BIBA researchers Abdullah Al Noman, Anton Zitnikov, Firoj Ahmmed Patwary, Aaron Heuermann, and Klaus-Dieter Thoben received the Commended Paper Award at the 11th IFAC International Conference on Manufacturing Modeling, Management, and Control (IFAC MIM 2025), held from June 30 to July 3 in Trondheim, Norway, for their paper "Explaining Manufacturing Anomalies: Transformer-Based Detection with xAI for Imbalanced Process Data."

The awarded work addresses a pressing challenge in modern manufacturing environments—detecting and understanding anomalies in complex, imbalanced process data. By combining Transformer-based deep learning models with Explainable AI (xAI) techniques, the authors developed a method that not only identifies potential process anomalies but also provides meaningful explanations of the contributing factors. This is especially valuable in industrial settings, where model interpretability is crucial for process engineers and operators to take corrective action with confidence. The paper exemplifies how emerging AI technologies can be applied responsibly and effectively to support digitalization and data-driven decision-making in manufacturing.

The 11th IFAC MIM welcomed over 700 participants from 51 countries and featured 574 paper presentations across 119 sessions, along with more than five keynote speeches and several workshops—making it one of the largest and most impactful editions to date. BIBA contributed a total of three papers to the conference, including "Enhancing Condition Monitoring: A Semi-Automatic Framework Using Meta-Learning for Algorithm Selection" and "Knowledge Modeling Method for Assembly Process Planning Based on Process History." These contributions reflect BIBA’s continued commitment to advancing intelligent systems, data-driven decision support, and cutting-edge research in digital manufacturing.

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