マルチモーダルデータに基づく実時間ガスメタルアーク溶接の隅肉継手における説明可能な時間的注意機構を用いた欠陥検出
Explainable Temporal Attention-based Defect Detection For Fillet Joints in Real-Time Gas Metal Arc Welding Based on Multi-modal Data
溶接中の画像と音声データを用いて、時間的注意機構を備えた深層学習モデルで内部欠陥を検出し、F1スコア0.99を達成。説明可能AIでモデルの判断根拠を解釈し、信頼性を向上させた。
著者: Mobina Mobaraki, Mahyar Asadi, Klaske Van Heusden, Guy A. Dumont
分類: cs.AI, cs.LG
原文アブストラクト
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.