A NEURAL NETWORK FOR WELD PENETRATION CONTROL IN GAS TUNGSTEN ARC WELDING

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.