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Ultrasonic Detection of the Metallurgical Defects in the Steel and its Evaluation by Neural Network Based on the Wavelet Transform Noise Suppression

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Periodical:

Key Engineering Materials (Volumes 270-273)

Pages:

160-167

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.270-273.160

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Online since:

August 2004

Authors:

Guo Xing Zhang, Hui Jun Hu, Dean Ta

Keywords:

Flaws, Inclusion, Neural Network (NN), Ultrasonic, Wavelet Transform (WT)

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© 2004 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] Agostino Abbate, Jeff Koay, Julius Frankel, Stephan C. Schroeder, and Pankaj Das, Singnal Detection and Noise Suppression Using a Wavelet Transform Signal Processor: Application to Ultrasonic Flaw Detection, IEEE Vol. 44, No. 1, pp.14-25, JANUARY (1997).

DOI: 10.1109/58.585186

Google Scholar

[2] Guangming Zhang, Shuyi Zhang, Yuwen Wang, Application of adaptive time-frequency decomposition in ultrasonic NDE of highly-scattering materials, Ultrasonics, Vol. 38, pp.961-964, (2000).

DOI: 10.1016/s0041-624x(00)00036-6

Google Scholar

[3] F.W. Margrave, K. Rigas, D.A. Bradley, P. Barrowcliffe, The use of neural networks in ultrasonic flaw detection, Measurement, Vol. 25, pp.143-154, (1999).

DOI: 10.1016/s0263-2241(98)00075-x

Google Scholar

[4] A.R. Baker. C.G. Windsor, The classification of defects from ultrasonic data using neural networks: the Hopfield method, NDT International, Vol. 22, pp.97-105, (1989).

DOI: 10.1016/0308-9126(89)90862-6

Google Scholar

[5] J. B. Santos, F. Perdigão, Automatic defects classification  a contribution, NDT&E International Vol. 34, pp.313-318, (2001).

DOI: 10.1016/s0963-8695(00)00043-8

Google Scholar

[6] L. Angrisani, L. Bechou, D. Dallet, P. Daponte, Y. Ousten, Detection and location of defects in electronic devices by means of scanning ultrasonic microscopy and the wavelet transform, Measurement, Vol. 31, pp.77-91.

DOI: 10.1016/s0263-2241(01)00032-x

Google Scholar

[7] S. Legendre, J. Goyette, D. Masicotte, Ultrasonic NDE of composite material structures using wavelet coefficients, NDT&E International, Vol. 34, pp.31-37, (2001).

DOI: 10.1016/s0963-8695(00)00029-3

Google Scholar

[8] J. Chen, Y. Shi, S. Shi, Noise analysis of digital ultrasonic nondestructive evaluation system, International Journal of Pressure Vessels and Piping, Vol. 76, pp.619-630, (1999).

DOI: 10.1016/s0308-0161(99)00052-6

Google Scholar

[9] Y.J. Chen, Y.W. Shi, X.P. Zhang, Detection of weak bonding in friction welds by ultrasound, Ultrasonics, Vol. 36, pp.141-146, (1998).

DOI: 10.1016/s0041-624x(97)00143-1

Google Scholar

[10] Xiaoyan Tang, Udantha R. Abeyratne, Wavelet transforms in estimating scatterer spacing from ultrasound echoes, Ultrasonics, Vol. 38, pp.688-692, (2000).

DOI: 10.1016/s0041-624x(99)00150-x

Google Scholar

[11] Gregorio Andria, Filippo Attivissimo, Nicola Giaquinto, Digital signal processing techniques for accurate ultrasonic sensor measurement, Measurement, Vol. 30, pp.105-114, (2001).

DOI: 10.1016/s0263-2241(00)00059-2

Google Scholar

[12] L. Bechou, L. Angrisiani, Y. Ousten, D. Dallet, H. Levi, P. Daponte, Y. Danto, Localizationof defects in die-attach assembly by continuous wavelet transform using scanning acoustic microscopy, Meroelectronics Reliability, Vol. 39, pp.1095-1101, (1999).

DOI: 10.1016/s0026-2714(99)00155-9

Google Scholar

[13] V.S. Moholkar, M. Huitema, S. Rekveld, M. M. C. G. Warmoeskerken, Characterization of an ultrasonic system using wavelet transforms, Chemical Engineering Science, Vol. 57, pp.617-629, (2002).

DOI: 10.1016/s0009-2509(01)00397-9

Google Scholar

[14] M. Peterson, A method for increased accuracy of the measurement of relative phase velocity, Ultrasonics, Vol. 35, pp.17-29, (1997).

DOI: 10.1016/s0041-624x(96)00095-9

Google Scholar

[15] L. Angrisani, P. Daponte, M. D ' Apuzzo, A method for the automatic detection and measurement of transients. Part II: applications, Measurement, Vol. 25, pp.31-40, (1999).

DOI: 10.1016/s0263-2241(98)00064-5

Google Scholar

[16] Balqies Sadoun, An efficient simulation scheme for testing materials in a nondestructive manner, Information Sciences, Vol. 137, pp.43-51, (2001).

DOI: 10.1016/s0020-0255(01)00113-x

Google Scholar