Analysis of Acoustic Emission Signals in Machining-Reference-Cited by-同舟云学术

Analysis of Acoustic Emission Signals in Machining

Published:1999-11-01 Issue:4 Volume:121 Page:568-576
ISSN:1087-1357
Container-title:Journal of Manufacturing Science and Engineering
language:en
Short-container-title:

Author:

Bukkapatnam S. T. S.¹,Kumara S. R. T.²,Lakhtakia A.³

Affiliation:

1. Industrial and Systems Engineering, University of Southern California, Los Angeles CA 90089

2. Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park PA 16802

3. Engineering Science and Mechanics, The Pennsylvania State University, University Park PA 16802

Abstract

Acoustic emission (AE) signals are emerging as promising means for monitoring machining processes, but understanding their generation is presently a topic of active research; hence techniques to analyze them are not completely developed. In this paper, we present a novel methodology based on chaos theory, wavelets and neural networks, for analyzing AE signals. Our methodology involves a thorough signal characterization, followed by signal representation using wavelet packets, and state estimation using multilayer neural networks. Our methodology has yielded a compact signal representation, facilitating the extraction of a tight set of features for flank wear estimation.

Publisher

ASME International

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Control and Systems Engineering

Link

http://asmedigitalcollection.asme.org/manufacturingscience/article-pdf/121/4/568/5633765/568_1.pdf

Reference23 articles.

1. Abarbanel H. , BrownR., and TsimiringL., “The Analysis of Observed Chaotic Data in Physical Systems,” Reviews of Modern Physics, Vol. 65, pp. 1331–1422, 1993.

2. Bukkapatnam S. T. S. , LakhtakiaA., and KumaraS. R. T., “Analysis of Sensor Signals Shows That Turning Process on a Lathe Exhibits Low-dimensional Chaos,” Physical Review E, Vol. 52(3), pp. 2375–2387, 1995.

3. Bukkapatnam, S. T. S., Lakhtakia, A., Kumara, S. R. T., and Satapathy, G., “Characterization of Nonlinearity of Cutting Tool Vibrations and Chatter,” Proceedings of ASME International Mechanical Engineering Congress and Exposition, San Francisco, CA, 1995.

4. Bukkapatnam, S. T. S., Kumara, S. R. T., and Lakhtakia, A., “Fractal Estimation of Flank Wear in Turning,” Submitted to ASME Journal of Dynamic Systems, Measurements and Control, 1997.

5. Bukkapatnam, S. T. S., Kumara, S. R. T., and Lakhtakia, A., “Local Eigenfunctions Based Suboptimal Wavelet Packet Representation of Contaminated Chaotic Signals,” Working Paper, Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA, 1997.

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