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

The digitalization of mechanical engineering requires reliable prediction tools that are interpretable and facilitate faster analysis. The study applied benchmarks for data-driven strategies and conducted a comprehensive analysis, using an 84-run grinding dataset on aluminum alloy 6061 that includes three different grinding wheel types, four different coolants, and seven specific removal rates with the surface roughness as the output variable. Particle Swarm Optimization was the first applied and tuned using a closed-form formula, but with unreliable accuracy metrics. Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGB) were the machine learning algorithms that were tested 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 R ² 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 R ² of 0.98. The sensitivity analysis was employed to assess the significance of input parameters. Shapley Additive Explanations (SHAP) was 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. These novel stacking-ensemble and analysis methods can be broadened for future applicability.