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Data-driven width spread prediction model improvement and parameters optimization in hot strip rolling process

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Abstract

The width spread is one of the key indices affecting hot rolling processes and product quality. The traditional Shibahara spread prediction model (SSM) does not take into account the wear and interference of production equipment. As a result, the prediction accuracy cannot satisfy the high precision and high reliability requirements. To improve the prediction accuracy, first, this paper analyzes the strip deformation mechanism and the source of prediction errors and proposes an improved Shibahara spread model (ISSM) based on wear and interference. Second, a Bayesian-optimized differential evolution and adaptive gradient descent (BDE-AGD) algorithm is proposed for ISSM parameter optimization. This algorithm adopts the Bayesian algorithm to optimize the differential evolution (BDE) algorithm in stage 1, which improves the global search ability. Furthermore, the adaptive strategy is used for gradient descent (AGD) to improve the local search capability in stage 2. Finally, the experimental results show that the BDE-AGD algorithm reduces the error by 21.9% and 3.1% compared with DE-AGD and BDE-GD, respectively. This shows that BDE-AGD has a better global optimization capability. In addition, the prediction precision of ISSM is improved by 9.77% compared with the traditional SSM. Moreover, the precision of ISSM is improved by 8.56% on average compared with seven machine learning algorithms. This shows that ISSM optimized by BDE-AGD can achieve high-precision prediction of strip spread.

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Data Availibility Statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments and insightful suggestions.

Funding

This work is supported by the National Natural Science Foundation of China (61633019, 61533013).

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Authors and Affiliations

  1. Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yanjiu Zhong, Jingcheng Wang, Jiahui Xu & Jun Rao

  2. School of Electronic Information Engineering, Xi’an Technological University, Xi’an, 710021, China

    Kangbo Dang

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  1. Yanjiu Zhong
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Zhong, Y., Wang, J., Xu, J. et al. Data-driven width spread prediction model improvement and parameters optimization in hot strip rolling process. Appl Intell 53, 25752–25770 (2023). https://doi.org/10.1007/s10489-023-04818-8

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