LAUSR

Abstract This paper proposes a Lamb wave based technique for detecting multiple disbonds using novel damage features based on mode conversion and nonlinear breathing phenomena. An aluminum scarf joint with a top cap has been considered as the example structure. Generation and sensing of Lamb waves have been done through piezoelectric patches. Three possible cases of disbonds, i.e., horizontal disbond between the top cap and the scarf joint, inclined disbond at the scarf joint, and the simultaneous presence of both the horizontal and inclined disbonds have been considered. Numerical simulations and experiments have been run considering disbonds of different lengths at different locations. For extraction of damage features, the wave propagation response has been decomposed into symmetric and antisymmetric components. Next, damage features have been defined using the amplitudes of the superharmonics present in each of these components. Detection of disbond has been done in two steps – localization and quantification. The problem of disbond localization has been formulated as a classification problem and solved employing a Probabilistic Neural Network (PNN) using damage features extracted from numerical simulation data. Quantificationof disbond length has been done through Artificial Neural Networks (ANN) using the same damage features. Finally, this technique has been tested using experimental data.