| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Mesnil, Olivier
Luxembourg Institute of Science and Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (18/18 displayed)
- 2024A Hybrid Actuator Model for Efficient Guided Wave-Based Structural Health Monitoring Simulations
- 2023Self-referenced robust guided wave based defect detection: application to woven composite parts of complex shapecitations
- 2023Lead Zirconate Titanate Transducers Embedded in Composite Laminates: The Influence of the Integration Method on Ultrasound Transduction ; Transducteur PZT intégré dans un composite stratifié : influence de la méthode d'intégration sur la transduction ultrasonorecitations
- 2023Lead Zirconate Titanate Transducers Embedded in Composite Laminates: The Influence of the Integration Method on Ultrasound Transductioncitations
- 2023Detection of barely visible impact damage in composite plates using non-linear pump-probe technique
- 2023Experimental and Numerical Study of Lamb Waves Generation Efficiency by Lead Zirconate Titanate Transducers Embedded in a Composite Laminate
- 2022Optimization of a Structural Health Monitoring systems integration in laminated composite cured in autoclavecitations
- 2022Experimental and Numerical Study of Lamb Waves Generation Efficiency by Lead Zirconate Titanate Transducers Embedded in a Composite Laminate
- 2021Damage quantification in an aluminium-CFRP composite structure using guided wave wavenumber mapping : Comparison of instantaneous and local wavenumber analyses
- 2021Characterization of Guided Wave Propagation in Woven Composites of Varying Geometry
- 2021Experimental validation of transient spectral finite element simulation tools dedicated to guided wave based structural health monitoringcitations
- 2019Machine-learning based temperature compensation for Guided Wave Imaging in Structural Health Monitoring
- 2019Defect sizing using convolution neural network applied to guided wave imagingcitations
- 2019Guided wave imaging of a composite plate using passive acquisitons by Fiber Bragg Gratings on optical fibers
- 2018Defect imaging in layered composite plates and honeycomb sandwich structures using sparse piezoelectric transducers networkcitations
- 2018Experimental determination of 3D Green's function in composite plates for defect imaging using guided waves
- 2017Defect imaging on CFRP and honeycomb composite structures by guided waves generated and detected by a sparse PZT arraycitations
- 2016Sparse wavefield reconstruction and source detection using Compressed Sensingwavefield reconstruction and source detection using Compressed Sensingcitations
Places of action
| Organizations | Location | People |
|---|
conferencepaper
Defect imaging on CFRP and honeycomb composite structures by guided waves generated and detected by a sparse PZT array
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.