Online tool wear monitoring by super-resolution based machine vision
文献类型：期刊论文
| 作者 | Zhu, Kunpeng1,3; Guo, Hao1,2; Li, Si3; Lin, Xin3 |
| 刊名 | COMPUTERS IN INDUSTRY
 |
| 出版日期 | 2023 |
| 卷号 | 144 |
| ISSN号 | 0166-3615 |
| 关键词 | Single image super -resolution Sparse decomposition Micro machining Tool monitoring |
| 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. |
| WOS关键词 | SINGLE-IMAGE SUPERRESOLUTION ; PREDICTION ; RECONSTRUCTION |
| 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 |
| 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.Changzhou Inst Adv Mfg Technol, Changzhou 213164, Peoples R China 3.Automation Wuhan Univ Sci & Technol, Inst Precis Mfg, Sch Machinery, Wuhan 430081, 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). |
入库方式： OAI收割
来源：合肥物质科学研究院
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