Vision-Based Acquisition Model for Molten Pool and Weld-Bead Profile in Gas Metal Arc Welding

Gas metal arc welding (GMAW) is widely used for its productivity and ease of automation across various industries. However, certain tasks in shipbuilding and heavy industry still require manual welding, where quality depends heavily on operator skill. Defects in manual welding often necessitate costly rework, reducing productivity. Vision sensing has become essential in automated welding, capturing dynamic changes in the molten pool and arc length for real-time defect insights. Laser vision sensors are particularly valuable for their high-precision bead profile data; however, most current models require offline inspection, limiting real-time application. This study proposes a deep learning-based system for the real-time monitoring of both the molten pool and weld-bead profile during GMAW. The system integrates an optimized optical design to reduce arc light interference, enabling the continuous acquisition of both molten pool images and 3D bead profiles. Experimental results demonstrate that the molten pool classification models achieved accuracies of 99.76% with ResNet50 and 99.02% with MobileNetV4, fulfilling real-time requirements with inference times of 6.53 ms and 9.06 ms, respectively. By combining 2D and 3D data through a semantic segmentation algorithm, the system enables the accurate, real-time extraction of weld-bead geometry, offering comprehensive weld quality monitoring that satisfies the performance demands of real-time industrial applications.