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.
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Acknowledgements
This project is funded by National Natural Science Foundation of China (No. 52074242 and No. U20A20187), Central Guiding Local Science and Technology Development Special Fund Project (Grant No. 216Z1602G), Natural Science Foundation of Hebei Province (No. E2020203068), the open fund of the State Key Laboratory of Rolling and Automation (No. 2022RALKFKT001), and Liao Ning Revitalization Talents Program of Liao Ning Province (No. XLYC2007087). L.S. is very grateful for the support from the Australian Research Council (ARC) through Discovery Early Career Researcher Award (DECRA) fellowship (No. DE180100124). G.D. would like to acknowledge the support from the University of Queensland (UQ) for awarding him the UQ Research Stimulus Allocation Fellowship.
Funding
This study was funded by Australian Research Council, DE180100124, Lihong Su, National Natural Science Foundation of China, Grant No. 52074242, Pengfei Wang, Grant No. U20A20187, Pengfei Wang, Central Guiding Local Science and Technology Development Special Fund Project, Grant No. 216Z1602G, Pengfei Wang, Natural Science Foundation of Hebei Province, No. E2020203068, Pengfei Wang, E2020203068, Pengfei Wang, the open fund of the State Key Laboratory of Rolling and Automation, No. 2022RALKFKT001, Pengfei Wang, Liao Ning Revitalization Talents Program of Liao Ning Province, No. XLYC2007087, Xu Li.
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PW: Conceptualization, Investigation, Formal analysis, Funding acquisition, Project administration, Writing—review & editing. JD: Investigation, Methodology, Formal analysis, Validation. XL: Investigation, Funding acquisition, Formal analysis, Project administration, Writing—review & editing. CH: Methodology, Formal analysis, Validation, Resources. LS: Investigation, Methodology, Formal analysis, Validation, Writing—review & editing. GD: Conceptualization, Investigation, Methodology, Formal analysis, Supervision, Writing—review & editing.
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Wang, P., Deng, J., Li, X. et al. A novel strategy based on machine learning of selective cooling control of work roll for improvement of cold rolled strip flatness. J Intell Manuf (2023). https://doi.org/10.1007/s10845-023-02204-2
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DOI: https://doi.org/10.1007/s10845-023-02204-2