Interpretable machine learning-based rolling force prediction using multivariate industrial data during tandem cold rolling

Abstract

In the pursuit of intelligent manufacturing goals, industrial big data technology has emerged as a key enabler in advancing the steel industry. Traditional rolling force (RF) models typically rely on data from individual cold rolling production lines, leading to lower accuracy and limited interpretability. To overcome this, an industrial data platform has been developed, offering a complete and reliable dataset to enhance the performance of RF prediction models. A data-driven machine learning framework is proposed, employing an improved sparrow search algorithm to optimise the weighting parameters of the broad learning system. The Shapley additive explanations method is further applied to elucidate the contributions of multivariate features from hot and cold rolling, thereby enhancing the interpretability of RF predictions. The performance of the proposed framework was validated on the production line of a leading steel plant, demonstrating significant advantages over existing state-of-the-art models. Furthermore, this study demonstrates and extensively elaborates on the significant impact of hot rolling parameters in enhancing the predictive accuracy of cold RF models. Industrial application validation demonstrates that the proposed framework accurately predicts the RF at the head of cold-rolled strip, enabling feedforward compensation for bending force and effectively improving flatness defects, further confirming the method's efficacy.