Prediction model of the Charpy impact toughness of deposited metals of welding materials
Based on artificial neural network (ANN), prediction model of the Charpy impact toughness for automatic welding is built. The input parameters of the model consist of the chemical elements and the diameter of the welding material and the outputs is the average Charpy impact toughness. The ANNs model is established by Visual C++ based on improved backpropagation (BP) arithmetic with momentum coefficients, in which the sample data used are from automatic welding materials for X70 pipeline steel. Based on the prediction model, the influence of chemical compositions, such as C, S, P, Si, Mn, Cu, Ti and Ni on the Charpy impact toughness of welding materials are analyzed. The results show that the influence of metallic elements is significantly greater than the nonmetallic, and the contents of Mn in metallic and the C in nonmetallic have primary effect on the average Charpy impact toughness.
welding average Charpy impact toughness selection of components
Tong Lige Bai Lu Ding Hongsheng Wang Li Bai Shiwu Sui Yongli Yu Jiangfeng
School of Mechanical Engineering, University of Science & Technology Beijing, Beijing, China Department of Physics, University of Science & Technology Beijing, Beijing, China Pipeline Research Institute of China National Petroleum Corporation, Langfang, China Chinese peoples liberation army 96819 power specialist group, Beijing, China
国际会议
合肥
英文
1001-1004
2011-09-23（万方平台首次上网日期，不代表论文的发表时间）