| 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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Zarouchas, Dimitrios
Delft University of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (30/30 displayed)
- 2024Innovative welding integration of acousto-ultrasonic composite transducers onto thermoplastic composite structurescitations
- 2023Intelligent Health Indicators Based on Semi-supervised Learning Utilizing Acoustic Emission Datacitations
- 2023Non-destructive strength prediction of composite laminates utilizing deep learning and the stochastic finite element methodscitations
- 2023Acousto-ultrasonic composite transducers integration into thermoplastic composite structures via ultrasonic welding
- 2023Hierarchical Upscaling of Data-Driven Damage Diagnostics for Stiffened Composite Aircraft Structures
- 2023Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM datacitations
- 2023An SHM Data-Driven Methodology for the Remaining Useful Life Prognosis of Aeronautical Subcomponentscitations
- 2023A novel strain-based health indicator for the remaining useful life estimation of degrading composite structurescitations
- 2023Developing health indicators for composite structures based on a two-stage semi-supervised machine learning model using acoustic emission datacitations
- 2023Analysis of Stochastic Matrix Crack Evolution in CFRP Cross-Ply Laminates under Fatigue Loadingcitations
- 2023Delamination Size Prediction for Compressive Fatigue Loaded Composite Structures Via Ultrasonic Guided Wave Based Structural Health Monitoring
- 2022On the Challenges of Upscaling Damage Monitoring Methodologies for Stiffened Composite Aircraft Panelscitations
- 2022Synthesis and characterization of novel eco-epoxy adhesives based on the modified tannic acid for self-healing jointscitations
- 2022Synthesis and characterization of novel eco-epoxy adhesives based on the modified tannic acid for self-healing jointscitations
- 2022Assessing stiffness degradation of stiffened composite panels in post-buckling compression-compression fatigue using guided wavescitations
- 2022Early fatigue damage accumulation of CFRP Cross-Ply laminates considering size and stress level effectscitations
- 2021A Strain-Based Health Indicator for the SHM of Skin-to-Stringer Disbond Growth of Composite Stiffened Panels in Fatiguecitations
- 2021Health monitoring of aerospace structures utilizing novel health indicators extracted from complex strain and acoustic emission datacitations
- 2021A review of experimental and theoretical fracture characterization of bi-material bonded jointscitations
- 2021Fusion-based damage diagnostics for stiffened composite panelscitations
- 2021Health indicators for diagnostics and prognostics of composite aerospace structurescitations
- 2021Damage assessment of a titanium skin adhesively bonded to carbon fiber–reinforced plastic omega stringers using acoustic emissioncitations
- 2020Damage assessment of NCF, 2D and 3D Woven Composites under Compression After Multiple-Impact using Acoustic Emissioncitations
- 2020The effect of temperature on fatigue strength of poly(ether-imide)/multiwalled carbon nanotube/carbon fibers composites for aeronautical applicationcitations
- 2019Compression After Multiple Low Velocity Impacts of NCF, 2D and 3D Woven Compositescitations
- 2019Physics of delamination onset in unidirectional composite laminates under mixed-mode I/II loadingcitations
- 2019Damage characterization of adhesively-bonded Bi-material joints using acoustic emissioncitations
- 2010Numerical failure analysis of composite structures
- 2009Study of the mechanical response of carbon Reinforced concrete beams using Non Destructive Techniques during a four-point bending test
- 2009Study of the crack propagation in carbon reinforced concrete beams during a four-point bending test
Places of action
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document
Delamination Size Prediction for Compressive Fatigue Loaded Composite Structures Via Ultrasonic Guided Wave Based Structural Health Monitoring
Abstract
Under in-plane compressive load conditions, the growth of a delamination initially induced by an impact can be followed by a fast growth after a threshold level, which leads to a catastrophic failure in composite structures. To avoid reaching this critical level, it is essential to uncover the delamination size and growth pattern in real time. Ultrasonic Guided Waves (UGW) have a strong capability to interrogate and monitor the structure in real-time and thus track the growth of damage, which may occur during the flight cycles. Although various types of damage affect the monitored UGW signals, it is challenging to determine from the UGW signals what types of damage and at what rate of growth are occurring within the structure. UGW signals can be acquired at defined intervals and then analysed to possibly detect different types of damages, such as delamination, and to quantify the rate of damage growth over fatigue cycles. However, correlating the UGW-based Damage Indicators (DIs) with the specific type of damage, such as delamination, and damage growth is a challenging task as the relation between these DIs and the actual damage state is very complex. Therefore, in this study, a supervised Deep Neural Network-based (DNN) prediction model is proposed aiming to diagnose the delamination size of the composite structure by correlating the UGW-based DIs with the quantified time-varying delamination size. UGW data is collected through a network of permanently installed piezoelectric transducers (PZTs). The delamination size is obtained through ultrasonic C-Scan technique at defined cycles. DIs are extracted in time, frequency, and time-frequency domains and used as the input for the DNN-based regression model. Each sensor-actuator path is considered as an independent set of indicators, which are separated for training, validation, and testing purposes. The effect of the different paths on the delamination size prediction is presented along with the model performance on measured delamination growth in woven type composite sample.