用多模态注意力模型实时检测焊缝缺陷，准确率高达99%
Explainable Temporal Attention-based Defect Detection For Fillet Joints in Real-Time Gas Metal Arc Welding Based on Multi-modal Data
- 融合图像与声音数据，通过时序注意力捕捉关键特征
- F1分数达0.99，有效识别气孔、未熔合等5类缺陷
- 可解释AI揭示关键判据，提升工业质检可信度
深度学习能高效监控实时焊接过程，减少焊后返修和生产延误。本文提出一种基于多模态时序注意力的深度学习缺陷检测模型，用于检测角焊缝气保焊中难以察觉的内部缺陷，包括气孔、未熔合、未焊透、咬边和冷接。模型基于工业协作焊接机器人采集的焊接图像与音频数据训练。结果表明，注意力模块使F1分数提升至0.99。采用可解释人工智能分析模型行为与数据分布，定位图像与声谱图中的关键区域，并确定各类缺陷的最优模态。该方法显著提升了人工智能驱动焊缝检测的信任度与可靠性。
原文摘要 · Abstract (English)
Deep learning is an efficient technique to monitor the real time welding process, reducing post-welding repairs and production delays. This paper leverages the monitoring capability by proposing a multi modal temporal attention based deep learning defect detection model for internal defects that are challenging to detect, including porosity, lack of penetration and fusion, undercut, and cold lap during Gas Metal Arc Welding in fillet joints. The model is trained on collected welding images and sound data from an industrial collaborative welding robot. The results show that the attention module can improve the F1 Score to 0.99. We use explainable Artificial Intelligence to interpret the proposed models behavior and dataset distribution, determining potential important areas in image and sound spectrograms and preferred modality to detect each defect. This improves trust and reliability in Artificial Intelligence driven welding inspection.
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