Analysis of Acoustic Emission Signals in Machining

[+] Author and Article Information
S. T. S. Bukkapatnam

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

S. R. T. Kumara

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

A. Lakhtakia

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

J. Manuf. Sci. Eng 121(4), 568-576 (Nov 01, 1999) (9 pages) doi:10.1115/1.2833058 History: Received September 01, 1997; Revised June 01, 1998; Online January 17, 2008


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.

Copyright © 1999 by The American Society of Mechanical Engineers
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