A NEURAL NETWORK FOR WELD PENETRATION CONTROL IN GAS TUNGSTEN ARC WELDING
来源期刊：Acta Metallurgica Sinica2006年第1期
论文作者：J.Q. Gao C.S. Wu Y.H. Zhao
Key words：neural network; weld penetration control; back-side weld width; gas tungsten arc welding;
Abstract: Realizing of weld penetration control in gas tungsten arc welding requires establishment of a model describing the relationship between the front-side geometrical parameters of weld pool and the back-side weld width with sufficient accuracy. A neural network model is developed to attain this aim. Welding experiments are conducted to obtain the training data set (including 973 groups of geometrical parameters of the weld pool and back-side weld width) and the verifying data set (108 groups). Two data sets are used for training and verifying the neural network, respectively.The testing results show that the model has sufficient accuracy and can meet the requirements of weld penetration control.
J.Q. Gao1，C.S. Wu1，Y.H. Zhao1
(1.Institute of Materials Joining, Shandong University, Jinan 250061, China)
Abstract:Realizing of weld penetration control in gas tungsten arc welding requires establishment of a model describing the relationship between the front-side geometrical parameters of weld pool and the back-side weld width with sufficient accuracy. A neural network model is developed to attain this aim. Welding experiments are conducted to obtain the training data set (including 973 groups of geometrical parameters of the weld pool and back-side weld width) and the verifying data set (108 groups). Two data sets are used for training and verifying the neural network, respectively.The testing results show that the model has sufficient accuracy and can meet the requirements of weld penetration control.
Key words:neural network; weld penetration control; back-side weld width; gas tungsten arc welding;
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