Intelligent Prediction of Process Parameters for Cold Bending
The choice of the process parameters in the conventional tube bending forming is often based on experience. The method of constantly testing to adjust has seriously affected the production efficiency and increased production costs. In this paper, an intelligent prediction model of the tube bending forming process parameters for utility boiler was set up based on neural network, which has been used to predict the main process parameters including the bending moment and boost power. In the intelligent prediction model, the analytical calculations, numerical simulation and experimental data are selected as the source of training samples. 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.
Zhengyi Jiang, Xianghua Liu and Jinglong Bu
S. L. Ren et al., "Intelligent Prediction of Process Parameters for Cold Bending", Advanced Materials Research, Vols. 154-155, pp. 74-78, 2011