Accurately capturing the complex relationships among chemical composition, process parameters, and mechanical properties is essential for quality control in hot strip rolling, yet theoretical models are constrained by idealized assumptions and simplified boundary conditions, making them unsuitable for applications under highly variable industrial conditions. To address these limitations, this study develops an ensemble learning framework guided by the technique for order preference by similarity to ideal solution. Built on an industrial data platform, the framework unifies process and quality data, adaptively combines multiple base learners through performance‐based weighting, and uses optimization‐driven hyperparameter tuning to achieve high‐precision prediction of mechanical properties. Shapley additive explanations analysis is further employed to interpret feature interactions and quantify their contributions to mechanical behavior, providing mechanistic insight that supports alloy design and process optimization. Validation on a domestic hot strip rolling production line demonstrates clear advantages over conventional ensemble methods, particularly for tensile strength prediction, where the model achieves an R 2 of 0.97, an mean absolute error of 11.82 MPa and an root mean square error of 17.15 MPa, confirming its predictive accuracy and industrial applicability.
Li et al. (Thu,) studied this question.