Focusing on the problem of strip shape quality control in the finishing process of hot rolling, a shape model based on metal flow and stress release with the application of varying contact rolling parameters is introduced. Combined with digital twin technology, the digital twin framework of the shape model is proposed, which realizes the deep integration between physical time–space and virtual time–space. With the utilization of the historical data, the parameters are optimized iteratively to complete the digital twin of the shape model. According to the schedule, the raw material information is taken as the input to obtain the simulation of the strip shape, which shows a variety of export shape conditions. The prediction absolute error of the crown and flatness are less than 5 μm and 5 I-unit, respectively. The results prove that the proposed shape simulation model with strong prediction performance can be effectively applied to hot rolling production. In addition, the proposed model provides operators with a reference for the parameter settings for actual production and promotes the intelligent application of a shape control strategy.

Automobile and household appliance panels require steel strips with extremely high-quality surfaces. Therefore, an in-depth study of the surface topography transfer of the steel strip during the rolling process is of considerable significance for improving product quality. In this study, the scale-invariant feature transform (SIFT) algorithm is used to realize the large-field stitching and the correspondence measurement of the surface topography of the roll and strip. The surface topography transfer mechanism and microconvex change law during cold rolling are revealed. Further analysis is conducted regarding the effects of different reduction rates and the initial surface topography of the roll on the formation of strip surface topography. Experimental results reveal that the furrow phenomenon occurs during the rolling process owing to the backward slip effect but is eliminated by the elastoplastic deformation of the matrix and the forward slip action. No furrow occurred along the width direction of the strip. With an increase in the rolling reduction rate, the transfer rate increases, and the strip surface topography is closer to the roll surface topography. Under the same rolling roughness condition and a small reduction rate (5%), the transfer degree increases remarkably with a rise in the reduction rate and increases slowly as the reduction rate continues to grow (from 7 to 10%). This study serves as a theoretical basis for the subsequent improvement of the surface quality of cold rolled strips.

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.