Machine Learning Based Predictive Modeling of Electrical Discharge Machining of Cryo-Treated NiTi, NiCu and BeCu Alloys

The advancement in technology has attracted researchers to electric discharge machining (EDM) for providing a practical solution overcoming the limitations of conventional machining. current study focused on predicting Material Removal Rate (MRR) using machine learning (ML) approaches. process parameters considered are namely, workpiece electrical conductivity, gap current, voltage, pulse time and off time. Cryo-treated viz, Nickel-Titanium (NiTi) alloys, Nickel Copper (NiCu) Beryllium copper (BCu) alloys cryo-treated pure as tool electrode was considered. In present research work, four supervised regression three classification-based algorithms used MRR. Machine result showed that voltage most significant effected It is observed from results Gradient boosting regression-based algorithm resulted highest coefficient determination value MRR while Random Forest classification based F1-Score obtaining