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 & PLUSMN; 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.