Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy

Min Zhang; Hong Qi Liu; Bin Li

doi:10.4028/www.scientific.net/AMM.541-542.1419

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 541-542Face Milling Tool Wear Condition Monitoring Based...

Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy

Abstract:

Tool condition monitoring is an important issue in the advanced machining process. Existing methods of tool wear monitoring is hardly suitable for mass production of cutting parameters fluctuation. In this paper, a new method for milling tool wear condition monitoring base on tunable Q-factor wavelet transform and Shannon entropy is presented. Spindle motor current signals were recorded during the face milling process. The wavelet energy entropy of the current signals carries information about the change of energy distribution associated with different tool wear conditions. Experiment results showed that the new method could successfully extract significant signature from the spindle-motor current signals to effectively estimate tool wear condition during face milling.

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Periodical:

Applied Mechanics and Materials (Volumes 541-542)

Pages:

1419-1423

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.541-542.1419

Citation:

Cite this paper

Online since:

March 2014

Authors:

Min Zhang*, Hong Qi Liu, Bin Li

Keywords:

Face Milling, Motor Current, Shannon Entropy, Tool Wear, Wavelet Transform (WT)

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* - Corresponding Author

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