Browse by author
Lookup NU author(s): Professor Gui Yun TianORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
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.
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: Applied Mechanics and Materials: e-Engineering & Digital Enterprise Technology
Year of Conference: 2008
Publisher: Trans Tech Publications Ltd.
Library holdings: Search Newcastle University Library for this item