Performance analysis of WEDM during the machining of Inconel 690 miniature gear using RSM and ANN modeling approaches

Abstract The present work aims to carry out a feasible study of wire electro-discharge machining (WEDM) during the Inconel 690 superalloy gears. Processing conditions power-on time, power-off current, and spark-gap voltage are varied evaluate process performance in terms material removal rate (MRR), surface roughness (SR), consumption. Parametric optimization has been carried using combined approach response methodology (RSM) artificial neural network (ANN). Results revealed that ANN predicted values 99% agreement with experimental results which validates its effectiveness as compared RSM values. A viability noise characteristics processed gear is also done testing setup. Additionally, FE-SEM used analyze machined surface’s topography. Greater discharge energy brought by longer pulse length raises MRR, SR, recast layer thickness (RLT). This explores capability WEDM produce more precise smooth profile other conventional methods. RLT microhardness have critically studied comprehend better understanding mechanism.