Application of convolutional neural network and finite element analysis to simulation of bar rolling reduction
In order to realize the intelligent rolling simulation process, the convolutional neural networks( CNN) and finite element analysis are combined to select the best rolling simulation results and determine the optimal pass parameters by machine instead of manual work. First of all, CNN is used for the classification of rolling pass, it is found that the test model"Convolutional neural network+image preprocessing"has the highest accuracy, which ranges from 91.84％ to 96％. Secondly, five evaluation methods are used to compare the rolling simulation results with the ideal simulation results, and the RMS error and perceptual hash are more sensitive to the simulation results. Finally, the pass parameters are adjusted in the direction of the optimization of the rolling simulation results, and the simulation results are compared with the pre-adjustment results, and the second adjustment is more ideal. In this study, CNN classification of bar rolling passes has been realized, better rolling simulation results have been identified by the machine, and more suitable pass parameters have been determined. This method has important theoretical significance and practical application value in reducing manpower and material consumption and realizing intelligent simulation.
convolutional neural networkfinite element analysisrollpass parameterintelligentize
万方数据
维普
