Optimization and machinability evaluation for WEDM of austempered ductile iron

Abstract

Abstract Wire electrical discharge machining (Wire EDM) is a non-contact CNC machining that removes material from a workpiece with electrical sparks. Optimization of parameters involved in wire EDM is essential for better operational economics and energy usage. The major goal and objective of this research are to assess the machining parameters, like surface roughness Ra, material removal rate MRR, and hardness HV by experimental investigation utilizing the wire cut EDM machine and austempered ductile iron (ADI) as the work material. An artificial neural network (ANN) has been employed to create a prediction model using experimental data. The Aquila optimization approach is then used to obtain the ideal operating parameters. With Aquila optimization, the predicted optimum values for MRR, Ra, and Hardness are 3.529 mm3/min, 1.966 µm, and 367 HV, respectively, when the input parameters are pulse ton 16 µs, pulse-toff time toff 14 µs, servo voltage 50 V, and current 3 A. Finally, SEM and 3D roughness analysis have been carried out to study surface morphology and material removal mechanism.