Paper Title:
State Recognition Technology and Application on Milling Tool Wear
  Abstract

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.

  Info
Periodical
Edited by
Kai Cheng, Yingxue Yao and Liang Zhou
Pages
869-873
DOI
10.4028/www.scientific.net/AMM.10-12.869
Citation
C. W. Xu, H. L. Chen, Z. Liu, "State Recognition Technology and Application on Milling Tool Wear", Applied Mechanics and Materials, Vols. 10-12, pp. 869-873, 2008
Online since
December 2007
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Price
$32.00
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