Prediction model of strip rough rolling width based on the fusion of mechanism and data

Shengyue Zong; Xiaolong Li; Shengyue Zong; Xiaolong Li

doi:10.3934/math.20251189

AIMS Mathematics

2025, Volume 10, Issue 11: 27058-27072. doi: 10.3934/math.20251189

Previous Article Next Article

Research article Special Issues

Prediction model of strip rough rolling width based on the fusion of mechanism and data

Shengyue Zong ^{1
,
,},
Xiaolong Li ²

1.
National Engineering Research Center for Advanced Rolling Technology and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing 100083, China
2.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Received: 01 July 2025 Revised: 28 July 2025 Accepted: 15 August 2025 Published: 21 November 2025
MSC : 35Q93, 62J05, 68T05

In the rough rolling production process of strip steel, the width change is affected by the coupling of multiple factors. Accurate prediction of the width expansion is of great significance for improving the yield rate and product quality. This paper proposes a strip rough rolling width prediction model that integrates the mechanism model and the data-driven method. First, the width expansion mechanism model is established based on the Shibahara equation. After linearization by Taylor expansion, the least squares method and linear support vector regression algorithm are used to estimate the main mechanism parameters. Second, the extreme gradient boosting learning mechanism is introduced to correct the deviation after model parameter identification, thereby improving the robustness and accuracy of the prediction. Finally, the parameter convergence of the proposed method is verified in the simulation. The result shows that the proposed method is superior to the traditional modeling method in terms of prediction accuracy and convergence speed. This study provides a high-precision modeling idea for rolling width control that integrates physical constraints and data learning.
- width prediction,
- parameter identification,
- least squares method,
- support vector regression,
- extreme gradient boosting
Citation: Shengyue Zong, Xiaolong Li. Prediction model of strip rough rolling width based on the fusion of mechanism and data[J]. AIMS Mathematics, 2025, 10(11): 27058-27072. doi: 10.3934/math.20251189

Related Papers:

Abstract

In the rough rolling production process of strip steel, the width change is affected by the coupling of multiple factors. Accurate prediction of the width expansion is of great significance for improving the yield rate and product quality. This paper proposes a strip rough rolling width prediction model that integrates the mechanism model and the data-driven method. First, the width expansion mechanism model is established based on the Shibahara equation. After linearization by Taylor expansion, the least squares method and linear support vector regression algorithm are used to estimate the main mechanism parameters. Second, the extreme gradient boosting learning mechanism is introduced to correct the deviation after model parameter identification, thereby improving the robustness and accuracy of the prediction. Finally, the parameter convergence of the proposed method is verified in the simulation. The result shows that the proposed method is superior to the traditional modeling method in terms of prediction accuracy and convergence speed. This study provides a high-precision modeling idea for rolling width control that integrates physical constraints and data learning.

References

[1]	W. Wu, W. Peng, Y. Liu, J. Liu, X. Li, D. Zhang, et al., Predictive modeling of strip width based on incremental learning and adaptive-weight fusion during the hot rolling process, J. Manuf. Process., 142 (2025), 157–176. https://doi.org/10.1016/j.jmapro.2025.03.091 doi: 10.1016/j.jmapro.2025.03.091
[2]	P. Ren, Q. Liu, J. Song, B. Yan, M. Liang, S. Dai, et al., Heterogeneous microstructure and texture evolution of K-doped tungsten thick plate during hot-rolling processing, Nucl. Mater. Energy, 43 (2025), 101931. https://doi.org/10.1016/j.nme.2025.101931 doi: 10.1016/j.nme.2025.101931
[3]	R. Jia, T. Wang, W. Xue, J. Guo, Y. Zhao, Multi-time scale consensus algorithm of multi-agent systems with binary-valued data under tampering attacks, IEEE Trans. Ind. Inf., 12 (2025), 9377–9388. https://doi.org/10.1109/TII.2025.3598451 doi: 10.1109/TII.2025.3598451
[4]	J. Guo, X. Wang, W. Xue, Y. Zhao, System identification with binary-valued observations under data tampering attacks, IEEE Trans. Autom. Control, 66 (2021), 3825–3832. https://doi.org/10.1109/TAC.2020.3029325 doi: 10.1109/TAC.2020.3029325
[5]	X. Li, J. Wang, J. Wang, J. Wang, J. Chen, X. Yu, Research on CNC machine tool spindle fault diagnosis method based on DRSN–GCE model, Algorithms, 18 (2025), 304. https://doi.org/10.3390/a18060304 doi: 10.3390/a18060304
[6]	P. Yu, Y. Hu, Y. Wang, R. Jia, J. Guo, Optimal consensus control strategy for multi-agent systems under cyber attacks via a stackelberg game approach, IEEE Trans. Autom. Sci. Eng., 22 (2025), 18875–18888. https://doi.org/10.1109/TASE.2025.3591858 doi: 10.1109/TASE.2025.3591858
[7]	M. Nilsson, M. Olsson, Microstructural, mechanical and tribological characterisation of roll materials for the finishing stands of the hot strip mill for steel rolling, Wear, 307 (2013), 209–217. https://doi.org/10.1016/j.wear.2013.09.002 doi: 10.1016/j.wear.2013.09.002
[8]	K. Wang, R. Doerner, M. Baldwin, C. Parish, Flux and fluence dependent helium plasma-materials interaction in hot-rolled and recrystallized tungsten, J. Nucl. Mater., 510 (2018), 80–92. https://doi.org/10.1016/j.jnucmat.2018.07.04 doi: 10.1016/j.jnucmat.2018.07.04
[9]	T. Shibahara, K. Misaka, T. Kono, M. Koriki, Y. Takemoto, Edger set-up model at roughing train in hot strip mill, Tetsu-to-Hagane, 67 (1981), 2509–2515. https://doi.org/10.2355/tetsutohagane1955.67.15_2509 doi: 10.2355/tetsutohagane1955.67.15_2509
[10]	J. Guo, Q. Zhang, Y. Zhao, Identification of FIR systems with binary-valued observations under replay attacks, Automatica, 172 (2025), 112001. https://doi.org/10.1016/j.automatica.2024.112001 doi: 10.1016/j.automatica.2024.112001
[11]	F. Jing, F. Li, Y. Song, J. Li, Z. Feng, J. Guo, Root cause tracing using equipment process accuracy evaluation for looper in hot rolling, Algorithms, 17 (2024), 102. https://doi.org/10.3390/a17030102 doi: 10.3390/a17030102
[12]	S. Zhang, Z. Liu, T. An, X. Cui, X. Zeng, N. Shi, et al., Hot rolled prognostic approach based on hybrid Bayesian progressive layered extraction multi-task learning, Expert Syst. Appl., 249 (2024), 123763. https://doi.org/10.1016/j.eswa.2024.123763 doi: 10.1016/j.eswa.2024.123763
[13]	L. Meng, J. Ding, X. Li, G. Cao, Y. Li, D. Zhang, Novel shape control system of hot-rolled strip based on machine learning fused mechanism model, Expert Syst. Appl., 255 (2024), 124789. https://doi.org/10.1016/j.eswa.2024.124789 doi: 10.1016/j.eswa.2024.124789
[14]	T. Hussain, J. Hong, J. Seok, A hybrid deep learning and machine learning-based approach to classify defects in hot rolled steel strips for smart manufacturing, Comput., Mater. Continua, 80 (2024), 2099–2119. https://doi.org/10.32604/cmc.2024.050884 doi: 10.32604/cmc.2024.050884
[15]	J. Lu, P. Wang, H. Huang, L. Hao, X. Li, Q. Peng, et al., Novel online prediction model for thermal convexity of work rolls during hot steel rolling based on machine learning algorithms, Expert Syst. Appl., 254 (2024), 124384. https://doi.org/10.1016/j.eswa.2024.124384 doi: 10.1016/j.eswa.2024.124384
[16]	C. Ding, J. Sun, X. Li, W. Peng, D. Zhang, A high-precision and transparent step-wise diagnostic framework for hot-rolled strip crown, J. Manuf. Syst., 71 (2023), 144–157. https://doi.org/10.1016/j.jmsy.2023.09.007 doi: 10.1016/j.jmsy.2023.09.007
[17]	Z. Wang, Y. Liu, T. Wang, J. Wang, Y. Liu, Q. Huang, Intelligent prediction model of mechanical properties of ultrathin niobium strips based on XGBoost ensemble learning algorithm, Comput. Mater. Sci., 231 (2024), 112579. https://doi.org/10.1016/j.commatsci.2023.112579 doi: 10.1016/j.commatsci.2023.112579
[18]	M. Li, G. Li, X. Li, L. Lu, P. Mitrouchev, Prediction of Mechanical Properties of Hot-Rolled Strip Steel Based on XGBoost and Metallurgical Mechanism, Phys. Metals Metallogr., 125 (2024), 1728–1738. https://doi.org/10.1134/S0031918X23600653 doi: 10.1134/S0031918X23600653
[19]	I. Trofimov, P. Korotaev, I. Ivanov, A. Malginov, A. Tokhtamyshev, A. Yanilkin, et al., Machine learning assisted design of reactor steels with high long-term strength and toughness, Mater. Design, 254 (2025), 114014. https://doi.org/10.1016/j.matdes.2025.114014 doi: 10.1016/j.matdes.2025.114014
[20]	B. Duan, Y. Zhu, D. Cao, X. Cai, X. Mao, F. Liu, Machine-learning-based performance prediction for partially encased steel-concrete composite columns after fire exposure, Structures, 77 (2025), 109082. https://doi.org/10.1016/j.istruc.2025.109082 doi: 10.1016/j.istruc.2025.109082
[21]	H. Emami, M. Ghazani, An interpretable stacking machine-learning model to predict the hot torsion flow characteristics of a micro-alloyed steel, Results Eng., 26 (2025), 105326. https://doi.org/10.1016/j.rineng.2025.105326 doi: 10.1016/j.rineng.2025.105326
[22]	F. Liang, Z. Gao, F. Chai, H. Su, X. Luo, Investigation on predicting flow stress of a Cu-containing steel based on machine learning algorithms, Mater. Today Commun., 46 (2025), 112728. https://doi.org/10.1016/j.mtcomm.2025.112728 doi: 10.1016/j.mtcomm.2025.112728
[23]	C. Wang, X. Wei, S. Zwaag, Y. Tan, From creep-life prediction to ultra-creep-resistant steel design: An uncertainty-informed machine learning approach, Acta Mater., 292 (2025), 121073. https://doi.org/10.1016/j.actamat.2025.121073 doi: 10.1016/j.actamat.2025.121073
[24]	L. Guo, Time-varying stochastic systems–stability, estimation and control, Changchun: Jilin Science and Technology Press, 1992.

Reader Comments

Your name:*

Email:*
© 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)