Real-Time CU-Net-Based Welding Quality Inspection Algorithm in Battery Production

In the production process of laser welding products, visual inspection is usually employed to recognize welding spot locations and diagnose their quality faults. However, commonly used algorithms fail to succeed in both reliability and computational efficiency, especially when applied to assembly line. In this article, a method based on deep learning algorithm and traditional computer vision (TCV) algorithm is proposed, which achieves quality inspection of laser welding spots in the process of battery production. First, compressed U-shape network (CU-net) is proposed to extract welding pads and welding spots. Then, a template-based method is proposed to confirm the validity of each welding spot. Finally, TCV heuristic algorithms are proposed to achieve three error detections, i.e., welding pad placed obliquely, electrode tab placed over highly, and welding spot welded through. Moreover, we build a Welding Spot Quality Inspection Dataset taken from real assembly line. Compared with other pipelines, including U-net, MaskRCNN, and PSPNet, CU-net shows a significant superiority in both processing speed and detection accuracy. The results of template-based method and TCV heuristic algorithms have shown high computational efficiency and ensured inspection accuracy. The inference time of the whole method is less than 100 ms with the implementation on NVIDIA 1060 and Intel i7-6700.