Mathematical model of lever arm coefficient in cold rolling process

• Published in: International journal of advanced manufacturing technology 2018-07, Vol.97 (5-8), p.1847-1859
• Main Authors: Sun, Jie, Liu, Yuan-Ming, Wang, Qing-Long, Hu, Yu-Kun, Zhang, Dian-Hua
• Format: Article
• Language: English
• Subjects: Back propagation networks; CAE) and Design; Coefficient of friction; Cold; Cold pressing; Cold rolling; Computer simulation; Computer-Aided Engineering (CAD; Deformation resistance; Engineering; Finite element method; Friction resistance; Industrial and Production Engineering; Low temperature resistance; Mathematical models; Mechanical Engineering; Media Management; Neural networks; Original Article; Predictions; Reduction; Stress concentration; Three dimensional models; Torque; Upper bounds
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-018-2078-7

The prediction precision of mechanics parameters such as rolling force and torque affects the yield, quality, cost, and benefit of products during the cold strip rolling. The lever arm coefficient is the core linking rolling force and torque, but there is no mathematical model of this in cold rolling. The distribution of rolling pressure and the change rule of lever arm coefficient under different reduction, forward and backward tension stress, deformation resistance, and friction coefficient in cold rolling are illustrated based on 3D (three-dimensional) elastic-plastic FEM (finite element model) simulation. The mathematical model of lever arm coefficient is built according to online measured data processed by BP (back propagation) neural network in the tandem cold rolling plant. The predicted rolling forces calculated on the basis of this model and upper bound method are consistent with online measured values. The proposed model provides valuable guidelines to determine the reduction and check the strength of the equipment such as rolls and stands.