The coupling machine learning for microstructural evolution and rolling force during hot strip rolling of steels

Abstract

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.