State Recognition Technology and Application on Milling Tool Wear

Chuang Wen Xu; Hua Ling Chen; Z. Liu

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

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 10-12State Recognition Technology and Application on...

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.

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Periodical:

Applied Mechanics and Materials (Volumes 10-12)

Pages:

869-873

DOI:

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

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Online since:

December 2007

Authors:

Chuang Wen Xu, Hua Ling Chen, Z. Liu

Keywords:

Fuzzy Cluster Analysis, Recognition, Time Series Analysis, Tool Wear

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