

response table, the effect of a factor on a response variable is the change in the response when the

factor goes from it slow level to its high level. The complete response table for a two level, 16 run

full factorial experimental design is shown in Table 5. If the effect of a factor is greater than zero,

the average response is higher for the higher level of the factor than for the low level. However, if

the estimated effect is less than zero, it indicates that the average response is higher at low level of



117

the factor than at high level. If the effect for a factor is very small, then it is probably because of

random variation than areal factor effect. The graphical display such as response graph can be used,

in conjunction with a response table, to identify appropriate settings for machining parameters to

minimize the average EWR [16]. The effect of main and interaction factors derived from the

response table for machining process is plotted in Fig. 2. The procedure involved in the construction

of a response table and response graph is explained elsewhere [17]. From Fig. 2, it is inferred that

the larger the vertical line, the larger the change in EWR when going from level -1 to level +1 for a

factor. It will be pointed out that the statistical significance of a factor is directly related to the

length of the vertical line.

Fig. 2: Effect graph

B. Normal probability plot In response graph, it is found that some of the factor effects are larger than the other, but it is

not clear, whether these results are real or chance. To identify the real effects, normal probability plot

are used and is shown in Fig. 3. Normal plot is a graphical technique based on ‘‘Central limit

theorem’’. The procedure for constructing the normal probability plot is given elsewhere [18]. As per

the normal probability plot, points which are close to a line fitted to the middle group of points

represent estimated factors which do not demonstrate any significant effect on the response variable.

On the other hand, the points appear to be far away from the straight line are likely to represent

the،real factor effects on the EWR. From Fig. 3, it has been asserted that the main factors A, B, C

and D and their five factor interactions AB, AD, BC, BD and CD are away from the straight line and

are considered to be significant.

C. ANOVA technique The normal probability plot has the disadvantage of not providing a clear criterion for what

values for estimated effects indicate significant factor or interaction effects. In addition, how do we

measure amount of departure from the straight line pattern .ANOVA meets this need by how much

an estimate must differ from zero in order to be judged “statistically significant”. The ANOVA result

is presented in Table 6. This analysis has been carried out for a level of significance of 5%, i.e., for a

level of confidence of 95%. From the ANOVA results, it is concluded that the factors A, B, C, D and

their interactions AB, AD, BC, BD and CD have significant effect on EWR and AC has no effect at

95% confidence level. As the interaction effect of AB, AD, BC, BD and CD seems to be significant

to the EWR, the average values of the EWR are calculated for all the combinations. By using the

values of interaction, the significant interaction graphs are drawn for each combination of levels. The

significant interactions between the parameters (AB, AD, BC, BD and CD) are shown in Figs. 4 and

8. The insignificant interactions (AC) is presented in shown in Fig 9.



118

Table 5: Response table for electrode wear rate (EWR)

S. No EWR

mm3/min.

A B C D AB AC AD

-1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1

1 1.115 1.115

1.115

1.115

1.115

1.115

1.115

1.115

2 0.885 0.885 0.885

0.885

0.885

0.885 0.885 0.885

3 1.152 1.152 1.152 1.152

1.152

1.152 1.152 1.152

4 0.923 0.923 0.923 0.923

0.923

0.923 0.923

0.923

5 1.232 1.232 1.232 1.232 1.232

1.232 1.232

1.232

6 0.985 0.985 0.985 0.985 0.985

0.985 0.985 0.985

7 1.267 1.267 1.267 1.267 1.267

1.267 1.267 1.267

8 1.012 1.012 1.012 1.012 1.012

1.012 1.012 1.012

9 0.970 0.970 0.970 0.970

0.970 0.970 0.970 0.970

10 0.785 0.785 0.785 0.785

0.785 0.785 0.785 0.785

11 1.005 1.005 1.005 1.005

1.005 1.005

1.005 1.005

12 0.813 0.813 0.813 0.813

0.813 0.813 0.813 0.813

13 1.082 1.082 1.082

1.082

1.082 1.082 1.082 1.082

14 0.865 0.865 0.865

0.865

0.865 0.865 0.865 0.865

15 1.102 1.102

1.102

1.102 1.102 1.102

1.102 1.102

16 1.012

1.012

1.012

1.012

1.012

1.012

1.012

1.012

Average 1.012 1.115 0.910 0.989 1.035 0.956 1.069 1.071 0.954 1.005 1.019 1.011 1.014 0.995 1.030

Effect

-0.205 0.046 0.113 -0.117 0.014 0.003 0.035

S. No BC BD CD ABC ABD ACD BCD ABCD

-1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1

1

1.115 1.115 1.115 1.115 1.115 1.115 1.115 1.115

2

0.885 0.885 0.885 0.885 0.885 0.885 0.885 0.885

3 1.152 1.152 1.152 1.152 1.152 1.152 1.152 1.152

4 0.923 0.923 0.923 0.923 0.923 0.923 0.923 0.923

5 1.232 1.232 1.232

1.232 1.232

1.232 1.232 1.232

6 0.985 0.985 0.985

0.985 0.985 0.985

0.985 0.985

7 1.267 1.267

1.267 1.267 1.267 1.267 1.267

1.267

8 1.012 1.012

1.012 1.012 1.012

1.012 1.012 1.012

9 0.970 0.970

0.970 0.970 0.970 0.970 0.970 0.970

10 0.785 0.785 0.785 0.785 0.785 0.785 0.785 0.785

11 1.005 1.005 1.005

1.005 1.005

1.005 1.005

1.005

12 0.813 0.813 0.813 0.813 0.813 0.813 0.813 0.813

13 1.082 1.082 1.082 1.082 1.082 1.082 1.082 1.082

14 1.102 1.102 1.102 1.102 1.102 1.102 1.102

1.102

15

1.102 1.102 1.102 1.102 1.102 1.102 1.102 1.102

16

1.012 1.012 1.012 1.012 1.012 1.012 1.012 1.012

Average 1.036 1.018 1.036 1.018 1.008 1.046 1.034 1.020 1.034 1.021 1.005 1.050 1.035 1.020 1.033 1.021

Effect -0.018 -0.018 0.038 -0.014 -0.003 0.045 -0.015 -0.012

In this figures the lines are parallel to each other, which show that there is no interaction

between parameters. By analyzing these figures also evident from ANOVA analysis, it has been

concluded that CD and AD are more interactive than other interactions.



119

Table 6: ANOVA test results

Fig. 3: Normal probability plot

S. no Factors Estimated effects Effect squared

(E2)

DOF Mean square

(MS)

Fratio

1 A -0.205 0.042 1 0.042 84

2 B 0.046 0.002 1 0.002 4

3 C 0.113 0.012 1 0.012 24

4 D -0.117 0.013 1 0.013 26

5 AB 0.014 0.0002 1 0.0002 0.4

6 AC 0.003 0.000009 1 0.000009 0.018

7 AD 0.035 0.0012 1 0.0012 2.4

8 BC -0.018 0.0003 1 0.0003 0.6

9 BD -0.018 0.0003 1 0.0003 0.6

10 CD 0.038 0.0014 1 0.0014 2.8

11 ABC -0.014 0.0002

12 ABD -0.003 0.000009

13 ACD 0.045 0.002

14 BCD -0.015 0.0002

15 ABCD -0.012 0.0001

Error 5 0.0005



120

Fig. 4: Interaction between A and B Fig. 5: Interaction between A and D

Fig. 6: Interaction between B and C Fig. 7: Interaction between B and D

Fig. 8: Interaction between C and D Fig. 9: Interaction between A and C

4. OPTIMIZING THE CHOSEN FACTOR LEVELS TO ATTAIN MINIMUM EWR

From the analysis of response graph, response table, and interaction graphs, the optimal

machining parameters for the AISI 304 SS machining process is achieved for the minimum value of

EWR. The optimal conditions arrived are:

(A) Electrode shape at high level (7 channels).

(B) Pulse current at low level (30 Amp).



121

(C) Pulse on time at low level (100 µsec.).

(D) Pulse off time at high level (75 µsec.).

Based on the above optimum conditions, the minimum value of EWR can be obtained from

the following expression using the values form response table (Table 5) [16, 18].

EWR (min.)= [grand mean] + [contribution of A] + [contribution of B] + [contribution of C] +

[contribution of D] + [contribution of AB] + [contribution of AD] + [contribution of BC] +

[contribution of BD] + [contribution of CD]

………………………………………………………..(3)

EWR (min.)= Ȳ + [A (+1)− Ȳ] + [B (−1)− Ȳ] + [C (−1)− Ȳ] + [D (+1)− Ȳ] +[AB (−1)− Ȳ] + [AD (+1)− Ȳ)] +

[BC (+1)− Ȳ)] + [BD (−1)− Ȳ)] + [CD (−1)− Ȳ)]

EWR (min.)=1.012 + [0.910 − 1.012] + [0.989 –1.012] + [0.956 – 1.012] + [0.954 – 1.012] + [1.006

−1.012] + [1.030 – 1.012] + [1.018 – 1.012] + [1.036 – 1.012] + [1.008 – 1.012]

EWR (min.)= 0.807 ≈ 0.80 mm3 /min.

The above result reveal that the minimum electrode wear on the machining of AISI 304 SS

within the range of factor under investigation is 0.80 mm3/min. To check the validity of the

optimization procedure, the aforementioned EWR value is compared with the experimental values,

obtained for the same optimized conditions and the variation is within the reasonable accuracy

(+5%). Moreover, the equation mentioned earlier (3) can be effectively used to predict the EWR of

EDMed AISI 304 SS drills at a 95% confidence level by multiplying the contributions with

corresponding coded values of the main and interaction factors.

5. DISCUSSIONS

The electrode wear depends on the dielectric flow in the machining zone. If the flow is too

turbulent, it results in an increase in electrode wear. Pulsed injection of the dielectric has enable

reduction of wear due to dielectric flow. [19]

From the results, it can be seen that the EWR is maximum at Multi - 4 channels electrode

shape. The EWR decreases with the increase of number of holes inside the electrode. The reason

being at multi 7 - channels, debris removed from efficient machining area more than multi 4 -

channels thus multi 7 - channels brings fresh dielectric in the inter electrode gap.

It has been observed that EWR increases with increase of peak current (from 30 to 36 Amp). Higher

current density available at the working gap, at higher peak current conditions, generates large

amount of heat. This rapidly overheats the electrode and increases electrode wear rate as it is

concluded in Ref. [20].

It was observed that EWR increases with increment in pulse-on-time. This is because of

formation of ‘black layer’ at tool surface. The black layer formation is due to migration of workpiece

element and carbon from dielectric to the tool electrode surface. This finding has close relationship

with the results presented by Ref. [21].

Also the increase in pulse-off-time decreased the EWR as with long pulse-off time the

dielectric fluid produces the cooling effect on electrode and work piece and hence decreases the

EWR [14].

The observed results shown proved that the main factor, which affects the electrode wear

rate, is electrode shape. The pulse-on-time only plays small role on EDM process. The results



122

indicated that the electrode wear rate is minimum at a pulse-on-time of 100 µsec. In machining, the

interaction between the chosen factors also plays some role in deciding the electrode wear rate. The

results indicate that the interactions namely AB, AD, BC, BD and CD have significant effect on

electrode wear rate. Out of four main factors considered, electrode shape is the most significant

factor which affecting the electrode wear rate; while pulse-on-time is the least significant parameter.

Among the interactions, the interaction pulse-on-time and pulse-off-time is more significant than

other parameters.

6. CONCLUSION

Using design of experiments technique, the parameters, which are having significant

influence on electrode wear rate on the machining of AISI 304 SS, have been studied.

(1) This experimental technique is easier and convenient technique used to study the main and

interaction effects of different influential combinations of machining parameters affecting

electrode wear rate.

(2) Electrode shape is the factor, which has greater influence on EWR, followed by pulse off time.

(3) The interaction also play some role in deciding the EWR on the EDM of AISI 304 SS. The

interaction between pulse on time and pulse off time has more influence comparing with other

interactions on EWR on the machining of AISI 304 SS.

(4) The parameters considered in the experiments are optimized to attain minimum EWR using

response graph, response table, normal probability plot, interaction graphs and analysis of

variance (ANOVA) technique.

(5) The optimization procedure can be used to predict the EWR for EDM of AISI 304 SS within

the ranges of variable studied. However, the validity of the procedure is limited to the range of

factors considered for the experimentation.

7. REFERENCES

1. C.A. Rashed, L. Romoli, F. Tantussi, F. Fuso, L. Bertoncini, M. Fiaschi, M. Allegrini and G.

Dini, (2014), Experimental optimization of micro-electrical discharge drilling process from

the perspective of inner surface enhancement measured by shear-force microscopy, CIRP

Journal of Manufacturing Science and Technology, Vol. 7, pp.11-19.

2. P. S. Rao, B. S. Reddy, J. S. Kumar and K. V. Reddy, (2010), Fuzzy Modeling for Electrical

Discharge Machining of AISI 304 Stainless Steel, Journal of Applied Sciences Research,

Vol. 6, No.11, pp. 1687-1700.

3. S. Dhanik, S. Joshi, N. Ramakrishnan, and P. Apte, (2005), Evolution of EDM process

modelling and development towards modelling of the micro-EDM process, International

Journal of Manufacturing Technology and Management, Vol. 7, No.2, pp.157-180.

4. K.H. Ho, and S.T. Newman, (2003), State of Art Electrical Discharge Machining (EDM),

International Journal of Machine Tools and Manufacture, Volu. 43, No.13, pp.1287-1300.

5. J. Marafona, and C.A. Wykes, (2000), New Method of Optimizing Material Removal Rate

using EDM with Copper-tungsten Electrodes, International Journal of Machine Tools and

Manufacture, Vol. 40, No.2, pp.153-164.

6. A.A. Khan and S. Mridha ( 2007), Performance of Copper and Aluminum Electrodes during

EDM of Stainless Steel and Carbide, The International Journal for Manufacturing Science

and Production, Vol. 7, No.1, pp.1-7.



123

7. P. R. Kubade and V. S. Jadhav, (2012), An experimental investigation of electrode wear rate

(EWR), material removal rate (MRR) and radial overcut (ROC) in EDM of high carbon-high

chromium steel (AISI D3), International Journal of Engineering and Advanced Technology

Vol.1, Issue 5, pp.135-140.

8. M. Sahoo, R. N. Pramanik and D. Sahoo, (2013), Experimental investigation of machining of

tungsten carbide by EDM and its mathematical expression, International Journal of

Mechanical and Production Engineering , Vol.2, Issue 1, pp.138-150.

9. P. S. Rao, J. S. Kumar, K. V. K. Reddy and B. S. Reddy, (2010), Parametric study of

electrical discharge machining of AISI 304 stainless steel International Journal of

Engineering Science and Technology, Vol. 2 No.8, pp.3535-3550.

10. R. Atefi, N. Javam, A. Razmavar and F. Teimoori, (2012), The Influence of EDM Parameters

in Finishing Stage on Surface Quality, MRR and EWR, Research Journal of Applied

Sciences, Engineering and Technology Vol.4, No.10, pp.1287-1294.

11. L. Li, L. Gu, X. Xi and W. Zhao, (2012), Influence of flushing on performance of EDM with

bunched electrode, Int J Adv Manuf Technol. Vol.58, pp187–194.

12. O. Yilmaz and M. A. Okka, (2010), Effect of single and multi-channel electrodes application

on EDM fast hole drilling performance, Int J Adv Manuf Technol, Vol. 51, pp.185–194.

13. S.N. Mehta, (2013), Investigation of optimum process parameters using genetic algorithm

based neural networks during EDM of 64WC-9Co, International Journal of Engineering

Research and Technology, Vol. 6, No. 5, pp. 701-708.

14. M.M. Rahman, (2012), Modeling of machining parameters of Ti-6Al-4V for electric

discharge machining: A neural network approach, Scientific Research and Essays, Vol. 7,

No. 8, pp. 881-890.

15. A. N Sait, S. Aravindan and A. N. Haq, (2009), Optimisation of machining parameters of

glass-fibre-reinforced plastic (GFRP) pipes by desirability function analysis using Taguchi

technique, Int J Adv Manuf Technol Vol, 43, pp.581–589.

16. S. Ravi, V. Balasubramanian, S. Babu and S. N. Nasser, (2004), Assessment of factors

influencing the fatigue life of strength mis-matched HSLA steel weldments. Mater. Des.

Vol.25, pp. 125–135.

17. T. Barker, (1985), Quality by experimental design. New York: Marcel Dekker.

18. R. Lochner and J. Mater, (1990), Designing for quality, London: Chapman and Hall.

19. S.H.Tomadi, M.A.Hassan, Z. Hamedon, R.Daud and A.G.Khalid, (2009), Analysis of the

influence of EDM parameters on surface quality, material removal rate and electrode wear of

tungsten carbide, Proceedings of the International MultiConference of Engineers and

Computer Scientists, Vol II, March 18 – 20.

20. P. M. Arvindbhai, (2011), An investigation and analysis of process parameters for EDM

drilling machine using Taguchi method, PhD thesis, Saurashtra University, Rajkot.

21. P. S. Bharti, (2012), Optimization of process parameters of electric discharge machining

based on neural networks and Taguchi’s method, PhD thesis, Guru Gobind Singh

Indraprastha University, Dwarka, Delhi.

22. A. Parshuramulu, K. Buschaiah and P. Laxminarayana, “A Study on Influence of Polarity on

the Machining Characteristics of Sinker EDM”, International Journal of Advanced Research

in Engineering & Technology (IJARET), Volume 4, Issue 3, 2013, pp. 158 - 162, ISSN Print:

0976-6480, ISSN Online: 0976-6499.

23. Mane S.G. and Hargude N.V., “An Overview of Experimental Investigation of Near Dry

Electrical Discharge Machining Process”, International Journal of Advanced Research in

Engineering & Technology (IJARET), Volume 3, Issue 2, 2012, pp. 22 - 36, ISSN Print:

0976-6480, ISSN Online: 0976-6499.



124

AUTHOR BIOGRAPHY

Dr. Maan A. Tawfiq received his PhD in Production Engineering and Metallurgy at University of

Technology, and currently is an assistant professor at this university. His research interests mainly

include advanced manufacturing methods.

Saad Hameed Najem received his MSc in Production Engineering and Metallurgy

at University of Technology, and currently is a PhD student at this university under

supervision of Assistant Professor Maan A. Tawfiq. He is doing research in ED

Machining.