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A novel strategy based on machine learning of selective cooling control of work roll for improvement of cold rolled strip flatness

Author

Listed:
  • Pengfei Wang

    (Yanshan University)

  • Jinkun Deng

    (Hebei North China Petroleum Rongsheng Machinery Manufacturing Co., Ltd)

  • Xu Li

    (Northeastern University)

  • Changchun Hua

    (Yanshan University)

  • Lihong Su

    (University of Wollongong)

  • Guanyu Deng

    (University of Queensland)

Abstract

Precise selective cooling control of work roll can significantly improve the cold rolled strip flatness in steel manufacturing industry. To improve the control accuracy of the coolant output of selective work roll cooling control system, a machine learning (ML) algorithm with differential evolution-gray wolf algorithm optimization support vector machine regression (DE-GWO-SVR) model has been proposed for the first time in this study. This model combines the differential evolution (DE) with grey wolf optimization algorithm (GWO) to improve the optimization performance of the algorithm. Then, the SVR model parameters are optimized with differential evolutionary gray wolf hybrid algorithm (DE-GWO) to improve the regression accuracy. Finally, the influences of data normalization methods and the selection of SVR kernel functions were systematically investigated. Compared with the test results of other regression models, the evaluation index R2 based on the DE-GWO-SVR model is greater and the RMSE, MAE, and MAPE are smaller. The DE-GWO-SVR model performs the best, with a higher regression accuracy than the other regression models. Besides, it has been successfully applied to a 1450 mm five-stand industrial cold rolling mill. The model has higher control accuracy for the thermal crown of the work roll and better control effect for the flatness deviation of the strip steel. This study provides a novel strategy with a help of ML algorithm to effectively improve the flatness quality of cold rolled strips by optimizing the selective cooling control of work roll, which exhibits a great practical application potential in steel manufacturing.

Suggested Citation

  • Pengfei Wang & Jinkun Deng & Xu Li & Changchun Hua & Lihong Su & Guanyu Deng, 2024. "A novel strategy based on machine learning of selective cooling control of work roll for improvement of cold rolled strip flatness," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3559-3576, October.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:7:d:10.1007_s10845-023-02204-2
    DOI: 10.1007/s10845-023-02204-2
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    References listed on IDEAS

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    1. Chunyang Xia & Zengxi Pan & Joseph Polden & Huijun Li & Yanling Xu & Shanben Chen, 2022. "Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1467-1482, June.
    2. Yi, Yong & Wang, Liming & Chen, Zhengying, 2021. "Adaptive global kernel interval SVR-based machine learning for accelerated dielectric constant prediction of polymer-based dielectric energy storage," Renewable Energy, Elsevier, vol. 176(C), pages 81-88.
    3. Hong Zhang & Lixing Chen & Yong Qu & Guo Zhao & Zhenwei Guo, 2014. "Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-11, June.
    4. Sherwan Mohammed Najm & Imre Paniti, 2023. "Investigation and machine learning-based prediction of parametric effects of single point incremental forming on pillow effect and wall profile of AlMn1Mg1 aluminum alloy sheets," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 331-367, January.
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