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.
Abstract (translated)
激光粉末床熔合（LPBF）中的表面缺陷对增材制造部件的结构完整性构成了重大风险。本文介绍了TransMatch，这是一种将迁移学习和半监督少样本学习相结合的新框架，旨在解决增材制造缺陷数据标签稀缺的问题。通过有效利用标记和未标记的新类图像，TransMatch克服了先前元学习方法的限制。 在包含8,284张图像的表面缺陷数据集上进行的实验评估表明，TransMatch具有显著的效果，在保持最小损失的情况下实现了98.91%的准确性，并且对于多个缺陷类别都达到了高精度、召回率和F1分数。这些发现强调了其能够准确识别各种类型的缺陷（如裂纹、针孔、孔洞和飞溅）的能力。 因此，TransMatch在增材制造缺陷检测方面代表了一个重要的进步，为确保广泛工业应用中的质量和可靠性提供了实际且可扩展的解决方案。
URL
https://arxiv.org/abs/2509.01754
