| Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing | |
Li, Wenhao1; Zhang, Haiou1; Wang, Guilan2; Xiong, Gang3,4 ; Zhao, Meihua5,6; Li, Guokuan7; Li, Runsheng1
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| Source Publication | ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
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| ISSN | 0736-5845 |
| 2023-04-01 | |
| Volume | 80Pages:12 |
| Corresponding Author | Li, Runsheng(lirunsheng@hust.edu.cn) |
| Abstract | 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. |
| Keyword | Wire and arc additive manufacturing Defect detection Online Deep learning |
| DOI | 10.1016/j.rcim.2022.102470 |
| WOS Keyword | TESTING APPLICATION ; IMAGES ; INSPECTION ; YOLO |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 Research Area | Computer Science ; Engineering ; Robotics |
| WOS Subject | Computer Science, Interdisciplinary Applications ; Engineering, Manufacturing ; Robotics |
| WOS ID | WOS:000869978400003 |
| Publisher | PERGAMON-ELSEVIER SCIENCE LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50300 |
| Collection | 复杂系统管理与控制国家重点实验室_平行智能技术与系统团队 |
| Corresponding Author | Li, Runsheng |
| Affiliation | 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 |
| Recommended Citation 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. |
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