Our research aims at the image-based prediction of the material properties of stochastic microstructures. Uncertain microstructure information is gathered from dedicated synthetic microstructures as well as from experimental measurements. Mechanical simulations feed in-silico databases for data analysis and processing in machine learning tools. These serve for the automatic feature extraction and for the development of dedicated mechanical substitute models. The inputs consist of 3D image data only. Linking the stochastic microstructure synthesis and experimental data is a key objective during this task, in EXC2075 Data-Integrated Simulation Science and in the GAMM AG Data in order to augment scarce real-world data.
| Acronym: | EXC2075-PUMD |
| Website: | www.simtech.uni-stuttgart.de/exc/research/pn/pn3/pn3-1 |
| Contact: | Prof. Dr. Felix Fritzen, University of Stuttgart |
| DFG Classification: | 402-02 Mechanics and Constructive Mechanical Engineering; 405-05, -06 Materials Engineering, 406-03 Materials Science; 410-05 Construction Engineering and Architecture |
| Material/ Methodology: | synthetic and experimental inclusion-matrix and porous microstructures / image-based homogenization and uncertainty prediction |
| Engagement: | 14 EXC2075 projects, 2 EXC2075 project networks, Fraunhofer ITWM collaboration, GAMM AG Data |
NFDI-MatWerk
Funded by the German Research Foundation (DFG) under the National Research Data Infrastructure – NFDI 38/1 – 460247524
NFDI-MatWerk
Funded by the German Research Foundation (DFG) under the National Research Data Infrastructure – NFDI 38/1 – 460247524
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