Machine Learning Based Surface Roughness Prediction for Parameters of ECM

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.

Electrochemical Machining, Surface Roughness, Machine Learning, Process Parameters, Predictive Modeling

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