Microstructural evolution during hot rolling, which is complex and unperceptive but has direct effects on rolling forces, determines the final microstructure and mechanical properties of steel products. In this paper, a comprehensive set of machine learning (ML) models was developed through rolling forces to reveal the evolutions of recrystallization and grain size of austenite, in which the grain size was estimated by considering the grain shape effect after each rolling pass. By using the backpropagation neural network (BPNN) and genetic algorithm (GA) combined with industrial data, the quantitative connections of parameters in the models for static recrystallization (SRX), meta-dynamic recrystallization (MDRX), and mean flow stress (MFS) were established with steel chemical compositions and deformation conditions. Through the machine learning (ML) approach, softening kinetics and rolling forces can be more accurately predicted than traditional models. Also, the recrystallization behavior and evolution of austenite grain size during hot rolling were analyzed by using the proposed ML models, which are consistent with experimental results.

热轧过程中的显微组织演变复杂且不易察觉，但对轧制力有直接影响，决定了钢材的最终显微组织和力学性能。在本文中，通过轧制力开发了一套全面的机器学习 (ML) 模型，以揭示奥氏体的再结晶和晶粒尺寸的演变，其中晶粒尺寸是通过考虑每次轧制后的晶粒形状效应来估计的。利用反向传播神经网络（BPNN）和遗传算法（GA）结合工业数据，对静态再结晶（SRX）、元动态再结晶（MDRX）和平均流动应力（MFS）模型中参数的定量联系建立了钢的化学成分和变形条件。通过机器学习 (ML) 方法，可以比传统模型更准确地预测软化动力学和轧制力。此外，使用提出的ML模型分析了热轧过程中奥氏体晶粒尺寸的再结晶行为和演变，与实验结果一致。