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To overcome limitations of traditional inspection methods\u2014often inefficient, subjective, or reliant on costly offline equipment\u2014a high-precision, real-time defect-detection model, YOLO-AMI, is proposed, based on the YOLOv10 architecture. The neck network was reconstructed using the Asymptotic Feature Pyramid Network to enhance multi-scale feature fusion and suppress background noise. In addition, a parameter-free attention mechanism was integrated to adaptively emphasize critical features without increasing computational complexity. To improve detection of small defects, a composite loss function combining Normalized Wasserstein Distance and Intersection over Union was adopted. Experimental evaluation on a dataset of 6,000 AM images shows that YOLO-AMI attains a mean average precision (mAP@0.5) of 85.5%, precision of 87.1%, and recall of 83.2%, outperforming state-of-the-art models such as YOLOv8, YOLOv11, and RT-DETR-L. With an inference speed of 105.6 frames per second and a compact model size of 8.6 million parameters, the proposed approach achieves a favorable balance between accuracy and efficiency, providing a robust solution for intelligent online quality monitoring in Industry 4.0.<\/jats:p>","DOI":"10.1038\/s41598-026-44342-6","type":"journal-article","created":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T10:18:15Z","timestamp":1775902695000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["YOLO-AMI: enhancing online quality monitoring in 3D printing with composite loss and parameter-free attention"],"prefix":"10.1038","volume":"16","author":[{"given":"Zhaoxuan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohd Salman","family":"Abu Mansor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"44342_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mtphys.2025.101933","volume":"59","author":"R Wang","year":"2025","unstructured":"Wang, R. et al. 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