{"ID":2876073,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.01754","arxiv_id":"2509.01754","title":"TransMatch: A Transfer-Learning Framework for Defect Detection in Laser Powder Bed Fusion Additive Manufacturing","abstract":"Surface defects in Laser Powder Bed Fusion (LPBF) pose significant risks to the structural integrity of additively manufactured components. This paper introduces TransMatch, a novel framework that merges transfer learning and semi-supervised few-shot learning to address the scarcity of labeled AM defect data. By effectively leveraging both labeled and unlabeled novel-class images, TransMatch circumvents the limitations of previous meta-learning approaches. Experimental evaluations on a Surface Defects dataset of 8,284 images demonstrate the efficacy of TransMatch, achieving 98.91% accuracy with minimal loss, alongside high precision, recall, and F1-scores for multiple defect classes. These findings underscore its robustness in accurately identifying diverse defects, such as cracks, pinholes, holes, and spatter. TransMatch thus represents a significant leap forward in additive manufacturing defect detection, offering a practical and scalable solution for quality assurance and reliability across a wide range of industrial applications.","short_abstract":"Surface defects in Laser Powder Bed Fusion (LPBF) pose significant risks to the structural integrity of additively manufactured components. This paper introduces TransMatch, a novel framework that merges transfer learning and semi-supervised few-shot learning to address the scarcity of labeled AM defect data. By effect...","url_abs":"https://arxiv.org/abs/2509.01754","url_pdf":"https://arxiv.org/pdf/2509.01754v1","authors":"[\"Mohsen Asghari Ilani\",\"Yaser Mike Banad\"]","published":"2025-09-01T20:15:26Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"physics.comp-ph\"]","methods":"[]","has_code":false}