WeldNet: A deep learning based method for weld seam type identification and initial
文献类型：期刊论文
| 作者 | Ma, Yunkai2 ; Fan, Junfeng2; Zhou, Zhen1,2; Zhao, Sihan1,2; Jing, Fengshui1,2; Tan, Min1,2
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| 刊名 | EXPERT SYSTEMS WITH APPLICATIONS
 |
| 出版日期 | 2024-03-15 |
| 卷号 | 238页码:15 |
| 关键词 | Weld seam type identification Initial point guidance Deep learning Robot welding Vision sensors |
| ISSN号 | 0957-4174 |
| DOI | 10.1016/j.eswa.2023.121700 |
| 通讯作者 | Fan, Junfeng(junfeng.fan@ia.ac.cn) ; Jing, Fengshui(fengshui.jing@ia.ac.cn) |
| 英文摘要 | To address the limitations associated with the low intelligence of welding robots, a weld seam type identification and initial point guidance method based on deep neural network named WeldNet was proposed. By incorporating channel shuffling and an attention module, the size of the WeldNet model is reduced while preserving high detection accuracy. With the help of the proposed Center-Box annotation method, the optimized WeldNet network can not only automatically identify the type of welding workpieces at a frequency of 66 Hz, but also extract the initial point of the weld seam with an error of less than 1.63 pixels. Based on the principle of "monocular vision dual position shooting", automatic guidance of the initial point of the weld seam is achieved, which greatly improves the intelligence level of welding robots. The experimental results show that the method proposed can accurately identify various types of weld joints such as butt joints, lap joints, and fillet joints with a recognition rate of 99.6%, and the method can also guide the welding torch to align with the initial point of the weld seam with an error of just 0.85 mm. |
| WOS关键词 | TRACKING ; RECOGNITION ; SYSTEM ; POSITION |
| 资助项目 | National Natural Science Foundation of China[62173327] ; National Natural Science Foundation of China[62003341] ; National Natural Science Foundation of China[62373354] ; Beijing Natural Science Foundation[4232057] ; Youth Innovation Promotion Association of CAS, China[2022130] |
| WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001087530500001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association of CAS, China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54379]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Fan, Junfeng; Jing, Fengshui |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, 19 A Yuquan Rd, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Yunkai,Fan, Junfeng,Zhou, Zhen,et al. WeldNet: A deep learning based method for weld seam type identification and initial[J]. EXPERT SYSTEMS WITH APPLICATIONS,2024,238:15. |
| APA | Ma, Yunkai,Fan, Junfeng,Zhou, Zhen,Zhao, Sihan,Jing, Fengshui,&Tan, Min.(2024).WeldNet: A deep learning based method for weld seam type identification and initial.EXPERT SYSTEMS WITH APPLICATIONS,238,15. |
| MLA | Ma, Yunkai,et al."WeldNet: A deep learning based method for weld seam type identification and initial".EXPERT SYSTEMS WITH APPLICATIONS 238(2024):15. |
入库方式： OAI收割
来源：自动化研究所
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