Crack-depth determination by a neural network with a synthetic training data set
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Begun in 1973, the Review of Progress in Quantitative Nondestructive Evaluation (QNDE) is the premier international NDE meeting designed to provide an interface between research and early engineering through the presentation of current ideas and results focused on facilitating a rapid transfer to engineering development.
This site provides free, public access to papers presented at the annual QNDE conference between 1983 and 1999, and abstracts for papers presented at the conference since 2001.
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Abstract
A neural network with an analog output is presented for crack-depth estimation from ultrasonic signals backscattered from a surface-breaking crack in a steel plate. The network has only one response unit and this unit directly reports the crack depth from the measured signals. A completely synthetic data set, spot-checked by comparison with experimental results, is utilized for the training of the network. The synthetic data set has been obtained by solving governing boundary integral equations by the boundary element method. A Gaussian modulated sinusoid has been utilized as incident signal. The architecture of the present network, which is a feedforward three-layered network together with an error back- propagation algorithm, has been discussed in Refs. [1,2].