WeldNet: A deep learning based method for weld seam type identification and initial

doi:10.1016/j.eswa.2023.121700

	WeldNet: A deep learning based method for weld seam type identification and initial
	Ma, Yunkai1 ; Fan, Junfeng1 ; Zhou, Zhen1,2 ; Zhao, Sihan 1,2; Jing, Fengshui1,2 ; Tan, Min1,2
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
	2024-03-15
卷号	238 页码:15
通讯作者	Fan, Junfeng(junfeng.fan@ia.ac.cn) ; Jing, Fengshui(fengshui.jing@ia.ac.cn)
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.
关键词	Weld seam type identification Initial point guidance Deep learning Robot welding Vision sensors
DOI	10.1016/j.eswa.2023.121700
关键词[WOS]	TRACKING ; RECOGNITION ; SYSTEM ; POSITION
收录类别	SCI
语种	英语
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]
项目资助者	National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association of CAS, China
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:001087530500001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：2[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/173211/54380
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.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, 19 A Yuquan Rd, 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,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.