Locating and Classifying Defects with Artificial Neural Networks

Abstract:

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Locating defects and classifying them by their size was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). Postulated void of three different sizes (1x1 mm, 2x2 mm and 2x1 mm) were introduced in a bar with and without a notch. The size of a defect and its localization in a bar change its natural frequencies. Accordingly, synthetic data was generated with the finite element method. A parametric analysis was carried out. Only one defect was taken into account and the first five natural frequencies were calculated. 495 cases were evaluated. All the input data was classified in three groups. Each one has 165 cases and corresponds to one of the three defects mentioned above. 395 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. This procedure was followed in the cases of the plain bar and a bar with a notch. In the next stage of this work, the ANN output was optimized with ANFIS. The accuracy of the localization and classifications of the defects was improved.

Info:

Periodical:

Edited by:

J.M. Dulieu-Barton, J.D. Lord and R.J. Greene

Pages:

117-123

DOI:

10.4028/www.scientific.net/AMM.13-14.117

Citation:

A. Luna Avilés et al., "Locating and Classifying Defects with Artificial Neural Networks", Applied Mechanics and Materials, Vols. 13-14, pp. 117-123, 2008

Online since:

July 2008

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