中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 合肥物质科学研究院
  3. 中国科学院合肥物质科学研究院
Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method

文献类型：期刊论文

作者Liu, Tongshun1; Zhu, Kunpeng2; Wang, Gang1
刊名INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
出版日期2020-11-07
关键词Micro-milling Cutting force model Tool runout Cutting force coefficient identification Tool wear monitoring
ISSN号0268-3768
DOI10.1007/s00170-020-06272-z
通讯作者Liu, Tongshun(tongshunliu@hotmail.com)
英文摘要Extracting discriminative tool wear features is of great importance for tool wear monitoring in micro-milling. However, due to the dependency on tool runout and cutting parameters, the traditional tool wear features are incompetent to monitor the tool wear condition in micro-milling with significant tool runout and varied cutting parameter interactions. In this study, micro-milling cutting force is represented by a parametric model including variable cutting parameters, tool runout, and tool wear. The cutting force coefficient in the model, which is not only discriminative to the tool wear condition but also independent to the tool runout and cutting parameters, is extracted as the micro-milling tool wear feature. To reduce the computation cost, a fast neural network-based method is proposed to identify the tool runout and the cutting force coefficient from the cutting force signal. Experimental results show that the proposed cutting force coefficient-based approach is efficient to monitor the micro-milling tool wear under varied cutting parameters and tool runout.
WOS关键词CHIP THICKNESS MODEL ; LIFE PREDICTION ; NEURAL-NETWORK ; SYSTEM ; STATE ; SENSOR
资助项目Natural Science Foundation of the Jiangsu Higher Education Institutions of China[19KJB460007] ; National Natural Science Foundation of China[51805341] ; Natural Science Foundation of Jiangsu Province[BK20180843]
WOS研究方向Automation & Control Systems ; Engineering
语种英语
WOS记录号WOS:000587273600004
出版者SPRINGER LONDON LTD
资助机构Natural Science Foundation of the Jiangsu Higher Education Institutions of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/105160]  
专题中国科学院合肥物质科学研究院
通讯作者Liu, Tongshun
作者单位1.Soochow Univ, Sch Mech & Elect Engn, Suzhou 215021, Jiangsu, Peoples R China
2.Chinese Acad Sci, Inst Adv Mfg Technol, Hefei Inst Phys Sci, Changzhou 213164, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Tongshun,Zhu, Kunpeng,Wang, Gang. Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method[J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY,2020.
APA Liu, Tongshun,Zhu, Kunpeng,&Wang, Gang.(2020).Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method.INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY.
MLA Liu, Tongshun,et al."Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method".INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020).

入库方式： OAI收割

来源：合肥物质科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。