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.
Issue Section:
Research Papers
1.
Abarbanel
H.
Brown
R.
Tsimiring
L.
The Analysis of Observed Chaotic Data in Physical Systems
,” Reviews of Modern Physics
, Vol. 65
, pp. 1331
–1422
, 1993
.2.
Bukkapatnam
S. T. S.
Lakhtakia
A.
Kumara
S. 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.
6.
Byrne
G.
Dornfeld
D.
Inasaki
I.
Ketteler
G.
Konig
W.
Teti
R.
Tool Condition Monitoring (TCM)—The Status of Research and Industrial Application
,” CIRP Annals
, Vol. 44
(2
), pp. 541
–567
, 1995
.7.
Chang, Y. P., Hashimura, M., and Dornfeld, D. A., “Analysis of Orthogonal Micro-cutting Using Acoustic Emission,” Proceedings of ASME International Mechanical Engineering Congress and Exposition, San Francisco, CA, 1995.
8.
Chittayil, K., Acoustic Emission Sensing for Tool Wear Estimation and Control in Metal Cutting, Ph. D. Thesis, Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA, 1996.
9.
Chryssolouris, G., and Domoroese, M., “Some Aspects of Acoustic Emission Modeling for Machining Control,” Proceedings of 17th North American Manufacturing Research Conference, 228–234, 1989.
10.
Daubechies
I.
Orthogonal Bases of Compactly Supported Wavelets
,” Communications on Pure and Applied Mathematics
, Vol. 41
, pp. 909
–996
, 1988
.11.
Kamarthi, S. V., On-line Flank Wear Estimation in Turning Using Multisensor Fusion and Neural Networks, Ph.D. Thesis, Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA, 1994.
12.
Kamarthi, S. V., Kumara, S. R. T., and Cohen, P. H., “Wavelet Representation of Acoustic Emission in Turning Process,” Proceedings of the Artificial Neural Networks in Engineering (ANNIE ’95), St. Louis, Mo, November 1995.
13.
Kannatey-Asibu
E.
Dornfeld
D. A.
Quantitative Relationships for Acoustic Emission from Orthogonal Metal Cutting
,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY
, Vol. 103
(3
), pp. 330
–340
, 1981
.14.
Kannatey-Asibu
E.
Emel
E.
Linear Discriminant Function Analysis of Acoustic Emission Signals for Cutting Tool Monitoring
,” Mechanical Systems and Signal Processing
, Vol. 32
(2
), pp. 469
–473
, 1988
.15.
Lan
M. S.
Dornfeld
D. A.
Acoustic Emission and Machining—Process Analysis and Control
,” Advanced Manufacturing Processes
, Vol. 1
(1
), pp. 1
–21
, 1986
.16.
Liang
S. Y.
Dornfeld
D. A.
Tool Wear Detection Using Time Series Analysis of Acoustic Emission
,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY
, Vol. 111
, pp. 199
–205
, 1989
.17.
Liu
J. J.
Dornfeld
D. A.
Modeling and Analysis of Acoustic Emission in Diamond Turning
,” ASME JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING
, Vol. 118
, pp. 199
–207
, 1996
.18.
Oppenheim, A. V., and Schafer, R. W., Discrete-time Signal Processing, Prentice-Hall, Englewood Cliffs, NJ, 1989.
19.
Rangwala
S.
Dornfeld
D. A.
Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring
,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY
, Vol. 123
(3
), pp. 219
–228
, 1990
.20.
Rangwala
S.
Dornfeld
D. A.
A Study of Acoustic Emission Generated During Orthogonal Metal Cutting, Part I: Energy Analysis
,” International Journal of Mechanical Sciences
, Vol. 33
, pp. 471
–487
, 1991
.21.
Stark, H., and Wood, J., Probability, Random Processes and Estimation Theory, Prentice-Hall, Englewood Cliffs, NJ, 1994.
22.
Tsonis, A., Chaos, From Theory to Applications, Plenum, New York, 1992.
23.
Wickerhauser, M. V., Lectures on Wavelet Packet Algorithms, Lecture Notes, Department of Mathematics, Washington University, St. Louis, MO, 1991.
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