30120140501006

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME

AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 5, Issue 1, January (2014), pp. 57-67
© IAEME: www.iaeme.com/ijmet.asp
Journal Impact Factor (2013): 5.7731 (Calculated by GISI)
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IJMET
©IAEME

PROCESS PARAMETER OPTIMISATION IN WEDM OF HCHCR STEEL
USING TAGUHI METHOD AND UTILTIY CONCEPT
Ms. Shalaka Kulkarni* and ManikRodge**
*Research Scholar, **Associate Professor
Production Engineering Dept., SGGSIE&T, Nanded (India)

ABSTRACT
Wire Electrical Discharge Machining (WEDM) is used as a valuable machining tool in the
world of non-traditional machining due to various features which includes higher degree of accuracy,
fine surface quality and good productivity. WEDM consists of large number of process parameters,
thus it is difficult to obtain a combination of optimum parameters which provides higher accuracy.
Optimization of a single response is often carried out with the well known technique Taguchi
method. This method results in the solution which gives optimum value of each response. During the
manufacturing the performance of a product can be evaluated by several response variables.
Optimization of a single response may result in the non optimum solution for the remaining
responses; this problem can be solved by multi-characteristics response optimization. In this paper,
an attempt is made to study the effect of various process parameters such as pulse on time, pulse off
time, wire feed, wire tension, upper flush and lower flush for high carbon high chromium steel. The
experimentation has been completed with the help of Taguchi’s L25 Orthogonal Array. Taguchi’s
method and utility concept is used to optimize the process parameters on multiple performance
characteristics such as Material removal rate, surface finish and kerf width.The experimental result
analysis showed that the combination of higher levels of pulse on time, pulse off time, wire feed and
lower flush and lower level of wire tension and upper flush is essential to achieve simultaneous
maximization of material removal rate and minimization of surface roughness and kerf width.
KEYWORDS: Genetic algorithm (GA), Gray based analysis (GRA), Orthogonal array (OA), Signal
to noise ratio (S/N Ratio), Techniques for Order Preference by Similarity to Ideal Solution
(TOPSIS).

57

1. INTRODUCTION
Accompanying the development of mechanical industry, the demands for alloy materials
having high hardness, toughness and impact resistance are increasing. Wire EDM machines are able
to cut the materials regardless of its hardness. The machines also specialize in cutting complex
contours on fragile geometries that would be difficult to produce using conventional cutting methods.
Such complex geometries can be found in intricate punches, dies, and various spindles. In the
manufacturing of these products High carbon high chromium steel is used due to their good
dimensional accuracy, good wear resistance, higher machinability, very high compressive strength,
good corrosion resistance, and effective cost.
WEDM is a non-traditional thermoelectric process, which erode materials from the
workpiece by series of discrete sparks between tool and workpiece. Deionized water is used as a
dielectric medium. This dielectric medium provides insulation and ionization to the system.
Enormous amount of energy is produced after the generation of spark this causes heating of the tool
and workpiece.This heat is carried away by dielectric medium. Flushing of the dielectric in the spark
gap prevents the contamination of debris and premature discharge.
Surface roughness is the most significant performance measure in quality of a product. Along
with surface roughness material, removal rate is also an important characteristic in various
manufacturing operation. [1]. Hence while manufacturing a product the main objective is to achieve
a higher MRR at lower SR. This need will results in the process of optimization. Taguchi method is a
well known tool of optimization. The parametric optimization of WEDM process has been described
by Mahapatra S.S and Amar patnaik [2]. Results of the optimization shows the combinations of
discharge current, pulse duration, pulse frequency, wire speed, wire tension and dielectric flow rate
giving higher MRR and lower SR. While machining EN-31 tool steel for achieving higher MRR,
discharge currentwas the most significant factor. Sivakiran S. et al [3] predicted MRR values with
6.77% error using regression analysis. Use of full factorial design solves the problem with minimum
number of number of experiments [4]. The analysis gives a combination of process parameters for
better surface finish. The authors [4], [5], [6], [7], [8], [9] also used taguchi method of optimization
of process parameter but all of them have considered single response variable at a time. The
optimum combination for one response variable may result in the non optimum solution for other
responses, when number of responses are to be considered. This problem can be solved with the help
of multiple response optimization method. Modelling and optimization of process parameters in
powder mixed electrical discharge machining (PMEDM) has been studied by Farhad Kolahan [10].
The process output characteristics include MRR and EWR. Grain size of Aluminium powder (S),
concentration of the powder (C), discharge current (I) pulse on time (T) are chosen as control
variables to study the process performance. Regression models are developed using experimental
results. A genetic algorithm (GA) has been employed to determine optimal process parameters for
any desired output values of machining characteristics. Another effective way of solving multi
response problem is to use of taguchi method [9]. This approach reduces complexity and effort of
data analysis. Use of taguchi method to derive objective function and Gray based analysis (GRA) to
carry out multi response optimization in turning process is evaluated by Yigit kazancoglue [11]. A
multi-characteristics response optimization model based on Taguchi and Utility concept is used by
M. Kaladhar et al [1].
From the literature it is found that there is the need of multi response optimization. Various
techniques are available for such optimization which includes genetic algorithm, gray based analysis,
TOPSIS, Taguchi method and utility concept. In this paper, we present a method for finding the
fitness function (several objectives are to be combined to have fitness function) with the
consideration of user preference.

58

2. DESIGN OF EXPERIMENT
2.1 Selection of Orthogonal Array
The experiment design is done based on the Taguchi Method. Genichi Taguchi a Japanese
scientist developed a technique based on Orthogonal Array of experiments. This technique has been
widely used in different fields of engineering to optimize the process parameters [1]. The control
factors considered for the study are Pulse-on time (Ton), Pulse-off time (Toff), Wire feed (Wf), Wire
tension(Wt), Upper flush (Uf), and Lower flush (Lf). Five levels for each control factor are be used.
Based on number of control factors and their levels, L25 orthogonal array (OA) is selected.Table-1
represents various levels of control factors and Table-2 represents experimental plan with assigned
values.
Table-1: Levels of various control factors
Factors
Ton
Toff
Wf
Wt
Uf
Lf

level 1
4
3
5
500
6
5

level 2
5
4
6
600
7
6

level 3
6
5
7
700
8
7

level 4
7
6
8
800
9
8

level 5
8
7
9
900
10
9

Units
µsecond
µsecond
mm/sec
Gm
kg/cm2
kg/cm2

Table -2: L25 OA with assigned values of control Factors
Exp.No.
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

Ton
4
4
4
4
4
5
5
5
5
5
6
6
6
6
6
7
7
7
7
7
8
8
8
8
8

Toff
3
4
5
6
7
3
4
5
6
7
3
4
5
6
7
3
4
5
6
7
3
4
5
6
7

Wf
5
6
7
8
9
8
9
5
6
7
6
7
8
9
5
9
5
6
7
8
7
8
9
5
6

59

Wt
500
600
700
800
900
900
500
600
700
800
800
900
500
600
700
700
800
900
500
600
600
700
800
900
500

Uf
6
7
8
9
10
7
8
9
10
6
8
9
10
6
7
9
10
6
7
8
10
6
7
8
9

Lf
5
6
7
8
9
7
8
9
5
6
9
5
6
7
8
6
7
8
9
5
8
9
5
6
7

2.2 Selection of Material
The work piece material used in this study is High Carbon High Chromium [HCHCr] steel
grade D3 and its chemical composition is given in Table -3.
Table-3: Chemical Composition of HCHCr steel
% Carbon

2.05

% Silicon

0.43

% Manganese

0.25

% Phosphorous

0.029

% Sulphur

0.04

% Chromium

12.08

% Iron

Remainder

2.3 Experimental Work
The experiments were performed on Electronica make maxicut 734 CNC Wire-cut electrical
discharge machining (WEDM). The basic parts of the WEDM machine consists of a wire Electrode,
a work table, and a servo control system, a power supply and dielectric supply system. Maximum
movement of X and Y axis is 300, 400 mm respectively. Maximum Taper angle is 15°per 100 mm.
the wire material used is Zn coated Brass of diameter 0.25 mm.
2.4 Utility Concept
To improve the rational decision making, the evaluations of various attributes should be
combined to give a composite index. Such a composite index is known as utility of a product.The
sum of utilities of each quality attribute represents the overall utility of a product. It is difficult to
obtain the best combination of process parameters, when there are multi-responses to be optimized.
The adoption of weights in the utility concept helps in these difficult situations by differentiating the
relative importance of various responses. If xi represents the measure of effectiveness of i th process
response characteristic and n represents no. of responses, then the overall utility function can be
written as [9]
Uሺxଵ , xଶ … . x୬ ሻ ൌ fሾUଵ ሺxଵ ሻ, Uଶ ሺxଶ ሻ, … U୬ ሺx୬ ሻሿ…………………………………..……...…(1)
where U(X1, X2,...,Xn) is the overall utility of n process response characteristics and Ui(Xi) is utility
of i th response characteristic. Assignment of weights is based on the requirements and priorities
among the various responses. Therefore the general form or weighted from of equation can be
expressed as
60

Uሺxଵ , xଶ … x୬ ሻ ൌ ∑୬ W୧ ‫ כ‬U୧ ሺx୧ ሻ…………………………………………………………. (2)
୧ୀଵ
where,
∑୬ W୧ ൌ 1………………………………………………………………………………… (3)
୧ୀଵ
where Wi is the weight assigned to the ith response characteristic

3. RESULT AND ANALYSIS
The objective of the present work is to minimize surface roughness (SR), kerf width (KW)
and maximize the MRR in WEDM process optimization.Taguchi technique uses S/N ratio as a
performance measure to choose control levels. The S/N ratio considers both the mean and the
variability. In the present work, a multi- response methodology based on Taguchi technique and
Utility concept is used for optimizing the multi-responses (SR, KW and MRR). Taguchi proposed
many different possible S/N ratios to obtain the optimum parameters setting. Two of them are
selected for the present work.
Those are, Larger the better S/N ratio for MRR
ሾߟଵ ሿ ൌ െ10 ‫݃݋݈ כ‬ଵ଴ ቂ

ଵ

ெோோ మ

ቃ……………………………………………………………….. (4)

Smaller the better type S/N ratio for SR
ሾߟଶ ሿ ൌ െ10 ‫݃݋݈ כ‬ଵ଴ ሾܴܵ ଶ ሿ………………………………………………………………… (5)
Smaller the better type S/N ratio for KW
ሾߟଷ ሿ ൌ െ10 ‫݃݋݈ כ‬ଵ଴ ሾ‫ ܹܭ‬ଶ ሿ……………………………………………………………….. (6)
From the utility concept, the multi-response S/N ratio of the overall utility value is given by
ߟை௕௦ ൌ ߟଵ ܹଵ ൅ ߟଶ ܹଶ ൅ ߟଷ ܹଷ ………………………………………………………….... (7)
Where W1, W2&W3 are the weights assigned to the MRR, KW and SR respectively.
Assignment of weights to the performance characteristics are based on experience of engineers,
customer’s requirements and their priorities. In the present work, we have considered MRR as the
first priority and is weighted 50%, the second priority was surface roughness and thus the weight is
30%, while KW was third priority and thus the weight is 20%.
The analysis is done using the popular software specifically used for design of experiment
applications known as MINITAB 15. The S/N ratio for MRR, SR and KW is computed using above
equations for each treatment as shown in Table-4.

61

Table-4: Experimental results for MRR, SR, KW along with S/N ratios
Exp.
MRR η1 for MRR
SR
η2 for SR
KW
η3 for KW
No.
1
2.7
8.6273
2.513
-8.0039
0.0416
27.6182
2
2.6
8.2995
1.841
-5.3011
0.0433
27.2703
3
2.4
7.6043
1.771
-4.9644
0.0433
27.2703
4
2.3
7.2346
1.549
-3.8011
0.044
27.131
5
2.3
7.2346
1.519
-3.6312
0.0466
26.6323
6
2.1
6.4444
1.488
-3.4521
0.0416
27.6182
7
2
6.0206
1.706
-4.6396
0.0383
28.3361
8
2.2
6.8485
1.656
-4.3813
0.044
27.131
9
2.1
6.4444
1.516
-3.614
0.0433
27.2703
10
2.2
6.8485
1.681
-4.5114
0.0423
27.4732
11
2.5
7.9589
2.923
-9.3166
0.0393
28.1122
12
2.3
7.2346
1.903
-5.5888
0.0423
27.4732
13
2.2
6.8485
1.683
-4.5217
0.0433
27.2703
14
2.1
6.4444
1.69
-4.5578
0.04
27.9589
15
2.2
6.8485
1.665
-4.4283
0.04
27.9589
16
2.4
7.6043
1.718
-4.7005
0.0417
27.5973
17
2.3
7.2346
1.683
-4.5217
0.045
26.9358
18
2.3
7.2346
1.6
-4.0824
0.04
27.9589
19
2.5
7.9589
1.911
-5.6253
0.0443
27.072
20
2.6
8.2995
1.846
-5.3247
0.045
26.9358
21
2.7
8.6273
1.731
-4.766
0.044
27.131
22
2.6
8.2995
1.763
-4.9251
0.045
26.9358
23
2.5
7.9589
1.88
-5.4832
0.04
27.9589
24
2.4
7.6043
1.559
-3.857
0.0426
27.4119
25
2.6
8.2995
1.81
-5.1536
0.044
27.131
3.1Singleresponseoptimization
The optimal settings and the predicted optimal values for MRR, SR and KW are determined
individually by Taguchi’s approach. Then, overall mean for S/N ratios MRR, SR and KW are
calculated as average of all treatment responses for each level (Table-5, 6 and 7). The graphical
representation of the effect of the six control factors on MRR, SR and KW is shown in Figure 1, 2
and 3 respectively.
Table-5: Response Table for S/N ratios (larger the better) for MRR
LEVEL
TON
TOFF
WF
WT
UF
LF
1
7.8
7.491
7.433
7.551
7.852
7.713
2
6.521
7.418
7.441
7.647
7.502
7.704
3
7.067
7.299
7.497
7.205
7.36
7.655
4
7.666
7.137
7.444
7.193
7.425
7.447
5
7.506
7.278
7.66
7.053
7.15
8.158
1.637
0.715
0.224
0.52
0.602
0.553
DELTA
1
2
6
5
3
4
RANK
62

Table-6: Response Table for S/N ratios (smaller the better) for SR
LEVEL

TON

TOFF

WF

WT

UF

LF

1

-5.14

-6.048

-5.216

-5.603

-5.038

-5.589

2

-4.12

-4.995

-4.858

-4.578

-5.494

-4.866

3

-5.683

-4.687

-5.62

-4.53

-5.091

-4.526

4

-4.851

-4.291

-4.725

-4.343

-4.405

-5.527

5

-4.837

-4.61

-4.211

-5.576

-4.602

-4.122

DELTA

1.563

1.757

1.41

1.259

1.089

1.467

RANK

2

1

4

5

6

3

Table-7: Response Table for S/N ratios (smaller the better) for KW
LEVEL

TON

TOFF

WF

WT

UF

LF

1

27.18

27.62

27.59

27.45

27.41

27.49

2

27.57

27.39

27.58

27.4

27.55

27.29

3

27.75

27.52

27.61

27.38

27.28

27.41

4

27.3

27.37

27.29

27.7

27.18

27.52

5

27.31

27.23

27.05

27.18

27.7

27.42

DELTA

0.57

0.39

0.57

0.53

0.52

0.24

RANK

1

5

2

3

4

6

Figure 1: Graphs for MRR

63


Figure 2: Graphs for SR

Figure 3: Graphs for KW

3.2MULTI - RESPONSE OPTIMIZATION
The optimal combination of process parameters for simultaneous optimization of material
removal rate (MRR), Surface roughness (SR) and kerf width (KW) is obtained by the mean values of
the multi-response S/N ratio of the overall utility value. Table 8 shows the values of S/N ratio for the
individual response and the S/N ratio for the overall utility.

64

Table 8: L25 OA with multi-response S/N ratios
EXP. NO η1 for MRR η2 for SR η3 for KW η OBSERVED
1
7.4361
8.6273
-8.0039
27.6182
2
8.0135
8.2995
-5.3011
27.2703
3
7.7669
7.6043
-4.9644
27.2703
4
7.9032
7.2346
-3.8011
27.131
5
7.8544
7.2346
-3.6312
26.6323
6
7.7102
6.4444
-3.4521
27.6182
7
7.2856
6.0206
-4.6396
28.3361
8
7.5361
6.8485
-4.3813
27.131
9
7.5921
6.4444
-3.614
27.2703
10
7.5655
6.8485
-4.5114
27.4732
11
6.8069
7.9589
-9.3166
28.1122
12
7.4353
7.2346
-5.5888
27.4732
13
7.5218
6.8485
-4.5217
27.2703
14
7.4466
6.4444
-4.5578
27.9589
15
7.6875
6.8485
-4.4283
27.9589
16
7.9115
7.6043
-4.7005
27.5973
17
7.648
7.2346
-4.5217
26.9358
18
7.9844
7.2346
-4.0824
27.9589
19
7.7063
7.9589
-5.6253
27.072
20
7.9395
8.2995
-5.3247
26.9358
21
8.3101
8.6273
-4.766
27.131
22
8.0594
8.2995
-4.9251
26.9358
23
7.9263
7.9589
-5.4832
27.9589
24
8.1274
7.6043
-3.857
27.4119
25
8.0299
8.2995
-5.1536
27.131

Table 9: Response table for multi objective optimization
LEVEL
TON
TOFF
WF
WT
UF
LF
1
7.7948
7.635
7.687
7.5959
7.6984
7.6659
2
7.5379 7.6884 7.6854 7.8492
7.8279
7.8088
3
7.3796 7.7471 7.7568 7.8035
7.5853
7.7203
4
7.8379 7.7551 7.8268
7.57
7.7632
7.8342
5
8.0906 7.8154 7.6849 7.8223 7.78528 7.5926

4. DISCUSSION
The purpose of the experimentation is to identify the factors which have strong effects on the
machining performance. From mean of S/N ratios (Table 5) for MRR, it is found that pulse-on time
has highest rank ‘1’. Therefore, it has most significant effect on MRR. The wire feed has least effect
on MRR. The order of other influencing parameters for MRR: pulse-off time, upper flush, lower
flush and wire tension. Also, from mean of S/N ratios (Table 6) for SR, it is observed that, the Pulse
65

off time has highest rank ‘1’ and therefore, it affects SR significantly. The Upper flush has least
effect on SR. The order of other influencing parameters for SR is: Pulse on time, Lower Flush, Wire
Feed, Wire tension. Table 7 shows that, for KW, the pulse-on time has highest rank ‘1’ and hence, it
affects KW of the machined surface most significantly. The Lower flush has least effect on KW. The
order of other influencing parameters of KW is: wire feed, wire tension, upper flush, and pulse-off
time.
From Table 5, the optimal combination of process parameters for maximum MRR is found to
be: A5B1C2D1E3F2. The symbols A, B, C, D, E and F represents process parameters: Ton, Toff,
WF, WT, UF and LF respectively and numbers represents the levels.This means, to have maximum
MRR, Ton should be set on level 5, Toff on 1, WF on 2,WT on 1, UF on 3 and LF on 2. Similarly
from Table 6, it is observed that, the optimalcombination of process parameters for minimum SR is:
A2B4C5D4E4F5. This means,to have minimum SR, Ton should be set on level 2, Toff on 4, WF on
5, WT on4, UFon 4 and LF on 5. From Table 7, the optimalcombination of process parameters for
KW is:A3B1C3D4E5F4. This means to have low KW Ton should be set on level 3, Toff on 1, WF
on 3, WT on 4, UF on 5 and LF on 4.
From table 6 optimal combination of process parameter for simultaneous optimization to
obtain maximum MRR, minimum SR and minimum kerf width is found to be A5B5C4D2E2F4. The
symbols A, B, C, D, E and F represents process parameters: Ton, Toff, WF, WT, UF and LF
respectively and numbers represents their respective levels.
5. CONCLUSIONS
Present work is concerned with determining the optimum settings of process parameters for
single as well as multi response optimization during EDMing of high carbon high chromium steel on
the basis of taguchi approach and utility concept. The L25 OA was used for experimental planning.
In the first stage (single response) optimal settings of process parameters were obtained individually
so as to obtain optimum values for MRR, SR and KW respectively. It is found that TON is the most
influencing factor for both KW and MRR, while TOFF has significant effect on SR. In second stage
(multi response) response table establishes the combination of higher levels of pulse on time, pulse
off time, wire feed and lower flush and lower level of wire tension and upper flush is essential for
obtaining optimal value of multiple performance for the predefined weightages.
6. REFERANCES
[1]

[2]

[3]

[4]

Kaladhar M., Subbaiah K. V., Ch. SrinivasaRao and NarayanaRao K., ‘Application of
Taguchi approach and Utility Concept in solving the Multi-objective Problem when turning
AISI 202 Austenitic Stainless Steel’, Journal of Engineering Science and Technology Review
4 (1) (2011) 55-61.
Amar Patnaik., Mahapatra S.S., ‘Parametric Optimization of Wire Electrical Discharge
Machining (WEDM) Process using Taguchi Method’, Journal of Brazil society of mech. Sci.
&Eng, Vol. 28, No. 4,December 2006, PP 422-429.
Bhaskar Reddy C., Sivakiran S., Eswarareddy C., ‘Effect Of Process Parameters On Mrr In
Wire Electrical Discharge Machining Of En31 Steel’, International Journal of Engineering
Research and Applications (IJERA), Vol. 2, Issue 6, November- December 2012, pp.12211226.
Atul Kumar, D. K. Singh, ‘Strategic Optimization and Investigation Effect of Process
Parameters On Performance Of Wire Electric Discharge Machine (WEDM)’, International
Journal of Engineering Science and Technology (IJEST).

66

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]
[13]
[14]

[15]
[16]
[17]

[18]

[19]

[20]

Manoj Malik, Rakesh Kumar Yadav, Nitesh Kumar, Deepak Sharma, Manoj, ‘Optimization
of process parameters of wire EDM using zinc-coated brass wire’, International Journal of
Advanced Technology & Engineering Research (IJATER).
Konda R., Rajurkar K. P., Bishu R. R. , Guha A. and Parson M., ‘Design of experiments to
study and optimize process performance’, Int. J. Quality, Reliability and Management, 16 (1)
(1999) 56–71.
Parashar Vishal, A. Rehaman, J. L. Bhagoria, Y.M. Puri, ‘Kerf width analysis for wire cut
electro discharge machining of SS304L using design of experiments’, Indian Journal of
Science and Technolgy, vol. 3 No. 4, 2010, 369-373.
PujariUjariSrinivasaRao, Dr.KoonaRamji , Prof. BeelaSatyanarayana, ‘Prediction of Material
removal rate for Aluminium BIS-24345 Alloy in wire-cut EDM’, International Journal of
Engineering Science and Technology Vol. 2 (12), 2010, P.N. 7729-7739.
Abbas Al-Refaie, Tai-Hsi Wu, Ming-Hsien Li, ‘An Effective Approach for Solving The
Multi-Response Problem in Taguchi Method’, Jordan Journal of Mechanical and Industrial
Engineering, Volume 2010, Pages 314 – 323.
Farhad Kolahan, and Mohammad Bironro, ‘Modelling and Optimization of Process
Parameters in PMEDM by Genetic Algorithm’, World Academy of Science, Engineering and
Technology 48 2008.
Yigit Kazancoglu, UgurEsme, MelihBayramoglu, OnurGuven, SuedaOzgun, ‘Multi objective
optimization of the cutting forces in turning operations using the Grey-based Taguchi
method’, Original scientific article.
Maria Cristina Recchioni, ‘A Multiobjective Optimization Algorithm to Solve Nonlinear
Systems’, Applied Mathematical Sciences, Vol. 6, 2012, no. 129, 6403 – 6423.
MinghuiGao, LianfenHuang , ‘Intelligent Coverage Optimization with Multi- Objective
Genetic Algorithm in Cellular System’
Gerald Weigert, Sebastian Werner, Dirk Hampel, Hendrikje Heinrich, and Wilfried Sauer,
‘Multi objective decision making – solutions for the optimization of manufacturing
processes’.
Gadakh V.S., ' Parametric optimization of Wire Electrical DiScharge machining usisng
TOPSIS meyhod', Advances in production engineering and management 7 (2012) 3, 157-164.
Prof. S.R.Nipanikar, ‘Parameter optimization of electro discharge machining of AISI D3 steel
material by using Taguchi method’, Journal of Engineering Research and Studies.
Sarkar S., Mitra S. and Bhattacharyya B., ‘Parametric analysis and optimization of wire
electrical discharge machining of titanium aluminide alloy’, Journal of Materials Processing
Technology 159 (2005) 286-294.
Y.S.Sable, R.B.Patil and Dr.M.S.Kadam, “Investigation of MRR in WEDM for Wc-Co
Sintered Composite”, International Journal of Mechanical Engineering & Technology
(IJMET), Volume 4, Issue 3, 2013, pp. 349 - 358, ISSN Print: 0976 – 6340, ISSN Online:
0976 – 6359.
Brij Bhushan Tyagi, Mohd.Parvez, Rupesh Chalisgaonkar and Nitin Sharma, “Optimization
of Process Parameters of Wire Electrical Discharge Machining of AISI 316L”, International
Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012,
pp. 317 - 327, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.
S V Subrahmanyam and M. M. M. Sarcar, “Parametric Optimization for Cutting Speed – A
Statistical Regression Modeling for WEDM”, International Journal of Advanced Research in
Engineering & Technology (IJARET), Volume 4, Issue 1, 2013, pp. 142 - 150, ISSN Print:
0976-6480, ISSN Online: 0976-6499.

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