Mechanical property prediction of steel and influence factors selection based on random forests
YANG Wei1,LI Wei-gang1,2,ZHAO Yun-tao1,YAN Bao-kang1,WANG Wen-bo3
(1. College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081,Hubei, China 2. Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China 3. Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China)
Abstract:Many factors affecting the steel mechanical property and there are complex interactions among different factors. It is helpful to improve the model accuracy by reasonably selecting the influence factors of the property prediction model. Using the random forest algorithm and mechanical property modeling method combining data and mechanism analysis,the mechanical property prediction of hot rolled strip and selection of the influence factors are studied. Firstly,based on a large amount of production data of hot rolling process,the variable importance ranking was obtained by using random forests algorithm.Based on the importance ranking of influence factors,a series of mechanical property prediction models are established by increasing the number of independent variables. After that,according to the variation trend of the prediction error of each model,the influence of various factors on the precision of the model are determined,thereby,the more important factors are selected,and the prediction model with a small number of important factors as independent variables is established in the end. Finally,the tensile strength modeling experiment of Nb high strength steel products produced by a large hot strip mill in China was carried out,practice results shows that the mechanical property prediction model based on Mn,Cs,FDH,NbC,NbN,RT,Si,CT,FET has a high predicted accuracy,mean absolute percentage error is 2.52% and root mean square error is 21.65 MPa.
收稿日期: 2017-08-22
出版日期: 2018-03-16
引用本文:
杨 威,李维刚,赵云涛,严保康,王文波. 基于随机森林的钢材性能预报与影响因素筛选[J]. , 2018, 53(3): 44-49.
YANG Wei,LI Wei-gang,,ZHAO Yun-tao,YAN Bao-kang,WANG Wen-bo. Mechanical property prediction of steel and influence factors selection based on random forests. Iron and Steel, 2018, 53(3): 44-49.
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