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

Robert Napitupulu, Arif Wahyudi, Bobby Oedy Pramoedyo Soepangkat


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.


Electrical discharge machining (EDM); Artificial neural network (ANN); Multiple performance characteristics; Genetic algorithm (GA)

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