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Defect identification and classification for digital X-ray images

Lookup NU author(s): Ying Yin, Professor Gui Yun TianORCiD


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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.

Publication metadata

Author(s): Yin Y, Tian GY, Yin GF, Luo AM

Editor(s): Cheng, K; Yao, Y; Zhou, L

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: E-Engineering and Digital Enterprise Technology: Selected, Peer Reviewed Papers from the 6th International Conference on E-engineering and Digital Enterprise Technology

Year of Conference: 2008

Pages: 543-547

ISSN: 1660-9336

Publisher: Trans Tech Publications

Library holdings: Search Newcastle University Library for this item

Series Title: Applied Mechanics and Materials

ISBN: 9780878494705