基于机器学习的管材弯曲回弹有效预测与补偿

The machine learning algorithm modeling was adopted based on the optimized back propagation(BP) neural network and the precise prediction and efficient control method of bend springbacks was proposed. In this method, the particle swarm optimization(PSO) algorithm was improved by introducing the nonlinear inertia weight and hybrid operator of genetic algorithm, and then the BP neural network was optimized by the improved PSO algorithm, and the machine learning springback prediction and compensation model was constructed based on the improved PSO-BP neural network. Based on the springback data of different specifications, batches, and forming parameters in the actual productions, the applications of the machine learning prediction model were verified. The average relative error of the prediction results obtained by the model is as 6.3%. Compared with the traditional models, the prediction accuracy is increased by 18.5% at most, and the calculation time may be reduced from 1.5 h to 300 s. The prediction and compensation accuracy of springback and the calculation efficiency are improved significantly.