An artificial neural network approach for parametric study on welding defect classification

In this paper, a welding defect prediction model has been developed and investigated through training an artificial neural network (ANN) based model. The input data were three welding process measurements (welding current, travel speed, and protective gas flow). The output data were non-destructive test results of respective weldments on four defect types (underfill, lack of penetration,incomplete fusion, and porosity) to ensure the consistency of the welding following the designed parameters; all data were obtained from 289 specimens produced by an automated GMAW welding manufacturing system. The 2-stages model comprises 13 inputs, hidden layers with 80–100 neurons and 4 outputs. The outputs were used to evaluate the classification accuracy in the confusion matrix for the prediction of weld quality. A further 73 specimens were used to test the accuracy of the trained ANN model. The model achieved 85% accuracy.