Transactions of Materials and Heat Treatment

Title：Prediction of pipe shape in cross-rolled piercing by PSO-BP neural network based on GRA

Authors： Wang Qinghua Jia Shiying Hu Jianhua Shuang Yuanhua Zhao Tielin

Unit： Taiyuan University of Science and Technology Technical Center of Taiyuan Heavy Industry Co. Ltd.

KeyWords： cross-rolled piercing pipe shape prediction grey relational analysis particle swarm optimization algorithm BP neural network

ClassificationCode：TP183

year,vol(issue):pagenumber：2022,47(8):88-94

Abstract：

For the defects of complicated calculation and low precision for pipe shape of seamless steel pipe in cross-rolled piercing, a pipe shape prediction model of PSO-BP neural network based on grey relational analysis was proposed. Since there were many factors affecting the pipe shape during the rolling process, the correlation analysis of the process parameters was carried out by the gray correlation analysis, and the influencing factors with higher correlation were selected as the input. Then, the BP neural network was optimized by particle swarm optimization algorithm, the optimal neural network structure was determined, and the pipe shape prediction model of cross-rolled piercing for seamless steel pipe was constructed. Finally, the model was trained and tested by field data and was compared with BP neural network and traditional mathe-matical models. The research results show that the prediction model has higher accuracy and better reliability, which lays a foundation for improving the production quality of seamless steel pipes.

Funds：

山西省科技重大专项(20191102009)

作者简介：王清华（1980-），女，博士，讲师，E-mail：2002043@tyust.edu.cn；通信作者：加世滢（1996-），女，硕士研究生，E-mail：13103421357@163.com

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