Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26735
Title: Artificial Neural Network (ANN)-based crack identification in aluminum plates with lamb wave signals
Authors: Lu, Y
Ye, L
Su, Z 
Zhou, L 
Cheng, L 
Keywords: Artificial neural network
Damage detection
Digital damage fingerprints.
Lamb waves
Issue Date: 2009
Source: Journal of intelligent material systems and structures, 2009, v. 20, no. 1, p. 39-49 How to cite?
Journal: Journal of Intelligent Material Systems and Structures 
Abstract: An inverse analysis based on the artificial neural network technique is introduced for effective identification of crack damage in aluminum plates. The concepts of digital damage fingerprints and damage parameter database, which are prerequisites for neural network developing and training, are presented. Parameterized modeling for finite element analysis and an information mapping approach are applied to constitute the damage parameter database cost-effectively. The generalization performance of the neural network is examined by a process of 'leave-one-out' cross-validation and diverse factors are discussed, based on which the optimization of the neural network architecture is evaluated. The capability of this inverse approach is assessed by two crack cases from experiments, with good accuracy obtained in damage parameters (central position, size, and orientation).
URI: http://hdl.handle.net/10397/26735
ISSN: 1045-389X
DOI: 10.1177/1045389X07088782
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