A new data-driven roll force and roll torque model based on FEM and hybrid PSO-ELM for hot strip rolling

Abstract

In this paper, a new Extreme Learning Machine (ELM) regression model of roll force and roll torque based on data-driven is proposed. The three-dimensional elastic-plastic finite element model (FEM) is established to solve the roll force and roll torque under different parameters (including rolling reduction rate, roll radius, rolling speed, average width of strip, entry temperature of strip). The regression model of ELM optimized by Particle Swarm Optimization (PSO) is established through using the datasets obtained by FEM. The PSO-ELM model prediction values of roll force and roll torque are compared with the single ELM and PSO-SVM model, and the error results of the prediction values are analyzed. The error results fully verify the feasibility and accuracy of the PSO-ELM model proposed. It is found that the new data-drive model of roll force and roll torque is simple in structure and it can make up for the deficiency of traditional mathematical mechanism model in dealing with nonlinear problems. The research result reveals that PSO-ELM method is suitable for parameters prediction and model optimization in strip rolling process.