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Proceedings Paper

Pattern recognition of internal structural defects in industrial radiographic testing based on neural network
Author(s): Ming Ming; Zheng Li
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Paper Abstract

It is shown that an artificial neural network can be used to classify internal structural defects in radiographic nondestructive testing. We design a series of images presenting phantoms to simulate three different classes of defects: porosity, crack, and slag. Features of these defects are selected from domains of geometry, gray statistics, frequency spectrum, and etc. Some of them are especially suitable for pattern recognition in the case of radiographic image. A three-layered neural network trained with back-propagation rule is developed to carry out the classification. The training and testing data for the net are the features extracted from digitized radiographic images. Results are presented with satisfactory recognition rate.

Paper Details

Date Published: 20 September 2001
PDF: 6 pages
Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); doi: 10.1117/12.441683
Show Author Affiliations
Ming Ming, Tsinghua Univ. (United States)
Zheng Li, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 4555:
Neural Network and Distributed Processing
Xubang Shen; Jianguo Liu, Editor(s)

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