High-Precision AI-Based Vision Inspection for Defect Detection in PEMFC MEA Manufacturing

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Abstract
This study presents an AI-based vision inspection system for the automatic detection of various defect types in membrane electrode assemblies (MEAs), a core component of polymer electrolyte membrane fuel cells (PEMFCs). The system targets a wide range of defects, including electrode line defects, junction damage, wrinkles, bubbles, foreign material contamination, and gasket-to-electrode misalignment. High resolution fuel cell images are preprocessed and analyzed using a deep learning pipeline that integrates classification models and a DETR(DEtection TRansformer)-based object detection framework enhanced with slicing techniques to accurately detect both visible and fine-grained defects. The system supports real-time deployment in manufacturing environments, providing defect classification, traceability, and statistical reporting. This approach significantly improves inspection precision, reliability, and scalability in PEMFC production processes.