Advancing Multi-Label Melt Pool Defect Detection in Laser Powder Bed Fusion with Self-Supervised Learning

This paper presents an image-based deep learning model for analyzing melt pool images in laser powder bed fusion (LPBF) additive manufacturing. Melt pool imaging is essential for process monitoring and defect detection; however, challenges such as labeling accuracy, model robustness, and the scarcity of labeled high-frequency melt pool data remain. To address these issues, we implement a self-supervised learning approach using Bootstrap Your Own Latent (BYOL) to enhance label accuracy and improve feature extraction. Additionally, we propose an optimized pseudo-labeling strategy to further refine defect classification. Trained on a large dataset of melt pool images, our model significantly improves label reliability and predicts multiple defect types with high precision. Experimental results demonstrate robust predictive capabilities, enabling highly accurate and reliable defect detection. This advancement enhances image-based defect detection methods and contributes to the development of automated monitoring systems for LPBF, improving process stability and quality assurance.