Wavelet representation of Acoustic Emission in turning process

Sagar V. Kamarthi, Soundar Rajan Tirupatikumara, Paul H. Cohen

Research output: Contribution to conferencePaper

15 Citations (Scopus)

Abstract

This paper deals with the representational and analysis issues of Acoustic Emission (AE) signals in turning processes. In earlier works, the power spectral density of AE signals is computed from Fourier transform based techniques. This paper investigates the superiority of a wavelet transform over the Fourier transform in analyzing rapidly changing signals such as AE, in which high frequency components are to be studied with sharper time resolution than low frequency components. The effectiveness of the wavelet representation of AE signals is studied in the context of flank wear estimation problem in turning processes. A set of turning experiments are conducted in which the flank wear is monitored through AE signals. A specially designed neural network architecture is used to relate AE features to flank wear. The accurate flank wear estimation results indicate that the wavelet transform representation of AE signals is very effective in extracting the AE features sensitive to gradually increasing flank wear.

Original languageEnglish (US)
Pages861-866
Number of pages6
StatePublished - Dec 1 1995
EventProceedings of the 1995 Artificial Neural Networks in Engineering, ANNIE'95 - St.Louis, MO, USA
Duration: Nov 12 1995Nov 15 1995

Other

OtherProceedings of the 1995 Artificial Neural Networks in Engineering, ANNIE'95
CitySt.Louis, MO, USA
Period11/12/9511/15/95

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Acoustic emissions
Wear of materials
Wavelet transforms
Fourier transforms
Power spectral density
Network architecture
Neural networks

All Science Journal Classification (ASJC) codes

  • Software

Cite this

Kamarthi, S. V., Tirupatikumara, S. R., & Cohen, P. H. (1995). Wavelet representation of Acoustic Emission in turning process. 861-866. Paper presented at Proceedings of the 1995 Artificial Neural Networks in Engineering, ANNIE'95, St.Louis, MO, USA, .
Kamarthi, Sagar V. ; Tirupatikumara, Soundar Rajan ; Cohen, Paul H. / Wavelet representation of Acoustic Emission in turning process. Paper presented at Proceedings of the 1995 Artificial Neural Networks in Engineering, ANNIE'95, St.Louis, MO, USA, .6 p.
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Kamarthi, SV, Tirupatikumara, SR & Cohen, PH 1995, 'Wavelet representation of Acoustic Emission in turning process' Paper presented at Proceedings of the 1995 Artificial Neural Networks in Engineering, ANNIE'95, St.Louis, MO, USA, 11/12/95 - 11/15/95, pp. 861-866.

Wavelet representation of Acoustic Emission in turning process. / Kamarthi, Sagar V.; Tirupatikumara, Soundar Rajan; Cohen, Paul H.

1995. 861-866 Paper presented at Proceedings of the 1995 Artificial Neural Networks in Engineering, ANNIE'95, St.Louis, MO, USA, .

Research output: Contribution to conferencePaper

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AU - Kamarthi, Sagar V.

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N2 - This paper deals with the representational and analysis issues of Acoustic Emission (AE) signals in turning processes. In earlier works, the power spectral density of AE signals is computed from Fourier transform based techniques. This paper investigates the superiority of a wavelet transform over the Fourier transform in analyzing rapidly changing signals such as AE, in which high frequency components are to be studied with sharper time resolution than low frequency components. The effectiveness of the wavelet representation of AE signals is studied in the context of flank wear estimation problem in turning processes. A set of turning experiments are conducted in which the flank wear is monitored through AE signals. A specially designed neural network architecture is used to relate AE features to flank wear. The accurate flank wear estimation results indicate that the wavelet transform representation of AE signals is very effective in extracting the AE features sensitive to gradually increasing flank wear.

AB - This paper deals with the representational and analysis issues of Acoustic Emission (AE) signals in turning processes. In earlier works, the power spectral density of AE signals is computed from Fourier transform based techniques. This paper investigates the superiority of a wavelet transform over the Fourier transform in analyzing rapidly changing signals such as AE, in which high frequency components are to be studied with sharper time resolution than low frequency components. The effectiveness of the wavelet representation of AE signals is studied in the context of flank wear estimation problem in turning processes. A set of turning experiments are conducted in which the flank wear is monitored through AE signals. A specially designed neural network architecture is used to relate AE features to flank wear. The accurate flank wear estimation results indicate that the wavelet transform representation of AE signals is very effective in extracting the AE features sensitive to gradually increasing flank wear.

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Kamarthi SV, Tirupatikumara SR, Cohen PH. Wavelet representation of Acoustic Emission in turning process. 1995. Paper presented at Proceedings of the 1995 Artificial Neural Networks in Engineering, ANNIE'95, St.Louis, MO, USA, .