Deep Learning Methods Utilization in Mechanical Property of Medium‐Mn Steel
Haijun Pan, Wenyu Tao, Shunhu Zhang, Ketao Yan, Ze Sun, Zhiqiang Wu, Lin Liu
Abstract
This study presents an innovative method to predict the mechanical properties of medium‐Mn steel by deep learning (DL). Based on datasets, an artificial neural network (ANN) model serves as a crucial component of DL, demonstrating a coefficient of determination of 0.996, which indicates high accuracy between experimental and predicted values. Meanwhile, the contents of Mn and C, as well as Al and intercritical annealing (IA) conditions, have higher permutation feature importance (PFI) scores, which are 22.37% and 49.22%, respectively. An ANN model predicts that the experimental steel has good mechanical properties with IA at 710 °C for 60 min, with predicted values of ultimate tensile strength (UTS) and total elongation (TE) being 925 MPa and 45.3%, respectively. The experimental values for UTS (937 MPa) and TE (44.8%) closely correspond to the predicted results. The absolute errors between the experimental and predicted UTS and TE are 1.2% and 1.1%, respectively.