Journal Article

Online Prediction of Deformation Resistance for Strip Tandem Cold Rolling Based on Data-Driven

Li, Jingdong, Qie, Haotang, Wang, Xiaochen, Shao, Jian, Yang, Quan, Zhao, Jianwei

2023

Overview

An online model is proposed for predicting deformation resistance in the strip tandem cold rolling by combining the back propagation neural network optimized by the mind evolutionary algorithm (MEA-BP) and the deformation resistance analytical model. The real-time collection of hot and cold rolling process data is achieved by constructing a “hot and cold rolling” cross-process data platform. Based on this, a dataset including historical production data of hot and cold rolling is established to train and test the model. The application result of the proposed model shows that the deformation resistance prediction error can be reduced from ±12% to ±5% compared with the traditional analytical model, which demonstrates the model established in this work can effectively improve the prediction accuracy of the deformation resistance in the strip tandem cold rolling.

• Publisher: MDPI AG
• Subject: Accuracy / Algorithms / Back propagation networks / BP neural network / Cold / Cold rolling / Continuous casting / Data processing / Deformation / Deformation analysis / Deformation effects / Deformation resistance / deformation resistance analytical model / deformation resistance prediction / Error reduction / Evolutionary algorithms / Genetic algorithms / Hot rolling / Low temperature resistance / Manganese steel / Mathematical models / mind evolutionary algorithm / Neural networks / Optimization / Strip