Enhancing Grinding Efficiency in Aluminum Alloys: An Ensemble-Stacking and Single Machine Learning Framework for Predicting Surface Roughness with SHAP-based interpretability

As mechanical engineering becomes more data-driven, accurate and explainable prediction models are increasingly required. This study applied benchmarks for data-driven strategies and conducted a comprehensive analysis, using an 84-run grinding dataset on aluminum alloy 6061, with the surface roughness as the output variable. The conventional fitting method was the first applied and tuned using a closed-form formula, motivating the use of machine learning algorithms. Gaussian process regression, Artificial neural network, and Extreme gradient boosting were the algorithms that were used in predicting the relationship between input variables and the output. The best single model performance was given by GPR with an accuracy of 97.50%, a MAPE of 2.49%, and an R2 of 0.99. There were also three models of stacking ensembles that were applied. The stacking ensemble using ANN and XGB as base learners and GPR as the meta-learner offered the best trade-off between its bias and variance and achieved an overall accuracy of 94.54, a MAPE of 5.45, and an R2 of 0.98. The sensitivity analysis was employed to assess the significance of input parameters. Shapley additive explanations were also used to give attribution to each case, to attribute the impact of individual input features to each prediction. Among the variables, grinding wheel type 89A180K6V111 and the specific removal rate were the most influential. The framework can be adaptable to other datasets and grinding machining scenarios.