| ABSTRACT: | ARTMAP NETWORK AND WAVELET ANALYSIS FOR FLAWS CHARACTERIZATION
L. Medina,1 H. Benítez-pérez,2 E. Moreno,3 L. Leija,4 G. González4.
1Universidad Nacional Autónoma De México, México, D. F., Mexico; 2Disca-iimas, Unam, Mexico, D. F.,
Mexico; 3Icimaf, La Habana, Cuba; 4Cinvestav, México, D. F., Mexico
Ultrasonic pulse-echo technique has been successfully used in a non-destructive testing of materials. This
method aims to characterize the propagation path and/or to determine the physical properties of reflectors in
terms of their location, size, orientation, and porosity. To performultrasonic non-destructive evaluation
(NDE), an ultrasonic pulsed wave is transmitted into the materials using a transmitting/receiving transducer.
The spectral analysis of the backscattered echoes has been widely used for flaws detection, frequency-shift
estimation, and dispersive echoes characterisation.
An innovative methodology is presented in this paper, in order to characterize flaws within the tested
material. A pattern recognition technique based on ARTMAP network and wavelet transform is used as a
digital processing tool that allows the geometry of flaws being determined. This technique consists of two
non-supervised neural networks named ART2 (Adaptive Resonance Theory) and the Mexican hat wavelet
transform.
An aluminium block with three man-made defects of circular geometries was scanned, by moving a single
ultrasonic transducer along two perpendicular paths, producing two reflectivity maps containing the flaws
information. The signal processing method proposed here used the received signals as follows: The total set
of signals was split into two subsets, depending on the scanning path. The 10% of each subset was used to
train an ART2 network, via the spectral information produced by the Mexican hat wavelet and the rest of
them to validate the system. The ART2 networks can build a map field, since flaws are present in both axes,
map field reproduced the flaws contour, based on the proper selection of vigilance and learning parameters.
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