Prediction of surface roughness of tempered steel AISI 1060 under effective cooling using super learner machine learning.

Surface roughness is essential to evaluate the quality of the surface of the product. To predict the surface roughness researchers have been using statistical and empirical methodologies both of which lack generalizability when applied to unseen data. To overcome the limitation of existing models’ scholars have developed machine learning and artificial intelligence. Machine learning can predict the surface roughness of machined parts accurately. It has high generalization ability when applied to unseen data. For instance, this research endeavor has formulated a super learner machine learning model aimed at predicting surface roughness by leveraging a diverse array of machine learning techniques, including decision trees, random forests, gradient boosting, and extreme gradient boosting. The optimization of these models was achieved through the application of grid search hyperparameter tuning and K-fold cross-validation methodologies. The predictive efficacy of the proposed super learner model is compared with that of all alternative models. Achieving a coefficient of determination (R²) of 99.8% between the experimental and predicted values for surface roughness in the test dataset, the suggested super learner model exhibited superior predictive capabilities relative to its counterparts. This model is identified as the most accurate, distinguished by the highest coefficient of determination (R²), the lowest mean absolute error (1.92%), the lowest mean absolute percentage error (1.76%), and the lowest root mean square error (2.29%). In addition, the interpretations of the model's predictions are clarified using the Shapley additive explanations (SHAP) technique, thereby shedding light on the significant variables that affect the surface roughness of tempered steel AISI 1060.