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arXiv AI By Mobina Mobaraki, Mahyar Asadi, Klaske Van Heusden, Guy A. Dumont

Explainable Temporal Attention-based Defect Detection For Fillet Joints in Real-Time Gas Metal Arc Welding Based on Multi-modal Data

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The paper presents a multi‑modal deep learning model that uses temporal attention to detect internal welding defects such as porosity, lack of penetration, fusion, undercut, and cold lap in fillet joints during real‑time Gas Metal Arc Welding. Trained on images and sound data from an industrial collaborative welding robot, the model achieves an F1 score of 0.99. Explainable AI techniques are applied to interpret the model’s behavior, highlighting key image and sound spectrogram regions and the most effective modality for each defect type, thereby enhancing trust and reliability in AI‑driven welding inspection.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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