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Electric discharge machining (EDM) process is one of the earliest and most extensively used unconventional machining processes. It is a noncontact machining process that uses a series of electric discharges to remove material from an electrically conductive workpiece. This article is aimed to do a comprehensive experimental and thermal investigation of the EDM, which can predict the machining characteristic and then optimize the output parameters with a newly integrated neural network-based methodology for modelling and optimal selection of process variables involved in powder mixed EDM (PMEDM) process. To compare and investigate the effects caused by powder of differently thermo physical properties on the EDM process performance with each other as well as the pure case, a series of experiments were conducted on a specially designed experimental setup developed in the laboratory. Peak current, pulse period, and source voltage are selected as the independent input parameters to evaluate the process performance in terms of material removal rate (MRR) and surface roughness (Ra). In addition, finite element method (FEM) is utilized for thermal analysis on EDM of stainless-steel 630 (SS630) grade. Further, back propagated neural network (BPNN) with feed forward architecture with analysis of variance (ANOVA) is used to find the best fit and approximate solutions to optimization and search problems. Finally, confirmation test results of experimental MRR are compared using the values of MRR obtained using FEM and ANN. Similarly, the test results of experimental Ra also compared with obtained Ra using ANN.

Experimental and Thermal Investigation on Powder Mixed EDM Using FEM and Artificial Neural Networks