IPTEK The Journal for Technology and Science
Vol 25, No 3 (2014)

Performance Characteristics Optimization of Electrical Discharge Machining Process Using Back Propagation Neural Network And Genetic Algorithm

Napitupulu, Robert (Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia)
Wahyudi, Arif (Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia)
Soepangkat, Bobby Oedy Pramoedyo (Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia)

Article Info

Publish Date
08 Oct 2015

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.

Copyrights © 2014




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Journal Info
IPTEK The Journal for Technology and Science
Website

Abbrev

jts

Publisher

Institut Teknologi Sepuluh Nopember Surabaya

Subject

Computer Science & IT

Description

IPTEK The Journal for Technology and Science (eISSN: 2088-2033; Print ISSN:0853-4098), is an academic journal on the issued related to natural science and technology. The journal initially published four issues every year, i.e. February, May, August, and November. From 2014, IPTEK the Journal for ...