Online tool wear monitoring by super-resolution based machine vision

doi:10.1016/j.compind.2022.103782

	Online tool wear monitoring by super-resolution based machine vision
	Zhu, Kunpeng 1,2; Guo, Hao 1,3; Li, Si 2; Lin, Xin 2
刊名	COMPUTERS IN INDUSTRY
	2023
卷号	144
关键词	Single image super -resolution Sparse decomposition Micro machining Tool monitoring
ISSN号	0166-3615
DOI	10.1016/j.compind.2022.103782
通讯作者	Lin, Xin(xinlin@wust.edu.cn)
英文摘要	The tool condition has been a major concern in modern computer numerical control (CNC) machining due to its direct effects on the quality of final product both in the surface and dimensional integrity. The conventional machine vision-based tool condition monitoring (TCM) approaches cannot meet the high precision requirement in micro machining, as the cutting parameters are in micro scale and the spindle works in high rotation speed which makes online tool wear measurement quite difficult. To meet these challenges, this study develops a single image super-resolution (SISR) approach for direct tool wear estimation in micro-milling. Motivated by the selfsimilarity of tool wear image morphology, this study proposes a sparse decomposition framework by learning dictionaries from the tool wear image pyramid. Based on their multi-scale invariant properties, the similar image patches of coarse scales can be retrieved from fine scales to reconstruct the high-resolution image fastly and in high quality. The reconstructed high-resolution image then can be conveniently applied to wear monitoring, and overcomes the image acquisition deficiencies of the conventional machine vision-based monitoring approaches. Experimental results validate this approach for the tool wear area estimation as well as its generalization of the wear width with regarding to the conventional manual measurements.
资助项目	National Key Research and Development Program of China ; Chinese Ministry of Science and Technology ; National Natural Science Foundation of China ; [2018YFB1703200] ; [5127053454]
WOS关键词	SINGLE-IMAGE SUPERRESOLUTION ; PREDICTION ; RECONSTRUCTION
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000863721900002
资助机构	National Key Research and Development Program of China ; Chinese Ministry of Science and Technology ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/129313]
专题	中国科学院合肥物质科学研究院
通讯作者	Lin, Xin
作者单位	1.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Changzhou 213164, Peoples R China 2.Automation Wuhan Univ Sci & Technol, Inst Precis Mfg, Sch Machinery, Wuhan 430081, Peoples R China 3.Changzhou Inst Adv Mfg Technol, Changzhou 213164, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Kunpeng,Guo, Hao,Li, Si,et al. Online tool wear monitoring by super-resolution based machine vision[J]. COMPUTERS IN INDUSTRY,2023,144.
APA	Zhu, Kunpeng,Guo, Hao,Li, Si,&Lin, Xin.(2023).Online tool wear monitoring by super-resolution based machine vision.COMPUTERS IN INDUSTRY,144.
MLA	Zhu, Kunpeng,et al."Online tool wear monitoring by super-resolution based machine vision".COMPUTERS IN INDUSTRY 144(2023).