Fusion of Dynamic Film Evaluation Knowledge AI Model for Weld Defect Detection

doi:10.3901/JME.2025.06.024

Abstract

Abstract: The current methods for detecting weld defects essentially belong to the paradigm of ‘industry dataset + AI model’ based on a single image, lacking integration of knowledge in the field of non-destructive testing. For a large number of defects with low contrast and insignificant targets, it is easy to cause missed and false detections. Aiming at this problem, a new paradigm of ‘industry dataset + AI model + domain knowledge’ is proposed for weld defect target detection technology that can integrate dynamic evaluation knowledge in the field of non-destructive testing. Firstly, based on the knowledge of the ‘dynamic’ process of manually detecting defects, a multi graph decomposition method based on logarithmic transformation is proposed to transform the ‘static’ analysis of a single image into the ‘dynamic’ analysis of multiple images; Secondly, by designing a channel attention mechanism module, shallow fusion of multi-image features was achieved; Finally, based on YOLOX network and combined with bidirectional feature pyramid network (BiFPN), deep fusion of multi graph features and defect target detection were achieved. Using data from a certain enterprise’s pipeline circumferential weld seam for verification, the results show that the proposed method achieves a defect detection mAP of 96.72%, which is 6.68% higher than YOLOX. Especially, it significantly improves the detection ability for incomplete fusion and incomplete penetration, enhancing the application ability of AI technology in non-destructive testing.

Key words: weld defects, X-ray images, defect detection, AI model, non-destructive testing

CLC Number:

TP391

JIANG Hongquan, ZHANG Maojie, CHENG Huyue, GAO Jianmin, YAO Huan, GAO Fuchao, GUO Xiaofeng, HE Jicheng. Fusion of Dynamic Film Evaluation Knowledge AI Model for Weld Defect Detection[J]. Journal of Mechanical Engineering, 2025, 61(6): 24-32.

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