###### Abstract  
The advancement in technology has attracted researchers to electric discharge machining (EDM) for providing a practical solution for overcoming the limitations of conventional machining. The current study focused on predicting the Material Removal Rate (MRR) using machine learning (ML) approaches. The process parameters considered are namely, workpiece electrical conductivity, gap current, gap voltage, pulse on time and pulse off time. Cryo-treated workpiece viz, Nickel-Titanium (NiTi) alloys, Nickel Copper (NiCu) alloys, and Beryllium copper (BCu) alloys and cryo-treated pure copper as tool electrode was considered. In the present research work, four supervised machine learning regression and three supervised machine learning classification-based algorithms are used for predicting the MRR. Machine learning result showed that gap current, gap voltage and pulse on time are most significant parameters that effected MRR. It is observed from the results that the Gradient boosting regression-based algorithm resulted in the highest coefficient of determination value for predicting MRR while Random Forest classification based resulted in the highest F1-Score for obtaining MRR.  
electrical discharge machine; nickel-titanium alloys; nickel copper alloys; beryllium copper alloys; machine learning +
Footnote †: journal: Applied system  
machining of BeCu. They found peak current affected the most followed by pulse on time, pulse off time and machine voltage had less effect on MRR. [PERSON] et al. (2017) used RSM experimental plan to investigate the effect of peak current, workpiece thickness, pulse on time, and pulse frequency on MRR and SR during EDM of NiCu. Author's found all process parameters effect the MRR and SR. [PERSON] et al. (2018) studied the effect of servo voltage, pulse on time, pulse on time and discharge current on MRR and SR during machining of NiCu. They have used RSM experimental plan and desirability concept for optimization. [PERSON] and [PERSON] (2019) employed minimum quantity dielectric based EDM setup to machine NiCu and investigated the effect of wire feed, water flow rate, pulse on time, pulse off time, air inlet pressure on MRR and SR. [PERSON] et al. (2020) studied the productivity and surface integrity namely rate of tool wear, surface roughness, material removal rate and relative electrode wear during EDM of NiTi by considering discharge current, discharge voltage, pulse on time and pulse off time. MRR is effected by discharge current and pulse on time. Tool wear rate increases with increase in pulse on time upto threshold and then decreases. Surface roughness increases with increase in discharge current. MRR and SR decreases with increase in pulse off time. [PERSON] et al. (2018) investigated the effect of EDM process parameters on SR while machining of NiTi. They found that pulse off time has more positive effect in decreasing SR. [PERSON] et al. (2020) during EDM of NiTi-60 SMA studied the effect of tool rotation, Al\({}_{2}\)O\({}_{3}\), voltage, current, pulse on time and pulse off time on MRR and SR. MRR decreases with voltage & pulse off time and MRR increases with current and pulse on time. TWR and SR are effect by all process parameters. [PERSON] et al. (2020) discussed the review of ion implantation of superplastic NiTi shape memory alloys to enhance the surface properties. There are very few articles or research activities which focused on the implementation of Machine Learning algorithms in EDM process for predicting the Material Removal Rate. The present study combined experimental data obtained from the assessment of material removal rate during EDM of NiTi alloy, NiCu alloy & BeCu alloy and exposed them to supervised machine learning classification and regression-based methods.