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

doi:10.1109/TIE.2024.3395792

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 智能机器人系统研究

	From Model to Reality: A Robust Framework for Automatic Generation of Welding Paths
	Ma, Yunkai1 ; Fan, Junfeng1 ; Zhao, Sihan 2; Jing, Fengshui1,2 ; Wang, Shuo1,2 ; Tan, Min1,2
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
ISSN	0278-0046
	2024-05-31
页码	12
通讯作者	Fan, Junfeng(junfeng.fan@ia.ac.cn)
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.
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
DOI	10.1109/TIE.2024.3395792
关键词[WOS]	SEAM TRACKING ; EXTRACTION ; SENSOR ; LASER
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS类目	Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:001236613200001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/58500
专题	多模态人工智能系统全国重点实验室_智能机器人系统研究复杂系统认知与决策实验室_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
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.