Modeling hot strip rolling process under framework of generalized additive model  

基于广义可加模型框架的热轧带钢轧制过程建模（英文）

作　　者：LI Wei-gang YANG Wei ZHAO Yun-tao YAN Bao-kang LIU Xiang-hua 李维刚;杨威;赵云涛;严保康;刘相华(Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,China;National-provincial Joint Engineering Center of High Temperature Materials and Lining Technology,Wuhan University of Science and Technology,Wuhan 430081,China;Research Institute of Science and Technology,Northeastern University,Shenyang 110819,China)

机构地区：[1]Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,China [2]National-provincial Joint Engineering Center of High Temperature Materials and Lining Technology,Wuhan University of Science and Technology,Wuhan 430081,China [3]Research Institute of Science and Technology,Northeastern University,Shenyang 110819,China

出　　处：《Journal of Central South University》2019年第9期2379-2392,共14页中南大学学报（英文版）

基　　金：Project(51774219)supported by the National Natural Science Foundation of China

摘　　要：This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.本研究在广义可加模型的框架下,将工业大数据和过程机理分析相融合,提出了一种新的建模方法,从而建立兼顾泛化能力和预测精度的实用模型。新的建模方法主要包括四个方面。首先,利用机理知识和数据挖掘方法对影响因素进行筛选。其次,提出了一元无交互作用的广义可加模型的建模步骤,包括清理数据、建立子模型和验证子模型。随后,研究了各影响因素间的交互作用,构建了二元有交互作用的广义可加模型。最后,分析各子模型之间的关系,并建立整体模型。基于本文提出的建模方法,建立了热轧带钢力学性能预测模型和变形抗力模型。实际工业数据验证表明新建立的模型具有很好的预测精度,抗拉强度、屈服强度和变形抗力的平均绝对误差分别为2.54%、3.34%和6.53%。实验结果表明,本文提出的建模方法为工业过程建模提供了一种新的思路。

关 键 词：industrial big data generalized additive model mechanical property prediction deformation resistance prediction 

分 类 号：TG3[金属学及工艺—金属压力加工]