Optimization of wire electrical discharge machining using statistical methods coupled with artificial intelligence techniques and soft computing

Abstract

Wire electrical discharge machining (WEDM) is a special form of electrical discharge machining that uses a small diameter wire as the electrode to cut a narrow kerf in the workpiece. Although it is a simple concept, the performance of the process is highly dependent on the operating parameters. The aim of this work is to optimize WEDM operating parameters with the objective of achieving a maximum material removal rate (MRR) and minimum surface roughness (SR) for an AISI 304 stainless steel workpiece. This work compares the performance of response surface methodology (RSM) and artificial neural network (ANN) based on the coefficient of determination, root mean square error, and absolute average deviation calculations. This is followed by implementing the fuzzy logic technique to get the optimal operating parameters. The results show that using both the RSM and ANN is more adequate and reliable in predicting the MRR and SR. In addition, it is shown that by increasing the peak current, the pulse on time, and by decreasing pulse off time, the resulting workpiece surface is rougher despite achieving a higher material removal rate. It is concluded that the optimal process parameters combination that achieves the maximum MRR and minimum SR is 25 µs pulse on time, 5 µs pulse off time, and 6 A peak current.