A strategy combining machine learning and physical metallurgical principles to predict mechanical properties for hot rolled Ti micro-alloyed steels

Abstract

Determining the exact relationships between rolling parameters and mechanical properties is important to predict and even control the mechanical properties of hot rolled Ti micro-alloyed steel products. In this paper, two data dimension reduction strategies, respectively guided by physical metallurgical (PM) principles and data-driven (DD) strategy were proposed to preprocess the high dimensional steel composition and rolling parameters, and a comprehensive set of machine learning (ML) models was developed to predict yield strength (YS) and elongation (EL) combined with the reconstructed inputs. The artificial neural network (ANN) with the inputs including dislocation density, fractions, and sizes of ferrite and TiC precipitate transferred by PM principle outperformed that with the inputs extracted low variance feature elimination (LVFE) and autoencoder (AE) algorithms. Based on the proposed model, the effects of alloying elements, rolling temperatures, and microstructures on mechanical properties were comprehensively analyzed. Also, the variations of microstructures and mechanical properties over the strip length were discussed, which were consistent with the measured results. • PM-guided dimension reduction was proposed to transfer rolling parameters to microstructures. • The dimension reduction guided by PM performed better than that guided by DD methods with LVFE and AE. • The predicted effects of process and microstructures on YS and EL were consistent with experiments. • The predicted variations of YS and EL over strip length were consistent with measured results.