@article{Ma2026, 
author = {Jiameng Ma and Meng Zhou and Zhao Yang and Lei Song and Ting Wang and Jin Guo},
title = {A temperature curve generation method for predicting the mechanical properties of hot-rolled strip steel},
year = {2026},
journal = {Electronic Research Archive},
volume = {34},
number = {6},
pages = {4107-4130},
keywords = {physics-informed neural network, Bayesian optimization, cubic spline interpolation, hierarchical surrogate model},
url = {https://www.sciopen.com/article/10.3934/era.2026184},
doi = {10.3934/era.2026184},
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.}
}