| 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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Schneider, Matti
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (32/32 displayed)
- 2024Generation and analysis of digital twins for CoDiCoFRP accounting for fiber length and orientation distribution
- 2024Assumed strain methods in micromechanics, laminate composite voxels and level setscitations
- 2024Convergence of Damped Polarization Schemes for the FFT-Based Computational Homogenization of Inelastic Media With Pores
- 2024On the effectiveness of deep material networks for the multi-scale virtual characterization of short fiber-reinforced thermoplastics under highly nonlinear load casescitations
- 2024Generating microstructures of long fiber reinforced composites by the fused sequential addition and migration methodcitations
- 2023An orientation corrected shaking method for the microstructure generation of short fiber-reinforced composites with almost planar fiber orientationcitations
- 2023Accounting for weak interfaces in computing the effective crack energy of heterogeneous materials using the composite voxel technique
- 2023Homogenizing the viscosity of shear-thinning fiber suspensions with an FFT-based computational methodcitations
- 2023On fully symmetric implicit closure approximations for fiber orientation tensorscitations
- 2023Generation and analysis of digital twins for CoDiCoFRP accounting for fiber length and orientation distribution
- 2023On the Phase Space of Fourth-Order Fiber-Orientation Tensorscitations
- 2023Factors influencing the dynamic stiffness in short‐fiber reinforced polymers
- 2022On the impact of the mesostructure on the creep response of cellular NiAl-Mo eutecticscitations
- 2022Representative volume elements for matrix-inclusion composites - a computational study on periodizing the ensemblecitations
- 2022An algorithm for generating microstructures of fiber‐reinforced composites with long fibers
- 2022Probabilistic virtual process chain for process-induced uncertainties in fiber-reinforced composites
- 2022Multi-scale fatigue model to predict stiffness degradation in short-fiber reinforced composites
- 2022Solving phase-field models in the tensor train format to generate microstructures of bicontinuous compositescitations
- 2022A computational multiscale model for anisotropic failure of sheet molding compound composites
- 2022A sequential addition and migration method for generating microstructures of short fibers with prescribed length distribution
- 2022Accounting for viscoelastic effects in a multiscale fatigue model for the degradation of the dynamic stiffness of short-fiber reinforced thermoplastics
- 2022Identifying material parameters in crystal plasticity by Bayesian optimizationcitations
- 2021The sequential addition and migration method to generate representative volume elements for the homogenization of short fiber reinforced plasticscitations
- 2021An FE–DMN method for the multiscale analysis of short fiber reinforced plastic components
- 2021A multiscale high-cycle fatigue-damage model for the stiffness degradation of fiber-reinforced materials based on a mixed variational framework
- 2021Computing the effective crack energy of heterogeneous and anisotropic microstructures via anisotropic minimal surfaces
- 2021Identifying material parameters in crystal plasticity by Bayesian optimization
- 2021A computational multi-scale model for the stiffness degradation of short-fiber reinforced plastics subjected to fatigue loadingcitations
- 2020Computational homogenization of sheet molding compound composites based on high fidelity representative volume elementscitations
- 2019Material characterization and compression molding simulation of CF-SMC materials in a press rheometry testcitations
- 2017The sequential addition and migration method to generate representative volume elements for the homogenization of short fiber reinforced plasticscitations
- 2017Evaluating the Factors Influencing the Friction Behavior of Paperboard during the Deep Drawing Process
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article
Material characterization and compression molding simulation of CF-SMC materials in a press rheometry test
Abstract
S.467-472 ; The compression molding of sheet molding compounds (SMCs) is typically thought of as a fluid mechanics problem. The usage of CF-SMC with high fiber volume content (over 50%) and long fiber reinforcement structures (up to 50 mm) challenges the feasibility of this point of view. In this work a user-defined material model based on a solid mechanics formulation is developed in LS-DYNA®. The material model is built on a modular principle where the different influence factors caused by the material characteristics form building blocks. The idea is that these blocks are represented by simple mathematical models and interact in a way that forms the overall behavior of the SMC material. To analyze the behavior of the SMC material and create input parameters for the material model it is necessary to perform some kind of material characterization experiment. This paper presents the press rheometry test which can be perform in two variations, differing in terms of specimen size and shape and degree of coverage in the tool. Here the material response to the compression molding can be analyzed and by the visualization of the flow front development the anisotropy and homogeneity of the material can be assessed. For a comparison between the material model and reality the two variations of the press rheometry test are simulated. The simulation results show a good prediction of the experiments. The differences between experiment and simulation can be used to further improve the model in a later process.