Optimization of weld penetration prediction based on weld pool image and deep learning approach in gas tungsten arc welding

Abstract

Abstract Gas tungsten arc welding (GTAW) is a popular technology for joining metallic parts with high stability and quality. However, many GTAW-related processes are conducted manually, which is time-consuming and the weld quality strongly depends on the skill of the welder. Although various automatic GTAW methods and systems have been developed, controlling the weld quality remains a challenge because of the difficulties involved in predicting the weld quality, such as the penetration depth and backside bead geometry, during welding. Hence, this paper proposes an accurate and effective method for estimating the penetration depth through weld pool monitoring using a convolutional neural network (CNN) trained on weld pool images. The weld pool images contained several objects, each influencing the prediction accuracy. The CNN architecture and the structure of the fully connected layers (FCLs) also affected the prediction accuracy. To optimize the performance of the estimation model, the effects of each object in the weld pool image and the structure of the CNN architecture were analyzed and evaluated. The structure of the FCLs that outputted a quantitative penetration depth was optimized and evaluated through hyperparameter tuning. With the proposed method, the optimized model could quantitatively predict the penetration depth; the mean absolute error was 0.0516 mm, with an R2 value of 0.998. Accurately predicting the penetration depth can be employed in real-time weld quality control to ensure a sound weld back bead.