Machine Learning Based Surface Roughness Prediction for Parameters of ECM

Abstract

Electrochemical Machining (ECM) is a machining technique which is non traditional used for shaping complex components with superior accuracy and surface finish. However, optimizing surface roughness remains challenging because of the intricate, non-linear dependency between various process aspects such as electrolyte concentration, voltage, frequency, duty cycle, temperature, and feed rate. Traditional trial-and-error or analytical approaches are often time- consuming and inefficient. This study introduces a Machine Learning (ML)-based predictive modeling approach to estimate and optimize the roughness of the surface in ECM processes using data obtained by Chen Xuezhen et al.’s tests on the Ti60 titanium alloy.