用迁移学习+半监督少样本方法，解决增材制造缺陷数据少难题
TransMatch: A Transfer-Learning Framework for Defect Detection in Laser Powder Bed Fusion Additive Manufacturing
- 融合迁移学习与半监督少样本学习，利用少量标注数据和大量未标注数据
- 在8284张图像上达到98.91%准确率，各类缺陷识别效果优异
- 适合工业质检场景，尤其适用于标注数据稀缺的增材制造领域
激光粉末床熔融（LPBF）表面缺陷严重影响增材制造构件的结构完整性。本文提出TransMatch框架，结合迁移学习与半监督少样本学习，缓解增材制造缺陷标注数据稀缺的问题。通过有效利用标注与未标注的新类别图像，克服了传统元学习方法的局限性。在包含8,284张图像的表面缺陷数据集上进行实验，TransMatch实现了98.91%的准确率，同时保持高精度、高召回率和高F1分数，能够精准识别裂纹、孔洞、凹坑和飞溅等多种缺陷。结果表明该方法在复杂缺陷检测中具备强鲁棒性，为增材制造质量控制与可靠性提供了可扩展的实用解决方案。
原文摘要 · Abstract (English)
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.
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