发表机构
University of British Columbia; Novarc Technologies Inc.（不列颠哥伦比亚大学; 诺瓦克技术公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于多模态时间注意力的深度学习模型，用于实时气体金属电弧焊角焊缝内部缺陷检测，结合可解释AI提升F1至0.99，增强焊接检测的信任与可靠性。
AI 中文摘要
深度学习是监测实时焊接过程的有效技术，可减少焊后修复和生产延误。本文利用监测能力，提出一种基于多模态时间注意力的深度学习缺陷检测模型，用于检测气体金属电弧焊角焊缝中难以检测的内部缺陷，包括气孔、未焊透和未熔合、咬边及冷搭接。该模型在工业协作焊接机器人采集的焊接图像和声音数据上进行训练。结果表明，注意力模块可将F1分数提升至0.99。我们使用可解释人工智能来解释所提模型的行为和数据集分布，确定图像和声音频谱图中潜在的重要区域以及检测每种缺陷的优选模态。这提高了人工智能驱动焊接检测的信任度和可靠性。
英文摘要
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.
