Strength Prediction of Aluminum–Stainless Steel-Pulsed TIG Welding–Brazing Joints with RSM and ANN
来源期刊：Acta Metallurgica Sinica2014年第6期
论文作者：Huan He Chunli Yang Zhe Chen Sanbao Lin Chenglei Fan
文章页码：1012 - 1017
摘 要：Pulsed TIG welding–brazing process was applied to join aluminum with stainless steel dissimilar metals. Major parameters that affect the joint property significantly were identified as pulsed peak current, base current, pulse on time,and frequency by pre-experiments. A sample was established according to central composite design. Based on the sample,response surface methodology（RSM） and artificial neural networks（ANN） were employed to predict the tensile strength of the joints separately. With RSM, a significant and rational mathematical model was established to predict the joint strength.With ANN, a modified back-propagation algorithm consisting of one input layer with four neurons, one hidden layer with eight neurons, and one output layer with one neuron was trained for predicting the strength. Compared with RSM, average relative prediction error of ANN was \10% and it obtained more stable and precise results.
Huan He，Chunli Yang，Zhe Chen，Sanbao Lin，Chenglei Fan
State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology
摘 要：Pulsed TIG welding–brazing process was applied to join aluminum with stainless steel dissimilar metals. Major parameters that affect the joint property significantly were identified as pulsed peak current, base current, pulse on time,and frequency by pre-experiments. A sample was established according to central composite design. Based on the sample,response surface methodology（RSM） and artificial neural networks（ANN） were employed to predict the tensile strength of the joints separately. With RSM, a significant and rational mathematical model was established to predict the joint strength.With ANN, a modified back-propagation algorithm consisting of one input layer with four neurons, one hidden layer with eight neurons, and one output layer with one neuron was trained for predicting the strength. Compared with RSM, average relative prediction error of ANN was \10% and it obtained more stable and precise results.
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