Predicting hot-strip finish rolling thickness using stochastic configuration networks

作     者：Li, Xu He, Yaodong Ding, Jingguo Luan, Feng Zhang, Dianhua 

作者机构：Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Liaoning Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Liaoning Peoples R China 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2022年第611卷

页      面：677-689页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Liaoning Revitalization Talents Program [XLYC2007087] National Natural Science Foundation of China [U20A20187, 51634002] Fundamental Research Funds for the Central Universities [N2007006, N180708009] National Key R&D Program of China [2017YFB0304100] 

主　　题：Stochastic configuration networks Industrial data modeling Thickness prediction Metal forming process 

摘      要：In the hot-rolling metal forming process, the consistency and accuracy of the thickness of the metal strip are the most important factors for the product quality control. The current method of utilizing a mechanism prediction model with pre-defined parameters does not perform well due to some limits on the model assumptions and environmental interfer-ence. Manually tuning these parameters of the mechanism model may even result in worse performance. To resolve this problem, an advanced randomized learner model, termed stochastic configuration network (SCN), is employed to build a data-driven prediction model which can be trained by using a dataset collected from a real-world hot-rolling pro-duction site. Based on the rolling theory and gray relational analysis (GRA), 36 features are selected as the inputs of the prediction model. Experimental results with comparisons show that our proposed method is feasible and outperforms other machine learning meth-ods, such as deep learning models and the random vector functional link (RVFL) model. (c) 2022 Published by Elsevier Inc.