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From Model to Reality: A Robust Framework for Automatic Generation of Welding Paths

文献类型：期刊论文

作者Ma, Yunkai1; Fan, Junfeng1; Zhao, Sihan2; Jing, Fengshui1,2; Wang, Shuo1,2; Tan, Min1,2
刊名IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
出版日期2024-05-31
页码12
关键词Welding Point cloud compression Vision sensors Solid modeling Robot kinematics Cameras Intelligent sensors CAD model deep neural network robotic welding spatial curve weld welding path generation
ISSN号0278-0046
DOI10.1109/TIE.2024.3395792
通讯作者Fan, Junfeng(junfeng.fan@ia.ac.cn)
英文摘要Current programming methods for welding robots mainly rely on manual teaching or offline programming, making it difficult to adapt to the flexible production mode of small batches and multiple categories. To this end, a robotic welding path automatic generation framework is proposed in this article. The framework performs nonrigid registration between point clouds sampled from computeraided design (CAD) models of workpieces with point clouds captured by self-designed hybrid vision sensors. By doing so, the welding paths extracted from CAD models are transformed into actual welding paths. In addition, the WeldNet network is proposed to automatically identify weld types and key points, and the interested welding area is automatically extracted based on the point cloud segmentation network PointROINet. Combined with the coded structured light vision model, the 3-D coordinates of weld key points are obtained, thereby enabling fast and accurate registration of weld point clouds. Experimental results demonstrate that the proposed framework can efficiently and robustly generate welding paths for spatial curve butt welds, lap welds, and fillet welds before welding.
WOS关键词SEAM TRACKING ; EXTRACTION ; SENSOR ; LASER
资助项目National Key Research and Development Program of China[2023YFB4706800] ; National Natural Science Foundation of China[62373354] ; National Natural Science Foundation of China[62173327] ; Beijing Natural Science Foundation[4232057] ; National Commercial Aircraft Manufacturing Engineering Technology Research Center Innovation Foundation of China[20221870] ; Youth Innovation Promotion Association of CAS[2022130]
WOS研究方向Automation & Control Systems ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:001236613200001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Commercial Aircraft Manufacturing Engineering Technology Research Center Innovation Foundation of China ; Youth Innovation Promotion Association of CAS
源URL[http://ir.ia.ac.cn/handle/173211/58499]  
专题智能机器人系统研究
复杂系统管理与控制国家重点实验室_水下机器人
通讯作者Fan, Junfeng
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Yunkai,Fan, Junfeng,Zhao, Sihan,et al. From Model to Reality: A Robust Framework for Automatic Generation of Welding Paths[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2024:12.
APA Ma, Yunkai,Fan, Junfeng,Zhao, Sihan,Jing, Fengshui,Wang, Shuo,&Tan, Min.(2024).From Model to Reality: A Robust Framework for Automatic Generation of Welding Paths.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,12.
MLA Ma, Yunkai,et al."From Model to Reality: A Robust Framework for Automatic Generation of Welding Paths".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2024):12.

入库方式： OAI收割

来源：自动化研究所

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