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Defect Imaging on CFRP and Honeycomb Composite Structures by Guided Waves Generated and Detected by a Sparse PZT Array

ANDRII KULAKOVSKYI, BASTIEN CHAPUIS, OLIVIER MESNIL, NAS-REDINE BEDREDDINE, OSCAR D’ALMEIDA, ALAIN LHÉMERY

Abstract


Sandwich honeycomb structures (aluminum core bonded to Carbon Fiber Reinforced Polymer (CFRP) sheets on either side) are widely employed in the aerospace industry for their high strength to mass ratio. However, they might be subjected to damages such as delaminations of the composite sheets or debondings between the face sheets and the core due to impacts or thermo-mechanical aging. In order to reduce maintaining costs and extend the service time, Guided Waves (GW) based Structural Health Monitoring (SHM) systems are considered an adequate solution. Indeed, GW propagate over long distances and exhibit low attenuation, thus allowing to monitor wide areas with a limited number of sensors. Defect imaging on CFRP composites and honeycomb composite structures using both Delay-And-Sum [1] and correlation-based algorithm Excitelet [2] is presented in this communication. A machine learning algorithm is finally implemented in order to automatically identify and isolate defects on a given cartography map. The machine learning algorithm is trained on an experimental database of false and positive results obtained on representative specimens.

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