A temperature curve generation method for predicting the mechanical properties of hot-rolled strip steel

Jiameng Ma; Meng Zhou; Zhao Yang; Lei Song; Ting Wang; Jin Guo; Jiameng Ma; Meng Zhou; Zhao Yang; Lei Song; Ting Wang; Jin Guo

doi:10.3934/era.2026184

Electronic Research Archive

2026, Volume 34, Issue 6: 4107-4130. doi: 10.3934/era.2026184

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Research article

A temperature curve generation method for predicting the mechanical properties of hot-rolled strip steel

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
The 48th Research Institute of China Electronics Technology Group Corporation, Changsha 430111, China
3.
SINOMACH-HE Chengdu Heavy Machinery Co., Ltd., Chengdu 610052, China
4.
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
5.
Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China

Received: 13 January 2026 Revised: 11 February 2026 Accepted: 27 February 2026 Published: 15 May 2026

In response to the problems of sparse temperature measurement point distribution and poor cross-interval continuity in the hot continuous rolling process, a full-process temperature field prediction and reconstruction method based on the combination of a physics-informed neural network (PINN) and a Bayesian-XGBoost surrogate model is proposed in this paper. First, a PINN model across five process areas (from the reheating furnace to the coiler) is established, taking time nodes as input and directly outputting discrete points of the temperature-time curve along the strip in each process area. Subsequently, a Bayesian-XGBoost surrogate model is used; at this time, hierarchical surrogate decision-making determines the number of prediction points automatically by the maximum error principle and thereby improves the prediction accuracy. Finally, a piecewise cubic spline interpolation algorithm is designed, based on curvature detection to achieve a precise high-precision temperature curve reconstruction of discrete prediction results. The experimental results show that this method has high accuracy in temperature curve reconstruction and good reliability in mechanical property prediction; it verifies the effect and practicability of the proposed framework in real hot continuous rolling processes.
- physics-informed neural network,
- Bayesian optimization,
- cubic spline interpolation,
- hierarchical surrogate model
Citation: Jiameng Ma, Meng Zhou, Zhao Yang, Lei Song, Ting Wang, Jin Guo. A temperature curve generation method for predicting the mechanical properties of hot-rolled strip steel[J]. Electronic Research Archive, 2026, 34(6): 4107-4130. doi: 10.3934/era.2026184

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Abstract

In response to the problems of sparse temperature measurement point distribution and poor cross-interval continuity in the hot continuous rolling process, a full-process temperature field prediction and reconstruction method based on the combination of a physics-informed neural network (PINN) and a Bayesian-XGBoost surrogate model is proposed in this paper. First, a PINN model across five process areas (from the reheating furnace to the coiler) is established, taking time nodes as input and directly outputting discrete points of the temperature-time curve along the strip in each process area. Subsequently, a Bayesian-XGBoost surrogate model is used; at this time, hierarchical surrogate decision-making determines the number of prediction points automatically by the maximum error principle and thereby improves the prediction accuracy. Finally, a piecewise cubic spline interpolation algorithm is designed, based on curvature detection to achieve a precise high-precision temperature curve reconstruction of discrete prediction results. The experimental results show that this method has high accuracy in temperature curve reconstruction and good reliability in mechanical property prediction; it verifies the effect and practicability of the proposed framework in real hot continuous rolling processes.

References

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