Nowadays, powder mixed electrical discharge machining (PMEDM) has been used in manufacturing of hard-to-machine materials including super alloys, ceramic materials and etc,. In this study, regression modeling is used to model and analysis the effects of machining parameters on the machining characteristics in the PMEDM process. The process input machining parameters include, grain size of aluminum powder, concentration of the powder, discharge current and pulse duration. The process response characteristics considered are metal removal rate (MRR) and electrode wear rate (EWR). Different regression functions have been fitted to the experimental data to model the machining process. Then, the best and most fitted set of models are identified using analysis of variance (ANOVA). Moreover the influence of individual machining parameters, the interactions between these parameters is also investigated. Finally, a genetic algorithm (GA) procedure has been used to optimize the process parameters for any set of desired outputs. The results show that the proposed solution procedure performs very well in solving such complicated and non-linear optimization problems.

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