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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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Kronsteiner, Johannes
in Cooperation with on an Cooperation-Score of 37%
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Publications (5/5 displayed)
- 2024In situ conductometry for studying the homogenization of Al-Mg-Si alloys and predicting extrudate grain structure through machine learning
- 2024Modeling of Texture Development during Metal Forming Using Finite Element Visco-Plastic Self-Consistent Modelcitations
- 2024Parameter study of extrusion simulation and grain structure prediction for 6xxx alloys with varied Fe contentcitations
- 2023Wire arc additive manufacturing of light metals: From experimental investigation to numerical process simulation and microstructural modelingcitations
- 2017Recent Advances in Aluminium Casting Simulation: Evolving Domains & Dynamic Meshing
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article
In situ conductometry for studying the homogenization of Al-Mg-Si alloys and predicting extrudate grain structure through machine learning
Abstract
In industrial practice, no sensors capable of obtaining microstructural information in situ during thermomechanical processing of Al alloys are commonly employed. Inductive electrical conductivity measurement is safe, inexpensive, and capable of acquiring valuable information about precipitation and dissolution processes. However, commercial eddy current sensors work only at low temperatures near room temperature and are thus not suitable for in situ conductometry during heat treatments of Al alloys. We designed a high -temperature eddy current sensor and performed in situ conductometry during the homogenization of six Al -Mg -Si wrought alloys, three of which are experimental recycling -friendly alloys with increased Fe content. The results are interpreted with regard to microstructural investigations, and the advantages and limitations of our approach are discussed. As a proof -of -concept, we show how the conductivity curves and extrusion process parameters can be combined to predict final extrudate grain structures using machine learning. To achieve this, we employed finite element simulation of extrusion coupled with microstructural simulation over a wide parameter range, validated by extrusion experiments and metallography, and trained a feedforward neural network. We believe our interdisciplinary approach can lead to improvements in the industrial processing of Al wrought alloys.