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Artificial Neural Network (ANN)-based Crack Identification in Aluminum Plates with Lamb Wave Signals

¹ Laboratory of Smart Materials and Structures (LSMS), Centre for Advanced Materials Technology (CAMT) School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, NSW 2006, Australia, Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
² Laboratory of Smart Materials and Structures (LSMS), Centre for Advanced Materials Technology (CAMT) School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, NSW 2006, Australia
³ Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China

^* To whom correspondence should be addressed.

	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).

Key Words: Lamb waves, artificial neural network, damage detection, digital damage fingerprints.

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