Electro discharge machining (EDM) process need to be optimized when a new material invented or even if some process variables changed. This process has many variables and it is always difficult to get the optimum set of variables by chance. Therefore, an optimization process need to be conducted considering different combinations of machining parameters as well as other variables even if the process were optimized for a certain set of variables. Optimization of the EDM process for machining stainless steel 304 (SS304) (ASTM A240) was studied in this paper. Signal-to-noise ratio (S/N) was calculated for each performance measures, and multi response performance index (MRPI) was generated using fuzzy logic inference system. Optimal machining parameters for machining SS304 materials were identified, namely current 10, pulse on time 60 μs, and pulse off time 35 μs. Analyses of variances (ANOVA) method was used as well to see which machining parameter has significant effect on the performance measures. The result of ANOVA indicates that pulse off time and current are the most significant machining parameters in affecting the performance measures, with the pulse off time being the most significant parameter.

1.
El-Hofy
,
H. A.-G.
,
2005
,
Advanced Machining Processes; Non Traditional and Hybrid Machining Processes
,
McGraw-Hill
, New York.
2.
Ho
,
K. H.
, and
Newman
,
S. T.
,
2003
, “
State of the Art Electrical Discharge Machining (EDM)
,”
Int. J. Mach. Tools Manuf.
,
43
(
13
), pp.
1287
1300
.
3.
Rajesh
,
R.
, and
Anand
,
M. D.
,
2012
, “
The Optimization of the Electro-Discharge Machining Process Using Response Surface Methodology and Genetic Algorithms
,”
Procedia Eng.
,
38
, pp.
3941
3950
.
4.
Krishna Mohana Rao
,
G.
,
Rangajanardhaa
,
G.
,
Hanumantha Rao
,
D.
, and
Sreenivasa Rao
,
M.
,
2009
, “
Development of Hybrid Model and Optimization of Surface Roughness in Electric Discharge Machining Using Artificial Neural Networks and Genetic Algorithm
,”
J. Mater. Process. Technol.
,
209
(
3
), pp.
1512
1520
.
5.
AzadiMoghaddam
,
M.
, and
Kolahan
,
F.
,
2015
, “
Optimization of EDM Process Parameters Using Statistical Analysis and Simulated Annealing Algorithm
,”
Int. J. Eng. (IJE), Trans. A Basics
,
28
(
1
), pp.
154
163
.http://www.ije.ir/abstract/%7BVolume:28-Transactions:A-Number:1%7D/=1873
6.
Assarzadeh
,
S.
, and
Ghoreishi
,
M.
,
2013
, “
Statistical Modeling and Optimization of Process Parameters in Electro-Discharge Machining of Cobalt-Bonded Tungsten Carbide Composite (WC/6%Co)
,”
Procedia CIRP
,
6
, pp.
463
468
.
7.
Habib
,
S. S.
,
2009
, “
Study of the Parameters in Electrical Discharge Machining Through Response Surface Methodology Approach
,”
Appl. Math. Model.
,
33
(
12
), pp.
4397
4407
.
8.
Joshi
,
S. N.
, and
Pande
,
S. S.
,
2010
, “
Thermo-Physical Modeling of Die-Sinking EDM Process
,”
J. Manuf. Process.
,
12
(
1
), pp.
45
56
.
9.
Lin
,
C. L.
,
Lin
,
J. L.
, and
Ko
,
T. C.
,
2002
, “
Optimisation of the EDM Process Based on the Orthogonal Array With Fuzzy Logic and Grey Relational Analysis Method
,”
Int. J. Adv. Manuf. Technol.
,
19
(
4
), pp.
271
277
.
10.
Marani Barzani
,
M.
,
Zalnezhad
,
E.
,
Sarhan
,
A. A. D.
,
Farahany
,
S.
, and
Ramesh
,
S.
,
2015
, “
Fuzzy Logic Based Model for Predicting Surface Roughness of Machined Al–Si–Cu–Fe Die Casting Alloy Using Different Additives-Turning
,”
Measurement
,
61
, pp.
150
161
.
11.
Yilmaz
,
O.
,
Eyercioglu
,
O.
, and
Gindy
,
N. N. Z.
,
2006
, “
A User-Friendly Fuzzy-Based System for the Selection of Electro Discharge Machining Process Parameters
,”
J. Mater. Process. Technol.
,
172
(
3
), pp.
363
371
.
12.
Lin
,
J. L.
,
Wang
,
K. S.
,
Yan
,
B. H.
, and
Tarng
,
Y. S.
,
2000
, “
Optimization of the Electrical Discharge Machining Process Based on the Taguchi Method With Fuzzy Logics
,”
J. Mater. Process. Technol.
,
102
(
1–3
), pp.
48
55
.
13.
Biswas
,
C. K.
, and
Dewangan
,
S.
,
2012
, “
Optimisation of EDM Process With Fuzzy Logic Technique
,”
International Conference on Metallurgical, Manufacturing and Mechanical Engineering
(
ICMMME
), Dubai, United Arab Emirates, Dec. 26–27, pp.
346
349
.http://psrcentre.org/images/extraimages/98.%201412204.pdf
14.
Tarng
,
Y. S.
,
Tseng
,
C. M.
, and
Chung
,
L. K.
,
1997
, “
A Fuzzy Pulse Discriminating System for Electrical Discharge Machining
,”
Int. J. Mach. Tools Manuf.
,
37
(
4
), pp.
511
522
.
15.
McGeough
,
J. A.
,
1988
,
Advanced Methods of Machining
,
1st ed.
,
Chapman and Hall Ltd
, London.
16.
Zadeh
,
L. A.
,
1976
, “
A Fuzzy-Algorithm Approach to the Definition of Complex or Imprecise Concepts
,”
Int. J. Man Mach. Stud.
,
8
(
3
), pp.
249
291
.
17.
Jiang
,
B. C.
, and
Hsu
,
C. H.
,
2003
, “
Development of a Fuzzy Decision Model for Manufacturability Evaluation
,”
J. Intell. Manuf.
,
14
(
2
), pp.
169
181
.
18.
Tzeng
,
Y.
, and
Chen
,
F.
,
2007
, “
Multi-Objective Optimisation of High-Speed Electrical Discharge Machining Process Using a Taguchi Fuzzy-Based Approach
,”
Mater. Des.
,
28
(
4
), pp.
1159
1168
.
19.
ASTM,
2016
, “Standard Specification for Chromium and Chromium-Nickel Stainless Steel Plate, Sheet, and Strip for Pressure Vessels and for General Applications,” ASTM International, West Conshohocken, PA, Standard No.
ASTM A240/A240M-16a
.
20.
Majumder
,
A.
,
2013
, “
Process Parameter Optimization During EDM of AISI 316 LN Stainless Steel by Using Fuzzy Based Multi-Objective PSO
,”
J. Mech. Sci. Technol.
,
27
(
7
), pp.
2143
2151
.
21.
Das
,
M. K.
,
Kumar
,
K.
,
Barman
,
T. K.
, and
Sahoo
,
P.
,
2013
, “
Optimization of Surface Roughness and MRR in EDM Using WPCA
,”
Procedia Eng.
,
64
, pp.
446
455
.
22.
Sharma
,
P.
,
Singh
,
S.
, and
Mishra
,
D. R.
,
2014
, “
Electrical Discharge Machining of AISI 329 Stainless Steel Using Copper and Brass Rotary Tubular Electrode
,”
Procedia Mater. Sci.
,
5
, pp.
1771
1780
.
23.
Kolli
,
M.
, and
Kumar
,
A.
,
2014
, “
Effect of Boron Carbide Powder Mixed Into Dielectric Fluid on Electrical Discharge Machining of Titanium Alloy
,”
Procedia Mater. Sci.
,
5
, pp.
1957
1965
.
24.
Dastagiri
,
M.
, and
Kumar
,
A. H.
,
2014
, “
Experimental Investigation of EDM Parameters on Stainless Steel & En41b
,”
Procedia Eng.
,
97
, pp.
1551
1564
.
25.
Shashikant
,
Roy
,
A. K.
, and
Kumar
,
K.
,
2014
, “
Effect and Optimization of Various Machine Process Parameters on the Surface Roughness in EDM for an EN41 Material Using Grey-Taguchi
,”
Procedia Mater. Sci.
,
5
, pp.
1702
1709
.
26.
Gopalakannan
,
S.
,
Senthilvelan
,
T.
, and
Ranganathan
,
S.
,
2012
, “
Modeling and Optimization of EDM Process Parameters on Machining of Al 7075-B4C MMC Using RSM
,”
Procedia Eng.
,
38
, pp.
685
690
.
27.
Gopalakannan
,
S.
, and
Senthilvelan
,
T.
,
2013
, “
Application of Response Surface Method on Machining of Al–SiC Nano-Composites
,”
Measurement
,
46
(
8
), pp.
2705
2715
.
28.
Lin
,
M.
,
Tsao
,
C.
,
Hsu
,
C.
,
Chiou
,
A.
,
Huang
,
P.
, and
Lin
,
Y.
,
2013
, “
Optimization of Micro Milling Electrical Discharge Machining of Inconel 718 by Grey-Taguchi Method
,”
Trans. Nonferrous Met. Soc. China
,
23
(
3
), pp.
661
666
.
29.
Joshi
,
S. N.
, and
Pande
,
S. S.
,
2011
, “
Intelligent Process Modeling and Optimization of Die-Sinking Electric Discharge Machining
,”
Appl. Soft Comput.
,
11
(
2
), pp.
2743
2755
.
30.
Rajmohan
,
T.
,
Prabhu
,
R.
,
Rao
,
G. S.
, and
Palanikumar
,
K.
,
2012
, “
Optimization of Machining Parameters in Electrical Discharge Machining (EDM) of 304 Stainless Steel
,”
Procedia Eng.
,
38
, pp.
1030
1036
.
31.
Shandilya
,
P.
,
Jain
,
P. K.
, and
Jain
,
N. K.
,
2012
, “
Parametric Optimization During Wire Electrical Discharge Machining Using Response Surface Methodology
,”
Procedia Eng.
,
38
, pp.
2371
2377
.
32.
Jegan
,
T. M. C.
,
Anand
,
M. D.
, and
Ravindran
,
D.
,
2012
, “
Determination of Electro Discharge Machining Parameters in AISI202 Stainless Steel Using Grey Relational Analysis
,”
Procedia Eng.
,
38
, pp.
4005
4012
.
33.
Shanmugam
,
S. V.
,
Krishnaraj
,
V.
,
Jagdeesh
,
K. A.
,
Kumar
,
S. V.
, and
Subash
,
S.
,
2013
, “
Numerical Modelling of Electro-Discharge Machining Process Using Moving Mesh Feature
,”
Procedia Eng.
,
64
, pp.
747
756
.
34.
Das
,
M. K.
,
Kumar
,
K.
,
Barman
,
T. K.
, and
Sahoo
,
P.
,
2014
, “
Optimization of MRR and Surface Roughness in PAC of EN 31 Steel Using Weighted Principal Component Analysis
,”
Procedia Technol.
,
14
, pp.
211
218
.
35.
Balasubramanian
,
P.
, and
Senthilvelan
,
T.
,
2014
, “
Optimization of Machining Parameters in EDM Process Using Cast and Sintered Copper Electrodes
,”
Procedia Mater. Sci.
,
6
, pp.
1292
1302
.
36.
Dewangan
,
S.
,
Biswas
,
C. K.
, and
Gangopadhyay
,
S.
,
2014
, “
Optimization of the Surface Integrity Characteristics of EDM Process Using PCA Based Grey Relation Investigation
,”
Procedia Mater. Sci.
,
6
, pp.
1091
1096
.
37.
Ugrasen
,
G.
,
Ravindra
,
H. V.
,
Prakash
,
G. V. N.
, and
Keshavamurthy
,
R.
,
2014
, “
Process Optimization and Estimation of Machining Performances Using Artificial Neural Network in Wire EDM
,”
Procedia Mater. Sci.
,
6
, pp.
1752
1760
.
38.
Chandramouli
,
S.
, and
Eswaraiah
,
K.
,
2017
, “
Optimization of EDM Process Parameters in Machining of 17-4 PH Steel Using Taguchi Method
,”
Mater. Today Proc.
,
4
(
2
), pp.
2040
2047
.
39.
Gangil
,
M.
, and
Pradhan
,
M. K.
,
2017
, “
Review on Modelling and Optimization of Electrical Discharge Machining Process Using Modern Techniques
,”
Mater. Today Proc.
,
4
(
2
), pp.
2048
2057
.
40.
Mohanty
,
C. P.
,
Mahapatra
,
S. S.
, and
Singh
,
M. R.
,
2017
, “
An Intelligent Approach to Optimize the EDM Process Parameters Using Utility Concept and QPSO Algorithm
,”
Eng. Sci. Technol. an Int. J.
,
20
(
2
), pp.
552
562
.
41.
Hanjie
,
X.
,
Dalin
,
R.
, and
Shibo
,
W.
,
2017
, “
EDM Machining Quality Control and Parameters Optimization
,”
Int. J. Adv. Manuf. Technol.
,
89
(
5–8
), pp.
1307
1315
.
42.
Dweiri
,
F.
,
Al-Jarrah
,
M.
, and
Al-Wedyan
,
H.
,
2003
, “
Fuzzy Surface Roughness Modeling of CNC Down Milling of Alumic-79
,”
J. Mater. Process. Technol.
,
133
(
3
), pp.
266
275
.
43.
Ooi
,
M. E.
,
Sayuti
,
M.
, and
Sarhan
,
A. A. D.
,
2015
, “
Fuzzy Logic-Based Approach to Investigate the Novel Uses of Nano Suspended Lubrication in Precise Machining of Aerospace AL Tempered Grade 6061
,”
J. Clean. Prod.
,
89
, pp.
286
295
.
44.
Conţiu
,
G.
,
Popa
,
M. S.
,
Socaciu
,
L.
, and
Pop
,
G.
,
2015
, “
Fuzzy Analytical Hierarchy Process Applied to Determine the Material Machinability in EDM Process
,”
Appl. Math. Mech. Eng.
,
58
(
2
), pp.
385
394
.http://atna-mam.utcluj.ro/index.php/Acta/article/view/700.
45.
Lin
,
C.-T.
,
Chung
,
I.-F.
, and
Huang
,
S.-Y.
,
2001
, “
Improvement of Machining Accuracy by Fuzzy Logic at Corner Parts for Wire-EDM
,”
Fuzzy Sets Syst.
,
122
(
3
), pp.
499
511
.
46.
Sengottuvel
,
P.
,
Satishkumar
,
S.
, and
Dinakaran
,
D.
,
2013
, “
Optimization of Multiple Characteristics of EDM Parameters Based on Desirability Approach and Fuzzy Modeling
,”
Procedia Eng.
,
64
, pp.
1069
1078
.
47.
Kaneko
,
T.
, and
Onodera
,
T.
,
2004
, “
Improvement in Machining Performance of Die-Sinking EDM by Using Self-Adjusting Fuzzy Control
,”
J. Mater. Process. Technol.
,
149
(
1–3
), pp.
204
211
.
48.
Yan
,
M.-T.
,
2010
, “
An Adaptive Control System With Self-Organizing Fuzzy Sliding Mode Control Strategy for Micro Wire-EDM Machines
,”
Int. J. Adv. Manuf. Technol.
,
50
(
1–4
), pp.
315
328
.
49.
Shing Roger Jang
,
J. Y. H.
,
1993
, “
ANFIS: Adaptive-Network-Based Fuzzy Inference System
,”
IEEE Trans. Syst. Cybern.
,
23
(
3
), pp.
665
685
.
50.
Çaydaş
,
U.
,
Hasçalık
,
A.
, and
Ekici
,
S.
,
2009
, “
An Adaptive Neuro-Fuzzy Inference System (ANFIS) Model for Wire-EDM
,”
Expert Syst. Appl.
,
36
(
3
), pp.
6135
6139
.
51.
Suganthi
,
X. H.
,
Natarajan
,
U.
,
Sathiyamurthy
,
S.
, and
Chidambaram
,
K.
,
2013
, “
Prediction of Quality Responses in Micro-EDM Process Using an Adaptive Neuro-Fuzzy Inference System (ANFIS) Model
,”
Int. J. Adv. Manuf. Technol.
,
68
(
1–4
), pp.
339
347
.
52.
Shabgard
,
M. R.
,
Badamchizadeh
,
M. A.
,
Ranjbary
,
G.
, and
Amini
,
K.
,
2013
, “
Fuzzy Approach to Select Machining Parameters in Electrical Discharge Machining (EDM) and Ultrasonic-Assisted EDM Processes
,”
J. Manuf. Syst.
,
32
(
1
), pp.
32
39
.
53.
Pratiwi
,
D. K.
, and
Yudo
,
E.
,
2017
, “
The Effect of Sinking Parameters to Optimize Response at Edm of Aisi H13 Using Taguchi–Fuzzy Method
,”
J. Mech. Sci. Eng.
,
4
(
1
), pp.
11
15
.http://ejournal.unsri.ac.id/index.php/jmse/article/view/4135
54.
Asal
,
V. D.
, and
Patel
,
R. I.
, and
Choudhary, A. B.
,
2013
, “
Optimization of Process Parameters of EDM Using ANOVA Method
,”
Int. J. Eng. Res. Appl.
,
3
(
2
), pp.
1119
1125
.http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.414.8683&rep=rep1&type=pdf
55.
Prajapati
,
U.
,
Prajapati
,
J.
,
Modi
,
P.
, and
Banker
,
K.
,
2014
, “
A Review of Parameter Optimization of Electro Discharge Machine by Using Taguchi Method
,”
Int. J. Appl. Innovation Eng. Manage.
,
3
(
8)
, pp.
20
24
.http://www.ijsrd.com/Article.php?manuscript=IJSRDV2I4056
56.
Patel
,
D.
,
Deshpande
,
V.
,
Jha
,
E.
,
Patel
,
V.
,
Desai
,
S.
, and
Patel
,
J.
,
2015
, “
Study of Sand Composition on Mould Properties and Selection of Taguchi Orthogonal Array
,”
Int. J. Eng. Sci. Res.
,
2
(3), p. 31.https://www.researchgate.net/publication/282247404_STUDY_OF_SAND_COMPOSITION_ON_MOULD_PROPERTIES_AND_SELECTION_OF_TAGUCHI_ORTHOGONAL_ARRAY_FOR_DESIGN_OF_EXPERIMENTS
57.
Kuo
,
C.-F. J.
, and
Lin
,
W.-T.
,
2017
, “
A Study of Multi-Quality Processing Parameter Optimization for Sueded Fabric
,”
Text. Res. J.
,
87
(
4
), pp.
389
398
.
58.
BesterField
,
D. H.
,
Besterfield-Michna
,
C.
,
Besterfield-scare
,
M.
,
Besterfield
,
G. H.
,
Undhwareshe
,
H.
, and
Undhwareshe
,
R.
,
2015
,
Total Quality Management
,
4th ed.
, Pearson, Noida, India.
You do not currently have access to this content.