Estimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine method

Estimation the wear state of milling tools using a combined ensemble empirical mode decomposition and support vector machine method

Vibrational signals resulting from tool wear have non-linear and non-stationary features. It is also difficult to acquire large numbers of typically worn samples in practice. In this work, a method of predicting the wear of milling tools is proposed based on ensemble empirical mode decomposition (EE...

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Bibliographic Details
Main Authors: Chuangwen XU, Yuzhen CHAI, Huaiyuan LI, Zhicheng SHI
Format: Article
Language:English
Published: The Japan Society of Mechanical Engineers 2018-06-01
Series:Journal of Advanced Mechanical Design, Systems, and Manufacturing
Subjects:
cutting vibration
ensemble empirical mode decomposition
intrinsic mode function
energy entropy
support vector machine
tool wear prediction
Online Access:https://www.jstage.jst.go.jp/article/jamdsm/12/2/12_2018jamdsm0059/_pdf/-char/en

Internet

https://www.jstage.jst.go.jp/article/jamdsm/12/2/12_2018jamdsm0059/_pdf/-char/en

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