Thickness accuracy is a critical quality indicator in medium and heavy plate production, as it directly affects material utilization, product performance, and manufacturing cost. The rolling process of medium and heavy plates is highly nonlinear. It also involves multivariable coupling and dynamic fluctuations in operating conditions. Therefore, achieving highly accurate and reliable thickness prediction in industrial applications remains a major challenge. To address this issue, this paper develops a joint point-interval prediction framework for medium and heavy plate thickness in industrial applications. First, recursive feature elimination with a LinearSVR estimator (LinearSVR-RFE) is employed to eliminate low-contribution features from the original process feature set, retain informative variables, and construct a compact and effective feature subset. Second, Bayesian optimization is employed to tune the hyperparameters of multiple machine learning regression models. A Stacking ensemble strategy is then adopted to improve the accuracy and robustness of point prediction under complex production conditions. Finally, quantile regression is introduced based on the optimal point prediction model to construct prediction intervals at multiple confidence levels. This provides uncertainty-aware results for production decision-making. Experimental results based on real industrial data from a 3500 mm medium and heavy plate production line show that the proposed framework achieves strong point prediction performance on the test set. The optimal Stacking model achieves a coefficient of determination (R2) of 0.9845 with a root mean square error (RMSE) of 0.73 mm on the test set. In addition, the framework produces prediction intervals with a good balance between coverage and compactness at confidence levels from 80% to 95%. For example, at the 90% confidence level, the interval prediction module achieves a PICP of 0.9043 and a PINAW of 0.0711. The results indicate that the proposed framework provides an effective solution for intelligent thickness prediction and quality evaluation in industrial rolling processes. It also shows good potential for engineering applications.
Cao et al. (Mon,) studied this question.