Prediction of material removal rate of SS304 alloy in electrochemical micromachining using machine learning

Abstract

Electrochemical micromachining (ECMM) is a non-traditional machining process used in machining microfeatures that are being used in the fabrication of MEMS, finishing surgical tools, implants, and cooling channels. Machine learning (ML) algorithms can be used to predict the output of a machining process based on the given input parameters. In this Work, ML is interpreted into ECMM where, different ML models were built based on Decision tree regression, Random Forest regression, and Support vector regression algorithms to find the relationship between the interactive effects of the type of electrolyte, voltage, feed rate, and duty cycle on Material Removal Rate (MRR). The work also aims to find the best-fit algorithm and approach toward the prediction of MRR of AISI SS304 alloy when machined in ECMM using Regression Analysis. The results showed that higher efficiency was attained in the Decision tree regression model with a training accuracy of 99.87% and testing accuracy of 99.75% and decision tree was found to be the best fit algorithm for the ECMM process for the single response parameter.