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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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Mahapatra, Sarthak
University of Bristol
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (5/5 displayed)
- 2023A comprehensive modelling framework for defect prediction in automated fibre placement of composites
- 2023Modelling the Effect of Process Conditions on Steering-Induced Defects in Automated Fibre Placement (AFP)citations
- 2022Understanding tack behaviour during prepreg-based composites’ processingcitations
- 2021Modelling compaction behavior of toughened prepreg during automated fibre placement
- 2017Void modelling and virtual testing of prepreg materials from 3D image capture
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article
Understanding tack behaviour during prepreg-based composites’ processing
Abstract
Prepreg tack is a critical material property that plays a key role in Automated Fibre Placement (AFP) manufacture-induced defect generation. However, there is a gap in current state-of-the-art manufacturing process modelling that includes tack phenomena and experimental data that shows the influence of multiple factors. In this paper, a modified probe test method, which is capable of inter-ply and ply-tool tack measurement, is proposed. The influence of multiple factors, consistent with the AFP deposition process (i.e. pull-off rate, contact time, pressure and temperature) on tack is studied. Further, a comprehensive modelling framework accounting for all the factors is developed and implemented as a user subroutine for a commercial finite element (FE) package. The work highlights the complexity of tack behaviour at play and shows promise for filling the gap between the tack data/model scarcity and simulation of manufacturing processes such as AFP and forming.