Paper Title:
Locating Defects Using Dynamic Strain Analysis and Artificial Neural Networks
  Abstract

An inverse artificial neural network (ANN) assessment for locating defects in bars with or without notches is presented in the paper. Postulated void defects of 1mm x 1mm were introduced into bars that were impacted with an impulse step load; the resultant elastic waves propagate impinging on the defects. The resultant transient strain field was analyzed using the finite element method. Transient strain data was collected at nodal points or sensors locations on the boundary of the bars and used to train and assess ANNs. The paper demonstrates quantitatively, the effects of features such as the design of ANN, sensing parameters such as number of data collection points, and the effect of geometric features such as notches in the bars.

  Info
Periodical
Edited by
J.M. Dulieu-Barton and S. Quinn
Pages
325-330
DOI
10.4028/www.scientific.net/AMM.3-4.325
Citation
L. H. Hernández-Gómez, J.F. Durodola, N.A. Fellows, G. Urriolagoitia-Calderón, "Locating Defects Using Dynamic Strain Analysis and Artificial Neural Networks", Applied Mechanics and Materials, Vols. 3-4, pp. 325-330, 2005
Online since
August 2006
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