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TECHNICAL PAPERS

Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals

[+] Author and Article Information
S. V. Kamarthi

Department of Mechanical, Industrial, & Manufacturing Engineering, Northeastern University, 334 Snell Engineering Center, Boston, MA 02115

S. R. T. Kumara

P. H. Cohen

Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, 207 Hammond, University Park, PA 16802

J. Manuf. Sci. Eng 122(1), 12-19 (Oct 01, 1997) (8 pages) doi:10.1115/1.538886 History: Received April 01, 1996; Revised October 01, 1997
Copyright © 2000 by ASME
Topics: Wear , Signals , Wavelets
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References

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Dan,  L., and Mathew,  J., 1990, “Tool Wear and Failure Monitoring Techniques for Turning—A Review,” Int. J. Machine Tools Manufacture, 30, No. 4, pp. 579–598.
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Kamarthi, S. V., Kumara, S. R. T., and Cohen, P. H., 1995, “Wavelet Representation of Acoustic Emission in Turning Process,” Intelligent Engineering Systems Through Artificial Neural Networks, Dagli, C. H., Akay, M., Chen, C. L. P., Fernandez, B. R., and Ghosh, J., eds., Vol. 5, ASME Press, New York, pp. 861–866.
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Figures

Grahic Jump Location
A typical record of AE signal
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Decomposition of a0 into sequences of wavelet coefficients
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Recurrent neural network architecture
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Flank wear estimation results over tool life of three sample tools in Set 2 experiments for (a) wavelet representation and (b) Fourier representation
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Flank wear estimation results over tool life of a sample tool in Set 3 experiments: (a) wavelet representation and (b) Fourier representation

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