焊管2012，Vol.35Issue(1)：5-7,12,4.
连续油管TIG焊接头最薄弱区工艺-性能神经网络预测模型
The Neural Network Prediction Model of Process-property in the Weakest Area of Coiled Tubing TIG Welded Joint
摘要
Abstract
The mechanical properties of the weakest areas in the joint of coiled tube welded by TIG was obtained according to the experiment BP neural network was used to simulate and predict the process performance of the region, The influence on network performance was studied under different training function, Line Energy-Impact energy prediction model of the weakest areas in the joint of coiled tube welded by TIG was obtained by comparing the Network performance which received under different training function. LM algorithm and SCG algorithm was selected to train the network finally. Both the algorithms present higher precision of line Energy-Impact energy prediction model. The average relative error of predicted and measured values of test data were 0.785% and 0.34% respectively. It was very well that the impact energy were predicted in the network.关键词
连续油管/TIG焊/BP神经网络/冲击韧性Key words
coiled tube/TIG welded joint/BP neural network/impact toughness分类
能源科技引用本文复制引用
李琳,李继红,余晗,赵鹏康,毕宗岳,张敏..连续油管TIG焊接头最薄弱区工艺-性能神经网络预测模型[J].焊管,2012,35(1):5-7,12,4.基金项目
陕西省教育厅自然科学基金资助项目(00k904) （00k904）
陕西省重点学科建设专项资金资助项目(00X901) （00X901）
