Research on Thickness Defect Control of Strip Head Based on GA-BP Rolling Force Preset Model

• Published in: Metals (Basel ) Vol. 12; no. 6; p. 924
• Main Authors: Chen, Luzhen, Sun, Wenquan, He, Anrui, Yuan, Tieheng, Shi, Jianrui, Qiang, Yi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022
• Subjects: Accuracy; Back propagation networks; BP neural network; Coefficient of friction; Cold; Cold rolling; Deformation; Deformation resistance; flying gauge change; Friction; Friction resistance; genetic algorithm; Genetic algorithms; Lubricants & lubrication; Mechanical properties; Neural networks; Optimization algorithms; Optimization models; Prediction models; preset rolling force; Process controls; Shear strength; Steel production; Strip; Thickness; thickness defect; Velocity
• ISSN: 2075-4701; 2075-4701
• DOI: 10.3390/met12060924

Due to the inaccuracy of the preset rolling force of cold rolling, there is a severe thickness defect in the strip head after cold rolling due to the flying gauge change (FGC), which affects the yield of the strip. This paper establishes a rolling force preset model (RFPM) by combining the rolling force optimization model (RFOM) and the rolling force deviation prediction model (RFDPM). The RFOM used a genetic algorithm (GA) to optimize the deformation resistance and friction coefficient models. The RFDPM is constructed using a backpropagation (BP) neural network. The calculation result of the RFPM shows that the average fraction defect of the preset rolling force is only 1.24%, which proves that the RFPM has good calculation accuracy. Experiments show that the defect length proportion of the strip head thickness at less than 20 m after FGC increases from 38.8% to 55.8%, while the average defect length decreases from 47.3 m to 29.6 m, effectively improving the yield of cold rolling.