Journal
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
Volume 69, Issue -, Pages 11-19Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ijmachtools.2013.02.007
Keywords
Chatter identification; Wavelet packet; Hilbert-Huang transform; Vibration signal; End milling
Funding
- University of British Columbia
- National Natural Science Foundation of China [51105294, 51222503]
- National Basic Research Program of China [2009CB724405]
- National Science and Technology Major Project [2012ZX04010-011]
- Fundamental Research Funds for the Central Universities
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Chatter detection is an important task to improve productivity and part quality in the machining process. Since measured signals from sensors are usually contaminated by background noise and other disturbances, it is necessary to find efficient signal processing algorithms to identify the chatter as soon as possible. This paper presents an effective chatter identification method for the end milling process based on the study of two advanced signal processing techniques, i.e., wavelet package transform (WPT) and Hilbert-Huang transform (HHT). The WPT works as a preprocessor to denoise the measured signals and hence the performance of the HHT is enhanced. The proposed method consists of four steps. First, the measured signals are decomposed by the WPT, so that the chatter signals are allocated in a certain frequency band. Secondly, wavelet packets with rich chatter information are selected and are used to reconstruct new signals. Thirdly, the reconstructed signals are analyzed with HHT to obtain a Hilbert-Huang spectrum, which is a full time-frequency-energy distribution of the signals. Finally, the mean value and standard deviation of the Hilbert-Huang spectrum are calculated to detect the chatter and identify its levels as well. The proposed method is applied to the end milling process and the experimental results prove that the method can identify the chatter effectively. (C) 2013 Elsevier Ltd. All rights reserved.
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