ISSN:
1662-7482
Source:
Scientific.Net: Materials Science & Technology / Trans Tech Publications Archiv 1984-2008
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
A new method of state recognition of milling tool wear was presented based on timeseries analysis and fuzzy cluster analysis. After calculating, verifying liberation signal of tool state,and analyzing cutoff property, trailing property, periodicity of the sample autocorrelation functionand partial autocorrelation function as well as estimating parameter of model. It can be decided thatdynamic data serial is suit AR(p) (autoregression) model. Taking p equal to 12 as a feature vectorextraction, based on the fuzzy cluster analysis the similarity relation between the feature vector ofthe tool working state and the sample feature vector was obtained. Working state of tool wear wasdetermined according to the similarity relation of feature vector. This method was used to recognizeinitial wear state, normal wear state and acute wear state of milling tool. The result indicates thatthis method of tool wear recognition based on time series analysis and fuzzy cluster is effective
Type of Medium:
Electronic Resource
URL:
http://www.tib-hannover.de/fulltexts/2011/0528/01/38/transtech_doi~10.4028%252Fwww.scientific.net%252FAMM.10-12.869.pdf
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