Title: Prediction model based on XGBoost for mechanical properties of steel materials

Authors: JinXiang Chen; Feng Zhao; YanGuang Sun; Lin Zhang; YiLan Yin

Addresses: State Key Laboratory of Hybrid Process Industry Automation Systems and Equipment Technology, Automation Research and Design Institute of Metallurgical Industry, China Iron & Steel Research Institute Group, Beijing, 100081, China ' State Key Laboratory of Hybrid Process Industry Automation Systems and Equipment Technology, Automation Research and Design Institute of Metallurgical Industry, China Iron & Steel Research Institute Group, Beijing, 100081, China ' State Key Laboratory of Hybrid Process Industry Automation Systems and Equipment Technology, Automation Research and Design Institute of Metallurgical Industry, China Iron & Steel Research Institute Group, Beijing, 100081, China ' State Key Laboratory of Hybrid Process Industry Automation Systems and Equipment Technology, Automation Research and Design Institute of Metallurgical Industry, China Iron & Steel Research Institute Group, Beijing, 100081, China ' State Key Laboratory of Hybrid Process Industry Automation Systems and Equipment Technology, Automation Research and Design Institute of Metallurgical Industry, China Iron & Steel Research Institute Group, Beijing, 100081, China

Abstract: In order to predict the mechanical properties of steel materials, an intelligent prediction approach based on XGBoost model and big data is presented in this paper. The effectiveness of the model was verified by using it to predict the mechanical properties of hot-rolled strips. Firstly, a dataset with 17,710 samples was established by using the practical hot-rolled process data, where every sample has 17 characteristics (C, Si, Mn, P, S, Alt, V, Ti, Nb, Ni, Cr, Cu, Mo, B, N, final rolling temperature and curling temperature) and three output variables (tensile strength, compressive strength and elongation). 90% of 17,710 samples were used as training samples and others were used as testing samples. The simulation results showed that the accuracy of the model for tensile strength, compressive strength and elongation of the hot-rolled strip was 0.99895, 0.99576, 0.96260, respectively, which were superior to the results by using BP neural network model.

Keywords: intelligent prediction; XGBoost; machine learning; steel materials; mechanical properties; big data analysis; hot-rolled strips; tensile strength; compressive strength; elongation.

DOI: 10.1504/IJMIC.2019.107482

International Journal of Modelling, Identification and Control, 2019 Vol.33 No.4, pp.322 - 330

Received: 25 Oct 2019
Accepted: 08 Nov 2019
Published online: 29 May 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article