TransMatch: A Transfer-Learning Framework for Defect Detection in Laser Powder Bed Fusion Additive Manufacturing

2025/09/01 by Mohsen Asghari Ilani, Ilani, Mohsen Asghari, Yaser Mike Banad +1 · 1 citation
Computer Science · Engineering · #Additive Manufacturing Materials and Processes #Additive Manufacturing and 3D Printing Technologies #Advanced Neural Network Applications #Computational Physics (physics.comp-ph) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences

paper · pdf · doi:10.48550/arxiv.2509.01754

openalex publication_date 2025/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• A Closer Look at Few-shot Classification
• Dynamic Few-Shot Visual Learning without Forgetting
• Few-Shot Image Recognition by Predicting Parameters from Activations
• ImageNet classification with deep convolutional neural networks

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