A new integrated model of deformation resistance and its application in prediction of rolling force of a thick plate

The prediction accuracy of existing models of the rolling force of a thick plate is always very low.To address this problem,a high-precision genetic algorithm-backpropagation network(GA-BP)model of deformation resistance was built,and its integration with the traditional fitted model was further established.Then,a novel rolling force model was obtained by embedding the integration model of deformation resistance in the original model of rolling force.According to this research idea,the industrial data are normalized at first.On this basis,the interactions among the process parameters were disclosed through the variance analysis,and then described by various virtual factors.These factors are set as part of input parameters.Then,the optimal structure of the GA-BP model of deformation resistance was determined and an integration model of deformation resistance was built.Finally,a novel rolling force model is obtained by substituting the traditional fitted deformation resistance into the Sims model with the integration model of the deformation resistance.The results proves that the introduction of virtual factors can increase the hit rate of±5%from 75.8%to 78%and can reduce the root mean square error from 4.72%to 4.48%.Besides,it is found that the mean relative error of the traditional fitted deformation resistance is 0.142,while that of the modified deformation resistance is only 0.03.In addition,the mean relative error in the original rolling force model is 0.145,while that of the present model is only 0.03.

Research progress and intelligent trend of accurate modeling of rolling force in metal sheet

The rolling force model is the basis for reasonable selection of rolling equipment and optimization of rolling process,and the establishment of an accurate mathematical model as well as doing the corresponding parameter analysis has been the focus of research in this field for many years.Different modeling methods of the rolling force and their research progress were introduced,the main methods of which are the theoretical analysis,the finite element simulation,the artificial neural network modeling,the hybrid modeling of theory and neural network,as well as the hybrid modeling of finite element and neural network.Meanwhile,the application examples of rolling force models in thickness control,strip crown control,and schedule optimization were presented,and an outlook on the new directions of future development was made,including establishing new type of hybrid models,solving the black box problem,and realizing the multi-objective optimization accounting for some special requirements.

Finite element model simulation and back propagation neural network modeling of void closure for an extra-thick plate during gradient temperature rolling

The void closure behavior in a central extra-thick plate during the gradient temperature rolling was simulated and a back propagation(BP)neural network model was established.The thermal–mechanical finite element model of the gradient temperature rolling process was first developed and validated.The prediction error of the model for the rolling force is less than 2.51%,which has provided the feasibility of imbedding a defect in it.Based on the relevant data obtained from the simulation,the BP neural network was used to establish a prediction model for the compression degree of a void defect.After statistical analysis,80%of the data had a hit rate higher than 95%,and the hit rate of all data was higher than 90%,which indicates that the BP neural network can accurately predict the compression degree.Meanwhile,the comparisons between the results with the gradient temperature rolling and uniform temperature rolling,and between the results with the single-pass rolling and multi-pass rolling were discussed,which provides a theoretical reference for developing process parameters in actual production.