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 

机构地区：[1]National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing,University of Science and Technology Beijing,Beijing 100083,China [2]China Academy of Machinery Science and Technology,Beijing 100044,China

出　　处：《International Journal of Minerals,Metallurgy and Materials》2023年第6期1093-1103,共11页矿物冶金与材料学报（英文版）

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).

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 production.It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled strip.Insufficient 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 process.In 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)framework.According 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 designed.The 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 features.Furthermore,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 normally.Finally,actual production data of three steel grades was used to conduct the experimental evaluation.The 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.

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

分 类 号：TG335.56[金属学及工艺—金属压力加工] TP18[自动化与计算机技术—控制理论与控制工程]