State recognition technology and application on milling tool wear

doi:10.4028/www.scientific.net/AMM.10-12.869

	State recognition technology and application on milling tool wear
	Xu, C.W.1,2; Chen, H.L.1; Liu, Z.2
	2008
会议名称	e-Engineering and Digital Enterprise Technology, e-ENGDET 2007
会议录名称	Applied Mechanics and Materials
卷号	10-12
页码	869-873
出版者	Trans Tech Publications Ltd
摘要	A new method of state recognition of milling tool wear was presented based on time series analysis and fuzzy cluster analysis. After calculating, verifying liberation signal of tool state, and analyzing cutoff property, trailing property, periodicity of the sample autocorrelation function and partial autocorrelation function as well as estimating parameter of model. It can be decided that dynamic data serial is suit AR(p) (autoregression) model. Taking p equal to 12 as a feature vector extraction, based on the fuzzy cluster analysis the similarity relation between the feature vector of the tool working state and the sample feature vector was obtained. Working state of tool wear was determined according to the similarity relation of feature vector. This method was used to recognize initial wear state, normal wear state and acute wear state of milling tool. The result indicates that this method of tool wear recognition based on time series analysis and fuzzy cluster is effective.
关键词	Autocorrelation Cluster analysis Cutting tools Fuzzy clustering Harmonic analysis Milling (machining) Regression analysis State estimation Vectors Wear of materials Autocorrelation functions Estimating parameters Feature vector extraction Partial autocorrelation function Recognition Similarity relations State recognition Tool wear
DOI	10.4028/www.scientific.net/AMM.10-12.869
收录类别	EI
语种	英语
EI入藏号	20082711343043
EI主题词	Time series analysis
ISSN	16609336
来源库	Compendex
分类代码	603.2 Machine Tool Accessories - 604.2 Machining Operations - 723 Computer Software, Data Handling and Applications - 731.1 Control Systems - 921 Mathematics - 922.2 Mathematical Statistics - 951 Materials Science
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/116967
专题	兰州理工大学
作者单位	1.School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; 2.Department of Mechanical Engineering, Lanzhou Polytechnic College, Lanzhou, 730050, China
推荐引用方式 GB/T 7714	Xu, C.W.,Chen, H.L.,Liu, Z.. State recognition technology and application on milling tool wear[C]:Trans Tech Publications Ltd,2008:869-873.