| 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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Meyer, Nils
University of Augsburg
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (24/24 displayed)
- 2024Anisotropic warpage prediction of injection molded parts with phenolic matrix
- 2024Initial stack placement strategies for carbon fiber- reinforced sheet molding compound (C-SMC)
- 2024Inverse computation of local fiber orientation using digital image correlation and differentiable finite element computations
- 2022Experimental and Numerical Analysis of SMC Compression Molding in Confined Regions : A Comparison of Simulation Approaches
- 2022Probabilistic virtual process chain for process-induced uncertainties in fiber-reinforced composites
- 2022Generation of Initial Fiber Orientation States for Long Fiber Reinforced Thermoplastic Compression Molding Simulation
- 2022Non-isothermal direct bundle simulation of SMC compression molding with a non-Newtonian compressible matrixcitations
- 2022A Benchmark for Fluid-Structure Interaction in Hybrid Manufacturing: Coupled Eulerian-Lagrangian Simulation
- 2022Manufacturing Simulation of Sheet Molding Compound (SMC)
- 2022Mesoscale simulation of the mold filling process of Sheet Molding Compound
- 2022Experimental and Numerical Analysis of SMC Compression Molding in Confined Regions—A Comparison of Simulation Approachescitations
- 2021A sequential approach for simulation of thermoforming and squeeze flow of glass mat thermoplasticscitations
- 2021A Benchmark for Fluid-Structure Interaction in Hybrid Manufacturing: Coupled Eulerian-Lagrangian Simulation
- 2021Manufacturing Simulation of Sheet Molding Compound (SMC)
- 2021Modeling Short-Range Interactions in Concentrated Newtonian Fiber Bundle Suspensionscitations
- 2021Mesoscale simulation of the mold filling process of Sheet Molding Compound
- 2021How to combine plastics and light metals for forming processes and the influence of moisture content on forming behavior
- 2020Motivating the development of a virtual process chain for sheet molding compound compositescitations
- 2020Parameter Identification of Fiber Orientation Models Based on Direct Fiber Simulation with Smoothed Particle Hydrodynamics
- 2019Virtual process chain of sheet molding compound: Development, validation and perspectivescitations
- 2019Motivating the development of a virtual process chain for sheet molding compound compositescitations
- 2019Process Simulation of Sheet Molding Compound (SMC) using Direct Bundle Simulation
- 2019A revisit of Jeffery‘s equation - modelling fiber suspensions with Smoothed Particle Hydrodynamics
- 2018A revisit of Jeffery‘s equation - modelling fiber suspensions with Smoothed Particle Hydrodynamics
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document
Generation of Initial Fiber Orientation States for Long Fiber Reinforced Thermoplastic Compression Molding Simulation
Abstract
The prediction of the fiber orientation state (FOS) is of utmost interest for compression molded long fiber reinforced thermoplastics as the part's properties strongly depend on it. Besides the position of the initial plastificate in the mold cavity and the process settings, detailed knowledge of the initial FOS is essential. During compounding, the fibers align depending on the extruder screw configuration yielding a non-uniform local FOS. For process simulation, a common approach is to neglect this effect and assume an isotropic or planar-isotropic FOS of the initial plastificate. A more sophisticated approach consists of micro-computed tomography (µCT-) scans of slices of the initial plastificate and the derivation of the initial FOS from the three-dimensional image data. This approach can yield accurate predictions but is quite cumbersome and expensive. In this paper, we present a novel approach to account for the FOS of the initial plastificate. The approach is motivated by experimental observations and based on geometric assumptions. Depending on the extruder type and the dimensions of the initial plastificate, the developed tool generates a three-dimensional data set containing the mesh information alongside the initial FOS in a tensorial representation. To investigate the influence of the initial FOS for different flow regimes, we conducted compression molding simulations on a planar part.