Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems

作     者：Feifei Li Anrui He Yong Song Zheng Wang Xiaoqing Xu Shiwei Zhang Yi Qiang Chao Liu Feifei Li;Anrui He;Yong Song;Zheng Wang;Xiaoqing Xu;Shiwei Zhang;Yi Qiang;Chao Liu

作者机构：National Engineering Research Center for Advanced Rolling and Intelligent ManufacturingUniversity of Science and Technology BeijingBeijing 100083China China Academy of Machinery Science and TechnologyBeijing 100044China 

出 版 物：《International Journal of Minerals,Metallurgy and Materials》 (矿物冶金与材料学报（英文版）)

年 卷 期：2023年第30卷第6期

页      面：1093-1103页

核心收录：

学科分类：12[管理学] 080503[工学-材料加工工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 080201[工学-机械制造及其自动化] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：financially supported by the National Natural Science Foundation of China(No.52004029) the Fundamental Research Funds for the Central Universities,China(No.FRF-TT-20-06) 

主　　题：hot-rolled strip prediction of mechanical properties deep learning multi-grained cascade forest time series feature extraction variable window subsampling 

摘      要：Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent *** has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled *** data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing *** order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)*** to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was *** basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global ***,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained ***,actual production data of three steel grades was used to conduct the experimental *** results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.