Intelligent Prediction of Process Parameters for Bending Forming

Abstract

Abstract:The choice of the process parameters in the conventional tube bending forming is often based on experience and adjusted by repeated bending tests. The method of constantly testing to adjust has seriously affected the production efficiency and increased production costs. In this paper, neural network is used to establish the intelligent prediction model of the pipe forming process parameters. The obtained datum from analytical calculations, numerical simulations and experiments then serve as the training samples and test samples of neural network training. By the trained neural network, the intelligent prediction for the main process parameters including the bending moment and the boost power can be executed. The test results show that the average relative error between the simulation output and target output of bending moment and boost power is less than 2%, and the predicted process parameters, i.e. bending moment and boost power, can be directly used for actual production.

Applications of Artificial Neural Network to Sheet Metal Work - A Review

“…Log-sigmoid function is used as an activation function. Ren et al [59] used back propagation NN to establish the intelligent prediction model of pipe forming process parameters, and the main process parameters including bending moment. Zhang [60] used FEM simulation and ANN to optimize the blank holder force (BHF) for drawing of automobile fuel tank.…”