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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

 

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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   ABSTRACT
Published date:
08 Oct 2015
 
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.
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