Surface Roughness and Microhardness Prediction: A Machine Learning Approach for Electrochemical Grinding

This  study focuses on the development of predictive machine learning models to estimate key surface integrity parameters in the electrochemical grinding (ECG) of AISI ۳۰۴ stainless steel. Experimental data were collected from a series of ۲۰ controlled tests based on a response surface methodology (RSM) design, varying three primary process parameters: voltage, electrolyte concentration, and grinding wheel speed. Using this dataset, Gaussian Process Regression (GPR) models were constructed for four output variables: current density, surface roughness in X- and Y-directions (Ra_x and Ra_y), and surface microhardness (Vickers). Model performance was evaluated using R² scores, residual analysis, and error distributions across both training and test datasets. The results demonstrate that surface roughness parameters, particularly Ra_y (R²_test = ۰.۹۷۰) and Ra_x (R²_test = ۰.۹۳۲), were predicted with the highest accuracy and consistency. Current density also exhibited strong performance (R²_test = ۰.۹۵۴), though with minor deviations at extreme values. Surface microhardness, in contrast, posed greater modeling challenges, achieving the lowest test R² (۰.۸۴۳) and showing systematic underprediction. Residual and error analyses confirmed these trends, with minimal bias and variance for Ra_x and Ra_y, and broader, asymmetric error profiles for hardness. The least roughness was observed under an electrolyte concentration of ۱۴۰ g/L, an applied voltage of ۲۰ V, and a grinding wheel rotational speed of ۲۰۰۰ rpm. Overall, the GPR models proved effective for capturing ECG process behavior and offer potential for process optimization in precision manufacturing

Keywords:

Electrochemical grinding , Surface integrity , Machine learning , Gaussian Process Regression