Predictive Modelling of Surface Roughness in Grinding Operations Using Machine Learning Techniques - Charde, Maya M.; Najan, Trupti P.; Cepova, Lenka; Jadhav, Ajinkya D.; Rash-inkard, Namdeo S.; Samal, S.P. (Manufacturing Technology Journal, 2025)
This paper details a systematic machine learning workflow designed for the prediction of surface roughness in grinding operations using key machining parameters. Those parameters are: Depth of Cut, Feed Rate, Work Speed, and Wheel Speed. The model was trained and validated on a data set which comprised experimental measurements of those parameters and their corresponding values of surface roughness. Three machine learning models, Random Forest, Gradient Boosting, and LightGBM, were developed and tested based on accuracy of prediction of the surface roughness. The validation of all three models was performed using performance metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2). Among the models, LightGBM exhibited the highest value of performance with the lowest error ob-served MSE 0.0047, MAE 0.064, and RMSE 0.09 respectively while an R-squared value closest to zero. (-0.02). The moderate performance was shown by the Random Forest which presented an MSE of 0.0063, MAE of 0.085, and RMSE of 0.10 while the Gradient Boosting recorded the highest error rates which may indicate that it is the least effective model. It's an effective application of machine learning in predicting surface roughness and gives an insight into machining process optimization through predictive modelling.
Keywords: Machine Learning Workflow; Surface Roughness Prediction; Grinding Operations; Machining Parameters; Depth of Cut; Feed Rate