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Abstract

Targeted at an online health monitoring technique for in-service composite structures, a Lamb wave propagation-based deterioration assessment approach is developed using an artificial neural network (ANN) algorithm and a PZT transducer network. Structural dynamic responses are numerically simulated using three-dimensional FEM analyses, and signal characteristics are then extracted with a Signal Processing and Interpretation Package (SPIP) in terms of the wavelet transform technique. A damage parameters database (DPD) is constructed to accommodate the extracted wave spectrographic characteristics, and adopted for ANN training under the supervision of an error-backpropagation neural algorithm. The validity of this methodology is evaluated by identifying through-hole-type damages in [45/45/0/90]s quasi-isotropic CF/EP (T650/F584) laminates. The results exhibit excellent quantitative prediction for damage in the CF/EP composites, including position, geometric identity, and orientation. Additionally, the dependence of ANN performance on inherent network configurations is also evaluated.

Keywords

structural health monitoring artificial neural network signal processing wavelet transform CF/EP composites

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Quantitative Damage Prediction for Composite Laminates Based on Wave Propagation and Artificial Neural Networks

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