Initial guess of rigid plastic finite element method in hot strip rolling

Although the rigid plastic finite element method (RPFEM) is extremely efficient and particularly suitable for analyzing the strip rolling, it is unavailable for online application due to the large computational time. During iterative solution of RPFEM, the convergence speed is greatly determined by the initial guess. In this paper, three different initial guesses are constructed through Engineering Method, G Functional and Neural Network, respectively. Especially, the back propagation neural network model for predicting the relative velocity field (nodal velocities/roll speed) is trained from huge amounts of RPFEM results. Whereafter, the initial guess is calculated by multiplying the predicted relative velocity field by the roll speed. The numerical examples of seven passes hot strip rolling are provided to show the solution efficiency and the accuracy of RPFEM code in the cases of different initial guess. Compared with other two methods, the Neural Network has the remarkable advantages to reduce the CPU time and the iterations of RPFEM code. From the numerical results, it is found that the CPU time, stability and the accuracy of RPFEM code in the initial guess by the Neural Network can meet the requirements of online control completely in hot strip rolling.