Finite Element Analysis Of Non-steady State Rolling Process, The Plate Thickness Modeling And Intelligent Control

Biting stages is unsteady process in the plate rolling and the unsteady process parameters result in that the head and tail is thicker than the middle.Generally speaking,the difference is more than 100μm and the maximum value may be up to 200-300μm.Therefore,only main part of the rolled piece is examined for quality.In order to increase the yield and reduce the unsteady process,it is essential to study the deformation characteristic of the unsteady rolling,analyze the effect of process parameters on the unsteady rolling process,set up mathematical model of the unsteady process,and propose a new control system to improve control precision,The main research work in this thesis includes:(1)Based on the theory of rigid plastic finite element,the 3-D finite element model of unsteady process was built with nonlinear finite element analysis software MSC.MARC.The elastic deformation of the roll and the influence of emulsion were considered in simulation.(2)The unsteady process of the plate rolling was simulated by using three-dimension large deformation thermo-mechanical coupled rigid-plastic finite element methods.The simulated result was compared with the real rolling data,which showed that the method used to simulate the unsteady rolling process was feasible and reliable.The rolling force distribution and the rolled piece deformation in the bite stage were studied and the rolled piece temperature influence on strip-head bending was analyzed.(3)Based on the proposed method,the effect of process parameters such as rolling temperature and tension on the unsteady rolling process was analyzed.Moreover,the way to shorten the unqualified length of the plate head was presented.(4)Based on the simulation results of FEM,the main process parameters were selected as the inputs and outputs of a neural network, and a BP neural network prediction model of.unsteady state rolling was built.The maximum calculated error of the model was 1.200%and the average relative error was 0.507%,and showing that the accuracy of the prediction model was high. (5)A neural network PID controller was built to control the gauge of plate head.Simulation results showed that this control system has the characteristics of fast convergence,strong stability and small overshoot. The unsteady state rolling was reduced from 6s to 3s by using the controller that has good performance for engineering application.