| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Gröger, Benjamin
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (14/14 displayed)
- 2023Development and verification of a cure-dependent visco-thermo-elastic simulation model for predicting the process-induced surface waviness of continuous fiber reinforced thermosetscitations
- 2023Modelling of composite manufacturing processes incorporating large fibre deformations and process parameter interactions
- 2023Correction: Troschitz et al. Joining Processes for Fibre-Reinforced Thermoplastics: Phenomena and Characterisation. Materials 2022, 15, 5454
- 2022A Data Driven Modelling Approach for the Strain Rate Dependent 3D Shear Deformation and Failure of Thermoplastic Fibre Reinforced Composites: Experimental Characterisation and Deriving Modelling Parameterscitations
- 2022A Review on the Modeling of the Clinching Process Chain—Part II: Joining Processcitations
- 2022Review on mechanical joining by plastic deformationcitations
- 2022Development of a high-fidelity framework to describe the process-dependent viscoelasticity of a fast-curing epoxy matrix resin including testing, modelling, calibration and validationcitations
- 2022Characterisation of Fibre Bundle Deformation Behaviour—Test Rig, Results and Conclusionscitations
- 2022Warmforming flow pressing characteristics of continuous fibre reinforced thermoplastic compositescitations
- 2022Computed tomography investigation of the material structure in clinch joints in aluminium fibre-reinforced thermoplastic sheetscitations
- 2021Temperature dependent modelling of fibre-reinforced thermoplastic organo-sheet material for forming and joining process simulationscitations
- 2021Clinching of thermoplastic composites and metals - a comparison of three novel joining technologiescitations
- 2021Modelling of thermally supported clinching of fibre-reinforced thermoplastics: Approaches on mesoscale considering large deformations and fibre failurecitations
- 2019Experimental description of draping effects and their influence on structural behavior of fiber reinforced composites.
Places of action
| Organizations | Location | People |
|---|
article
A Data Driven Modelling Approach for the Strain Rate Dependent 3D Shear Deformation and Failure of Thermoplastic Fibre Reinforced Composites: Experimental Characterisation and Deriving Modelling Parameters
Abstract
The 3D shear deformation and failure behaviour of a glass fibre reinforced polypropylene in a shear strain rate range of γ˙=2.2×10<sup>−4</sup> to 3.4 1/s is investigated. An Iosipescu testing setup on a servo-hydraulic high speed testing unit is used to experimentally characterise the in-plane and out-of-plane behaviour utilising three specimen configurations (12-, 13- and 31-direction). The experimental procedure as well as the testing results are presented and discussed. The measured shear stress–shear strain relations indicate a highly nonlinear behaviour and a distinct rate dependency. Two methods are investigated to derive according material characteristics: a classical engineering approach based on moduli and strengths and a data driven approach based on the curve progression. In all cases a Johnson–Cook based formulation is used to describe rate dependency. The analysis methodologies as well as the derived model parameters are described and discussed in detail. It is shown that a phenomenologically enhanced regression can be used to obtain material characteristics for a generalising constitutive model based on the data driven approach.