Analysis of Acoustic Emission Signals in Machining Academic Article uri icon

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. © 1999 by ASME.

author list (cited authors)

  • Bukkapatnam, S., Kumara, S., & Lakhtakia, A.

citation count

  • 34

publication date

  • November 1999