Optimization of electric discharge machining process using the response surface methodology and genetic algorithm approach

This study is analyzed the material removal rate, electrode wear ratio and workpiece surface finish on process parameters during the manufacture of SKD61 by electrical discharge machining (EDM). A hybrid method including a back-propagation neural network (BPNN), a genetic algorithm (GA), and response surface methodology (RSM) were proposed to determine optimal parameter settings of the EDM process. Specimens were prepared under different EDM processing conditions according to a Taguchi orthogonal array table. The results of 18 experimental runs were utilized to train the BPNN to predict the material removal rate (MRR), relative electrode wear ratio (REWR) and roughness average (Ra) properties. Simultaneously, the RSM and GA approaches were individually applied to search for an optimal setting. In addition, analysis of variance (ANOVA) was implemented to identify significant factors for the EDM process parameters, and results from the BPNN with integrated GA were compared with those from the RSM approach. The results show that the proposed algorithm of GA approach has better prediction and confirmation results than the RSM method.