Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing

doi:10.1016/j.rcim.2022.102470

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing
	Li, Wenhao 1; Zhang, Haiou 1; Wang, Guilan 2; Xiong, Gang3,4 ; Zhao, Meihua 5,6; Li, Guokuan 7; Li, Runsheng 1
发表期刊	ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
ISSN	0736-5845
	2023-04-01
卷号	80 页码:12
通讯作者	Li, Runsheng(lirunsheng@hust.edu.cn)
摘要	Wire and arc additive manufacturing (WAAM) is an emerging manufacturing technology that is widely used in different manufacturing industries. To achieve fully automated production, WAAM requires a dependable, efficient, and automatic defect detection system. Although machine learning is dominant in the object detection domain, classic algorithms have defect detection difficulty in WAAM due to complex defect types and noisy detection environments. This paper presents a deep learning-based novel automatic defect detection solution, you only look once (YOLO)-attention, based on YOLOv4, which achieves both fast and accurate defect detection for WAAM. YOLO-attention makes improvements on three existing object detection models: the channel-wise attention mechanism, multiple spatial pyramid pooling, and exponential moving average. The evaluation on the WAAM defect dataset shows that our model obtains a 94.5 mean average precision (mAP) with at least 42 frames per second. This method has been applied to additive manufacturing of single-pass, multi-pass deposition and parts. It demonstrates its feasibility in practical industrial applications and has potential as a vision-based methodology that can be implemented in real-time defect detection systems.
关键词	Wire and arc additive manufacturing Defect detection Online Deep learning
DOI	10.1016/j.rcim.2022.102470
关键词[WOS]	TESTING APPLICATION ; IMAGES ; INSPECTION ; YOLO
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U1909218] ; National Natural Science Foundation of China[61872365] ; Research and Development of Laser Repair Technology and Equipment, China for Landing Gear and Other Key Metal Parts of Transport Aircraft, Hubei Province Technology Innovation Special Key Project[2019AAA003] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences (CAS)[YZQT014] ; Guangdong Basic and Applied Basic Research Foundation[2021B1515140034]
项目资助者	National Natural Science Foundation of China ; Research and Development of Laser Repair Technology and Equipment, China for Landing Gear and Other Key Metal Parts of Transport Aircraft, Hubei Province Technology Innovation Special Key Project ; Scientific Instrument Developing Project of the Chinese Academy of Sciences (CAS) ; Guangdong Basic and Applied Basic Research Foundation
WOS研究方向	Computer Science ; Engineering ; Robotics
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Manufacturing ; Robotics
WOS记录号	WOS:000869978400003
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：52[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50300
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Li, Runsheng
作者单位	1.Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Wuhan 430074, Hubei, Peoples R China 2.Huazhong Univ Sci & Technol, Sch Mat Sci & Engn, Wuhan 430074, Hubei, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing Engn Res Ctr Intelligent Syst & Technol, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Guangdong Engn Res Ctr 3D Printing & Intelligent M, Cloud Comp Ctr, Donggguan 523808, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 7.Wuhan Natl Lab Optoelect, Wuhan 430074, Hubei, Peoples R China
推荐引用方式 GB/T 7714	Li, Wenhao,Zhang, Haiou,Wang, Guilan,et al. Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing[J]. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING,2023,80:12.
APA	Li, Wenhao.,Zhang, Haiou.,Wang, Guilan.,Xiong, Gang.,Zhao, Meihua.,...&Li, Runsheng.(2023).Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing.ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING,80,12.
MLA	Li, Wenhao,et al."Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing".ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 80(2023):12.