Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method

doi:10.1007/s00170-020-06272-z

HFCAS OpenIR

	Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method
	Liu, Tongshun 1; Zhu, Kunpeng 2; Wang, Gang 1
	2020-11-07
发表期刊	INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
ISSN	0268-3768
通讯作者	Liu, Tongshun(tongshunliu@hotmail.com)
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.
关键词	Micro-milling Cutting force model Tool runout Cutting force coefficient identification Tool wear monitoring
DOI	10.1007/s00170-020-06272-z
关键词[WOS]	CHIP THICKNESS MODEL ; LIFE PREDICTION ; NEURAL-NETWORK ; SYSTEM ; STATE ; SENSOR
收录类别	SCI
语种	英语
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]
项目资助者	Natural Science Foundation of the Jiangsu Higher Education Institutions of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province
WOS研究方向	Automation & Control Systems ; Engineering
WOS类目	Automation & Control Systems ; Engineering, Manufacturing
WOS记录号	WOS:000587273600004
出版者	SPRINGER LONDON LTD
引用统计	被引频次：26[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/105161
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).