Roll system abnormal state diagnosis method for high-strength thin strip flatness control process of precision cold rolling mill

TS Yang and SS Zheng and TH Yuan and WQ Sun and AR He, JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL, 32, 2403-2420 (2025).

DOI: 10.1007/s42243-024-01423-y

High-order asymmetric flatness defects resulting from the abnormal state of roll system are the main issue of precision rolling mill in the manufacturing process of high-strength thin strip. Due to the difficulty of monitoring and adjusting the abnormal state, the spatial state of roll system cannot be controlled by traditional methods. It is difficult to fundamentally improve these high-order asymmetric flatness defects. Therefore, a digital twin model of flatness control process for S6-high rolling mill was established, which could be used to analyze the influence of the abnormal state on the flatness control characteristic and propose improvement strategies. The internal relationship between the force state of side support roll system and the abnormal state of roll system was proposed. The XGBoost algorithm model was established to analyze the contribution degree of the side support roll system force to the flatness characteristic quantity. The abnormal state of roll system in the S6-high rolling mill can be diagnosed by analyzing the flatness characteristic difference between flatness value of the rolled strip and calculated characteristic value of finite element simulation. The flatness optimization model of the gray wolf optimization-long short- term memory non-dominated sorting whale optimization algorithm (GWO- LSTM-NSWOA) was established, and the decision-making selection was made from the Pareto frontier based on the flatness requirements of cold rolling to regulate the abnormal state of the roll system. The results indicate that the contribution degree of the force of the side support roll system to the flatness characteristics is more than 25%, which is the main influence of high-order asymmetric flatness defect. The performance of the GWO-LSTM flatness feature prediction model has clear advantages over back propagation and LSTM. The practical applications show that optimizing the force of side support roll system can reduce the high point of high-strength strip flatness from 13.2 to 6 IU and decrease the percentage of low-strength strip flatness defects from 1.6% to 1.2%. This optimization greatly reduced the proportion of flatness defects, improved the accuracy level of flatness control of precision rolling mill, and provided a guarantee for the stable production of thin strip.

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