| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Mechbal, Nazih
Processes and Engineering in Mechanics and Materials
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (17/17 displayed)
- 2023Hybrid twin of RTM process at the scarce data limitcitations
- 2022Design and control of a new electrostrictive polymer based continuum actuator for endoscopic robotcitations
- 2022Design and control of a new electrostrictive polymer based continuum actuator for endoscopic robot ; JIMSScitations
- 2022Prediction of frequency and spatially dependent attenuation of guided waves propagating in mounted and unmounted A380 parts made up of anisotropic viscoelastic composite laminatescitations
- 2022Experimental Damage Localization and Quantification with a Numerically Trained Convolutional Neural Network
- 2020Extrapolation of AR models using cubic splines for damage progression evaluation in composite structurescitations
- 2019Data-Driven Autoregressive Model Identification for Structural Health Monitoring in an Anisotropic Composite Plate.
- 2019Data-Driven Autoregressive Model Identification for Structural Health Monitoring in an Anisotropic Composite Plate.
- 2019Piezoelectric transducer for low frequency sound generation on surface loudspeakers
- 2019Investigation of nonlinear Lamb wave/damage interaction: numerical and experimental approaches
- 2018Optimal dual-PZT sizing and network design for baseline-free SHM of complex anisotropic composite structurescitations
- 2018LASER shock delamination generation and machine learning-based damage quantification in CFRP composites plates
- 2017Generation of controlled delaminations in composites using symmetrical laser shock configurationcitations
- 2017Estimation of the temperature field on a composite fan cowl using the static capacity of surface-mounted piezoceramic transducers
- 2017Estimation of the temperature field on a composite fan cowl using the static capacity of surface-mounted piezoceramic transducerscitations
- 2015Perturbation Analysis for Robust Damage Detection with Application to Multifunctional Aircraft Structures
- 2010Active Damage Detection and Localization Applied to Composite Structure Using Piezoceramic Patches
Places of action
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document
LASER shock delamination generation and machine learning-based damage quantification in CFRP composites plates
Abstract
In the aeronautic industry, composite materials are becoming more widespread due to their high strength to mass ratio. Piezoelectric elements can be permanently incorporated on composite parts during the manufacturing process and can then be used to provide a diagnosis of their current health and the prognosis of their remaining operational life. This approach is called Structural Health Monitoring (SHM). In this work, we approach delamination quantification in Carbon Fiber Reinforced Polymer (CFRP) plates as a classification problem whereby each class corresponds to a certain damage extent. Starting from the assumption that damage causes a structure to exhibit nonlinear response, we investigate whether the use of Nonlinear Model Based Features (NMBF) increases classification performance. NMBF are computed based on parallel Hammerstein models which are identified with an Exponential Sine Sweep (ESS) signal. Delamination damage is introduced into samples in a calibrated and realistic way using LASER Shock Wave Technique (LSWT) and more particularly symmetrical LASER shock configuration. Obtained results demonstrate that the proposed approach is very reliable for delamination quantification.