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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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Sijbers, Jan
University of Antwerp
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (4/4 displayed)
- 2022Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturingcitations
- 2019Fiber assignment by continuous tracking for parametric fiber reinforced polymer reconstructioncitations
- 2019Simulated grating-based x-ray phase contrast images of CFRP-like objects
- 2018Parametric reconstruction of glass fiber-reinforced polymer composites from X-ray projection data—A simulation studycitations
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document
Fiber assignment by continuous tracking for parametric fiber reinforced polymer reconstruction
Abstract
In this work, we propose an extension of the recently presented Parametric Reconstruction (PARE) algorithm(1) towards the direct reconstruction of straight and curved fibers in glass fiber-refinforced polymer (GFRP) samples. The fibers are traced based on the Fiber Assignment by Continuous Tracking by introducing a piece-wise linear model. We show how the algorithm can estimate fiber parameters from the X-ray projection data and give an outlook on its application in our existing fiber estimation framework.