ANN Modeling and Optimization of Process Parameters for Material Removal Rate in Electrical Discharge Machining of OHNS Steel

Oil hardening non-shrinking (OHNS) is a general-purpose tool steel. In this study, response surface methodology (RSM) is used to examine the impact of four control parameters, discharge current, pulse duration, duty factor, and voltage, on the material removal rate (MRR) of OHNS steel during electrical discharge machining (EDM). A central composite design (CCD) was used to create a predictable model and analyze the MRR. Experimental tests were conducted on OHNS tool steel, and the obtained data were used to develop the artificial neural network (ANN). Discharge current (Ip), pulse duration (Ton), duty factor (Tau), voltage (V), and their interactions significantly impacted MRR. The model's adequacy was deemed satisfactory with a coefficient of determination (R2). The Jaya algorithm effectively optimizes the obtained ANN model.