Observational analysis demonstrates improved defect classification in materials using artificial neural networks, indicating advanced techniques in nondestructive testing.
An artificial neural network is applied by nondestructive inspections in aerospace materials. The use of an artificial neural network is presented for classifying testing data as corresponding to sample materials with defect and without defect. The back-propagation learning for a multi-layer feed-forward neural network is applied to this classification. The trust region method is adopted to the back-propagation learning problem. Results of numerical tests are summarized.
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Fumio Kojima (1993) studied this question.
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