Regression modeling of welded joint quality in gas metal arc welding process using acoustic and electrical signals

Abstract

In any welding process, the quality of joints is significantly affected by the bead geometry as well as the presence of welding defects. One promising approach is to utilize the features of the welding signals such as voltage, current and sound to monitor and predict the quality of the welded joints. In this work, for gas metal arc welding, a regression modeling has been proposed to predict two main weld quality measures, namely, welding defects and bead shape factor. The proposed approach is based on the features of welding electrical and sound signals. The weld quality characteristics are defined in terms of weld defects (discontinuity, lack of fusion and overlap) as well as weld shape factor. These measures are modeled using statistical parameters of weld sound and electrical signals. To develop the models, 13 statistical parameters of the signals have been employed. To gather the required data, a total of 57 experiments have been performed based on the optimal determinant method (D-optimal) of design of experiments approach. The curvilinear function has proved to have the best fit for the process under consideration. The proposed models are validated through further experiments. Comparisons between the experimental measurements and the predicted values indicate that the developed models are quite accurate in predicting the actual welding process in terms of the two main weld quality indices. The proposed approach may be used to adjust the input parameters in such a way that the desired bead geometry is obtained while the welding defects are minimized.