| 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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Vallejo Rodríguez, Luis
VTT Technical Research Centre of Finland
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (3/3 displayed)
- 2024Analysis of rolling contact and tooth root bending fatigue in a new high-strength steel: Experiments and micromechanical modellingcitations
- 2022Micromechanical modelling of additively manufactured high entropy alloys to establish structure-properties-performance workflow
- 2021Numerical and experimental evaluation of dynamic shear response of a high entropy alloy
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document
Micromechanical modelling of additively manufactured high entropy alloys to establish structure-properties-performance workflow
Abstract
Additive manufacturing is a manufacturing route able to produce complex components with minimal raw-material utilization and high-level of process control. However, the rapid solidification rates, strong temperature gradients and extremely localized melting lead to non-equilibrium microstructures that require a better understanding of solid-state transformation, solidification behaviour and structure-property-performance workflow of AMed materials. HEAs unique compositions and complex microstructures slow down considerably the AM parameter optimization of these materials. Numerical simulations offer a better understanding of the structure-properties-performance of the materials with a reduced number or physical experiments. Hence, a multi-scale modelling approach is taken. For the alloy design phase, Calphad analysis together with DFT simulations and machine learning tools are used to find the most promising HEA compositions. Studying the different microstructural defects, deformation mechanisms that affect the strain hardening potential, Crystal Plasticity models are developed to evaluate the performance of AMed HEAs and the overall workflow.