Toward data-driven machining: Prediction of surface roughness in high-entropy alloy coatings using a stacking ensemble machine learning model

Abstract

In mechanical engineering, predictive tools are increasingly used to enable faster analysis. The study began with cold spraying of high-entropy alloy coatings, Al_0.1-0.5CoCrCuFeNi and MnCoCrCuFeNi coatings at nitrogen gas temperatures of 650°C, 750°C, and 850°C. The surface roughness, Ra, as the output, was measured using profilometry and microscopy techniques. The experimental sample set consisted of 20 samples of different experiments. The stacking ensemble structure included three base learners: Linear Regression, Extreme Gradient Boosting, and Gaussian Process Regression with RidgeCV as the meta-learner. This ensemble was compared with Extreme Gradient Boosting, which is a powerful single machine learning approach. Results indicated that the stacking ensemble performed better than Extreme Gradient Boosting in all the regression metrics. Extreme Gradient Boosting reached RMSE of 0.22 µm, MAPE of 3.32%, and R² of 0.83, whereas the stacking ensemble achieved RMSE of 0.17 µm, MAPE of 2.73%, and R² of 0.89. A sensitivity analysis was used to qualitatively assess the influence of input variables. The results suggest that stacking ensembles can improve predictive performance even in scenarios of small data.