Microstructural characterization of materials by neural network technique

Barat, P. ; Chatterjee, A. ; Mukherjee, P. ; Gayathri, N. ; Jayakumar, T. ; Baldev Raj, (2010) Microstructural characterization of materials by neural network technique Physics Letters A, 375 (1). pp. 6-10. ISSN 0375-9601

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Official URL: http://linkinghub.elsevier.com/retrieve/pii/S03759...

Related URL: http://dx.doi.org/10.1016/j.physleta.2010.10.023


Ultrasonic signals received by pulse echo technique from plane parallel Zircaloy 2 samples of fixed thickness and of three different microstructures, were subjected to signal analysis, as conventional parameters like velocity and attenuation could not reliably discriminate them. The signals, obtained from these samples, were first sampled and digitized. Modified Karhunen Loeve Transform was used to reduce their dimensionality. A multilayered feed forward Artificial Neural Network was trained using a few signals in their reduced domain from the three different microstructures. The rest of the signals from the three samples with different microstructures were classified satisfactorily using this network.

Item Type:Article
Source:Copyright of this article belongs to Elsevier Science.
Keywords:Neural Network; Ultrasonic Signals; Karhunen Loeve Transformation; Zircaloy 2
ID Code:40283
Deposited On:23 May 2011 11:15
Last Modified:23 May 2011 11:15

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