Paper Title:
Defect Identification and Classification for Digital X-Ray Images
  Abstract

Radiography inspection (X-ray or gamma ray) is one of the most commonly used Non-destructive Evaluation (NDE) methods. More and more digital X-ray imaging is used for medical diagnosis, security screening, or industrial inspection, which is important for e-manufacturing. In this paper, we firstly introduced an automatic welding defect inspection system for X-ray image evaluation, defect image database and applications of Artificial Neural Networks (ANNs) for NDE. Then, feature extraction and selection methods are used for defect representation. Seven categories of geometric features were defined and selected to represent characteristics of different kinds of welding defect. Finally, a feed-forward backpropagation neural network is implemented for the purpose of defect classification. The performance of the proposed methods are tested and discussed.

  Info
Periodical
Edited by
Kai Cheng, Yingxue Yao and Liang Zhou
Pages
543-547
DOI
10.4028/www.scientific.net/AMM.10-12.543
Citation
Y. Yin, G.Y. Tian, G. F. Yin, A.M. Luo, "Defect Identification and Classification for Digital X-Ray Images", Applied Mechanics and Materials, Vols. 10-12, pp. 543-547, 2008
Online since
December 2007
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$32.00
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