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Research Papers

Optimization of Electro Discharge Machining Process Parameters With Fuzzy Logic for Stainless Steel 304 (ASTM A240)

[+] Author and Article Information
Alaa M. Ubaid

College of Engineering,
University of Sharjah,
P. O. Box 27272,
Sharjah, United Arab Emirates
e-mail: aubaid@sharjah.ac.ae

Fikri T. Dweiri

College of Engineering,
University of Sharjah,
P. O. Box 27272,
Sharjah, United Arab Emirates
e-mail: fdweiri@sharjah.ac.ae

Shukry H. Aghdeab

Department of Production
Engineering & Metallurgy,
University of Technology,
P. O. Box 35010,
Baghdad, Iraq
e-mail: shukry_hammed@yahoo.com

Laith Abdullah Al-Juboori

Department of Mechanical Engineering,
Higher Colleges of Technology,
P.O. Box 4114,
Fujairah, United Arab Emirates
e-mail: laljuboori@hct.ac.ae

Manuscript received March 23, 2017; final manuscript received September 9, 2017; published online November 17, 2017. Assoc. Editor: Hongqiang Chen.

J. Manuf. Sci. Eng 140(1), 011013 (Nov 17, 2017) (13 pages) Paper No: MANU-17-1164; doi: 10.1115/1.4038139 History: Received March 23, 2017; Revised September 09, 2017

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.

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Figures

Grahic Jump Location
Fig. 3

EDM system components [1]

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Fig. 4

Classification of the spark erosion machining processes [3]

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Fig. 5

Important input and output parameters for EDM process [5]

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Fig. 6

EDM spark description [1]

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Fig. 7

Publications' distribution over the years

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Fig. 8

(a) Fuzzy reasoning and (b) equivalent ANFIS [49]

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Fig. 9

Classification of the various research areas, and possible future research directions [3]

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Fig. 10

Research methodology

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Fig. 11

EDM machine model (CM 323C)

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Fig. 12

S/N Ratio membership function for the EWR

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Fig. 13

S/N Ratio membership function for the MRR

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Fig. 14

Membership functions for the MRPI

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Fig. 15

MRPI Graph for machining parameters levels

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