State recognition technology and application on milling tool wear

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

CORC > 兰州理工大学 > 兰州理工大学

	State recognition technology and application on milling tool wear
	Xu, C.W.1,2; Chen, H.L.1; Liu, Z.2
	2008
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
卷号	10-12
DOI	10.4028/www.scientific.net/AMM.10-12.869
页码	869-873
英文摘要	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.
会议录	Applied Mechanics and Materials
会议录出版者	Trans Tech Publications Ltd
语种	英语
ISSN号	16609336
内容类型	会议论文
源URL	[http://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]. 见:.