A Review of Post-weld Inspection Technology

Welding robots are now extensively used in shipbuilding, automobile, and aviation industries. Defects are prone to occur after welding, and traditional non-destructive testing like ultrasonic and radiographic testing, depend on manual judgement, resulting in detection lag and difficulty in achieving online closed-loop control on production lines. This paper focuses on robot-mounted vision-based post-weld inspection, dividing the techniques into non-destructive and destructive testing. It outlines a three-level processing paradigm for vision-based inspection: low-level preprocessing, mid-level defect recognition and segmentation, and high-level quality diagnosis. The paper systematically summarizes three deep-learning technical routes: defect recognition and classification, 3D geometric measurement of weld seams, and multimodal temporal intelligent diagnosis. It also compares the application characteristics and applicable scenarios of mainstream models such as Convolutional Neural Networks (CNNs), You Only Look Once (YOLO), U-Net, and Detection Transformer (DETR). Various interference factors in industrial environments are analyzed. Finally, future technical directions are discussed in four key aspects, providing a comprehensive theoretical reference for the development of intelligent welding inspection algorithms and the engineering integration of robotic systems.