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Journal : IPTEK The Journal for Technology and Science

Performance Characteristics Optimization of Electrical Discharge Machining Process Using Back Propagation Neural Network And Genetic Algorithm Napitupulu, Robert; Wahyudi, Arif; Soepangkat, Bobby Oedy Pramoedyo
IPTEK The Journal for Technology and Science Vol 25, No 3 (2014)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v25i3.527

Abstract

This study attempts to model and optimize the complicated electrical discharge machining (EDM) process using soft computing techniques. Artificial neural network (ANN) with back propagation algorithm is used to model the process. In this study, the machining parameters, namely pulse current, on time, off time and gap voltage are optimized with considerations of multiple performance characteristics such as metal removal rate (MRR) and surface roughness. As the output parameters are conflicting in nature so there is no single combination of cutting parameters, which provides the best machining performance. Genetic algorithm (GA) with properly defined objective functions was then adapted to the neural network to determine the optimal multiple performance characteristics.
Multi-Responses Optimization Of Edm Sinking Process Of Aisi D2 Tool Steel Using Taguchi Grey–Fuzzy Method Bobby Oedy Pramoedyo Soepangkat; Arif Wahyudi; Bambang Pramujati
IPTEK The Journal for Technology and Science Vol 25, No 2 (2014)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v25i2.528

Abstract

Rough machining with Electro Discharge Machining (EDM) process gives a large Material Removal Rate (MRR) and high Surface Roughness (SR), while finish machining gives low SR and very slow MRR. In this study, Taguchi method coupled with Grey Relational Analysis (GRA) and fuzzy logic has been applied for optimization of multiple performance characteristics. The EDM machining parameters (gap voltage, pulse current, on time and duty factor) are optimized with considerations of multiple performance characteristics, i.e., MRR and SR. The quality characteristic of MRR is larger-is-better, while the quality characteristic of SR is smaller-is-better. Based on Taguchi method, an L18 mixed-orthogonal array is selected for the experiments. By using the combination of GRA and fuzzy logic, the optimization of complicated multiple performance characteristics was transformed into the optimization of a single response performance index. The most significant machining parameters which affect the multiple performance characteristics were gapvoltage and pulse current. Experimental results have also shown that machining performance characteristics of EDM process can be improved effectively through the combination of Taguchi method, GRA and fuzzy logic.