| 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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Durmaz, Ali Riza
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (12/12 displayed)
- 2023Influence of Transformation Temperature on the High‐Cycle Fatigue Performance of Carbide‐Bearing and Carbide‐Free Bainitecitations
- 2023Materials fatigue prediction using graph neural networks on microstructure representationscitations
- 2023Microstructure quality control of steels using deep learning
- 2022Micromechanical fatigue experiments for validation of microstructure-sensitive fatigue simulation modelscitations
- 2022Addressing materials’ microstructure diversity using transfer learningcitations
- 2022Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopycitations
- 2022Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopy
- 2022Using optically pumped magnetometers to identify initial damage in bulk material during fatigue testingcitations
- 2022Optically pumped magnetometer measuring fatigue-induced damage in steelcitations
- 2022Addressing materials' microstructure diversity using transfer learningcitations
- 2021A deep learning approach for complex microstructure inferencecitations
- 2021Efficient Experimental and Data-Centered Workflow for Microstructure-Based Fatigue Datacitations
Places of action
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article
Micromechanical fatigue experiments for validation of microstructure-sensitive fatigue simulation models
Abstract
Crack initiation governs high cycle fatigue life and is sensitive to microstructural details. While corresponding microstructure-sensitive models are available, their validation is difficult. We propose a validation framework where a fatigue test is mimicked in a sub-modeling simulation by embedding the measured microstructure into the specimen geometry and adopting an approximation of the experimental boundary conditions. Exemplary, a phenomenological crystal plasticity model was applied to predict deformation in ferritic steel (EN1.4003). Hotspots in commonly used fatigue indicator parameter maps are compared with damage segmented from micrographs. Along with the data, the framework is published for benchmarking future micromechanical fatigue models. ; 160