AccScience Publishing / MSAM / Volume 4 / Issue 4 / DOI: 10.36922/MSAM025210035
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ORIGINAL RESEARCH ARTICLE

Lightweight You Only Look Once-based automatic defect detection in wire arc additive manufacturing

Runsheng Li1 ,  Hui Ma1 ,  Baoqiang Cong2* ,  Yongjun Shi1 ,  Caiyou Zeng3 ,  Yanzhen Zhang1 ,  Boce Xue1 ,  Chaolin Tan4*
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1 College of Mechanical and Electronic Engineering, China University of Petroleum (East China), Qingdao, Shandong, China
2 School of Mechanical Engineering and Automation, Beihang University, Beijing, China
3 School of Mechanical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
4 Institute of Metallic Materials and Intelligent Manufacturing, Soochow University, Suzhou, Jiangsu, China
MSAM 2025, 4(4), 025210035 https://doi.org/10.36922/MSAM025210035
Received: 22 May 2025 | Accepted: 30 June 2025 | Published online: 15 August 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

Wire arc additive manufacturing (WAAM) technology has achieved significant advancements in fabricating complex metal components, yet defects such as surface porosity, lack of fusion, and slag inclusion continue to compromise quality and efficiency. To address challenges in detecting welding slag and micro-pores, we introduce an enhanced You Only Look Once (YOLO)v8n-Attention-Refined Feature Module (ARFM) architecture integrating receptive field attention convolution, an attention-based feature pyramid network, and the Focaler-minimum point distance intersection over union metric. This integration markedly improves the precision and resilience of defect recognition. Moreover, we assessed the defect detection capabilities of YOLOv3t, YOLOv5n, YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n in WAAM and developed a composite defect dataset incorporating aluminum, titanium, and stainless steel specimens. The experiments reveal that YOLOv8n achieved the highest overall detection effectiveness (mean average precision [mAP]@0.5 = 0.921). YOLOv10n exhibited marginal superiority in slag inclusion detection (mAP@0.5 = 0.886) with a peak throughput of 54.35 frames per second (FPS), while YOLOv5n converged the fastest within 500 epochs. Following the introduction of ARFM, mAP@0.5 increased to 0.944 (+2.4%), and slag inclusion detection reached 0.929 (+7.3%). However, the frame rate declined to 38 FPS, which suffices for basic real-time monitoring but remains inadequate for high real-time scenarios. This study provides an important reference for deploying YOLO-series models for real-time monitoring of WAAM and closed-loop control systems.

Graphical abstract
Keywords
Wire arc additive manufacturing
Lightweight You Only Look Once
Defect detection
Asymptotic feature pyramid network
Receptive field augmented convolution
Focaler-minimum point distance intersection over union
Funding
This work is sponsored by the CNPC Innovation Foundation (Grant No. 2024DQ02-0306), the Basic Business Research Fees of Central Universities (Grant No. 22CX06049A), the Natural Science Foundation of Qingdao (Grant No. 23-2-1-83-zyyd-jch), and the Natural Science Foundation of Shandong Province (Grant No. ZR2023QE164).
Conflict of interest
The authors declare that 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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