Cutter tool fault detection using a new spectral analysis method

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Volumn 224, Issue 12, 2010, Pages 1784-1791

  Kalvoda, T ^a   Hwang, Y R ^a   Vrabec, M ^b  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^b CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

Author keywords

cutter tool fault; Hilbert Huang transform; milling process monitoring; spectral analysis

Indexed keywords

ANALYSIS TECHNIQUES; CUTTER TOOL FAULT; CUTTING PROCESS; DATA ANALYSIS TECHNIQUES; FAULT MONITORING; FREQUENCY DOMAIN APPROACHES; FREQUENCY DOMAINS; FUNDAMENTAL FREQUENCIES; HILBERT HUANG TRANSFORMS; MILLING PROCESS; NON-LINEARITY; NON-STATIONARITIES; ON DYNAMICS; PHYSICAL NATURE; POWER-SPECTRA; SPECTRAL ANALYSIS; SPECTRAL ANALYSIS METHOD; TIME FREQUENCY DOMAIN;

CUTTING; CUTTING TOOLS; EQUIPMENT; FAULT DETECTION; FOURIER TRANSFORMS; MATHEMATICAL TRANSFORMATIONS; METADATA; MILLING (MACHINING); PROCESS CONTROL; PROCESS MONITORING; SPECTRUM ANALYSIS; TIME DOMAIN ANALYSIS;

FREQUENCY DOMAIN ANALYSIS;

EID: 78650392401     PISSN: 09544054     EISSN: None     Source Type: Journal    
DOI: 10.1243/09544054JEM1932     Document Type: Article

References (8)

1. Lan, M. S. and Naerheim, Y. In-process detection of tool breakage in milling. J. Engng Ind., 1986, 108(3), 191-198.
2. Huang, P. T., Chen, J. C., and Chou, C. Y. A statistical approach in detecting tool breakage in end milling operations. J. Ind. Technol., 1999, 15(3).
3. Weihong, Li, Weiguo, Gong, Obikawa, T., and Shirasaki, T. A method of recognizing tool-wear states based on a fast algorithm of wavelet transform. J. Mater. Processing Technol., 2005, 170(1-2), 374-380.
4. Zhu, K., Wong, Y. S., and Hong, G. S. Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results. Int. J. Machine Tools Mf, 2009, 49, 537-553.
5. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung. C. C., and Liu. H. H. The empirical mode decomposition method and the Hilbert spectrum for non stationary time series analysis. Proc. Roy. Soc. Lond., Ser. A, 1998, 454A, 903-995.
6. Huang, N. E. and Shen, S. S. P. (ed.). Hilbert-Huang transform and its applications, World Scientific, 2005, 311.
7. Huang, N. E., Wu, Z., Long, S. R., Arnold, K. C., Chen, X., and Blank, K. On instantaneous frequency. Advances in Adaptive Data Analysis, 2009, 1(2), 177-229.
8. Tlusty, G. Manufacturing equipment and processes, 2000 (Prentice-Hall).