AccScience Publishing / MSAM / Volume 4 / Issue 3 / DOI: 10.36922/MSAM025150022
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ORIGINAL RESEARCH ARTICLE

Artificial intelligence-driven defect detection and localization in metal 3D printing using convolutional neural networks

Xinyi Yin1* ,  Jan Akmal1,2 ,  Mika Salmi1
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1 Materials to Products Research Group, Department of Energy and Mechanical Engineering, Aalto University, Espoo, Finland
2 EOS Metal Materials, Electro Optical Systems Finland Oy, Turku, Finland
MSAM 2025, 4(3), 025150022 https://doi.org/10.36922/MSAM025150022
Received: 7 April 2025 | Accepted: 20 May 2025 | Published online: 25 June 2025
© 2025 by the Author(s).. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Metal additive manufacturing (AM) has attracted significant interest in high-value industries due to its ability to produce complex parts flexibly, but the reliance on costly manual monitoring remains a major burden for quality control. Artificial intelligence (AI)-driven models for automated defect detection are emerging as promising solutions. This study contributes a new annotated dataset for AI research in AM and evaluates the performance of four widely used convolutional neural network (CNN) models in detecting powder bed morphology defects, based on layer-wise images acquired by the EOSTATE PowderBed system during the metal laser-based powder bed fusion process. The models were trained through transfer learning methods with manually labeled and pre-processed data. Results demonstrated that ResNet50 and EfficientNetV2B0 achieved over 99% accuracy in defect classification, while YOLOv5 outperformed Faster region-based-CNN in defect detection and localization. However, lower average precision values in object detection tasks were attributed to variability in defect scales and annotation quality. This study confirms the potential of AI-based models for defect identification in AM, with YOLOv5 demonstrating clear advantages in managing complex, multi-scale defects. Future improvements will focus on expanding the dataset and refining annotation strategies to further enhance model robustness.

 

Graphical abstract
Keywords
Machine learning
Defects detection
Quality control
AI-driven models
Metal additive manufacturing
Image classification
Object detection
Funding
This research was financially supported by the Finnish Doctoral Program Network in Artificial Intelligence (AI-DOC, decision number VN/3137/2024-OKM-6) and the Tandem Industry Academia funding from the Finnish Research Impact Foundation.
Conflict of interest
Mika Salmi serves as the Editorial Board Member of the journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Other authors declare they have no competing interests.
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Materials Science in Additive Manufacturing, Electronic ISSN: 2810-9635 Published by AccScience Publishing
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