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Inventions 2018, 3(2), 25; https://doi.org/10.3390/inventions3020025

Identification of Milling Status Using Vibration Feature Extraction Techniques and Support Vector Machine Classifier

Department of Mechanical Engineering, National Chung Hsing University, Taichung City 40227, Taiwan
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Received: 11 February 2018 / Revised: 5 April 2018 / Accepted: 11 April 2018 / Published: 17 April 2018
(This article belongs to the Special Issue Selected Papers from ICI2017 and Spintech Thesis Awards)
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Abstract

The objective of this study is to use the vibration signal features of spindles during the cutting processing to identify the different milling statuses in cases of diverse tooling parameter combinations. Accelerometers were placed on a spindle to measure vibration behaviors, and the milling status could be divided into idle cutting, initial feeding, and stable cutting. Vibration signal processing and analysis were conducted in the time domain, as well as in the frequency domain. The original vibration measurements were separated using empirical mode decomposition (EMD) in the time domain, so that the signal features could be extracted in certain frequency bands and the useless signal components and trends could be removed. Multi-scale entropy (MSE) and root mean square (RMS) were computed to extract the time domain features. In the frequency domain, the specific intrinsic mode functions (IMFs) that were decomposed using the EMD method were analyzed by fast fourier transform (FFT) and a frequency normalization technique to extract the features of apparent physical representations. The Fisher scores (FS) of the extracted features are calculated to select the high-priority signal features. The selected high-priority signal features are utilized to identify the different milling statuses through a support vector machine (SVM). The results show that an identification accuracy of 98.21% could be obtained at the Z axis, and the average accuracy would be 95.91% for the three axes combination. View Full-Text
Keywords: milling status identification; multi-scale entropy; empirical mode decomposition; Fisher score; support vector machine milling status identification; multi-scale entropy; empirical mode decomposition; Fisher score; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Chang, C.-Y.; Wu, T.-Y. Identification of Milling Status Using Vibration Feature Extraction Techniques and Support Vector Machine Classifier. Inventions 2018, 3, 25.

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