Abstract
Electrical Discharge Machining (EDM) is a thermal energy based non-traditional shaping process for shaping of hard and brittle electrically conductive materials, but it suffers with low machinability and recast layer formations. The combination of grinding with EDM means enhancement in machining capability, but the process becomes highly complex. Therefore, the assortment of control factors for optimum results is greatly challenging for the industries. The objective of present study is to optimize the control factors such as current, pulse on-time, pulse off-time, wheel RPM and abrasive grit number (GN) to optimize the material removal rate (MRR) and average surface roughness (Ra) for Grinding Aided-EDM process. For this purpose, the simultaneous application of soft computing methods such as Artificial Neural Network (ANN) and Genetic Algorithm (GA) has been employed. The results demonstrate that combination of ANN with GA effectively predicts the data and provides optimal results with adequate percentage errors in MRR and Ra positively.
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