| 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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Österreicher, Johannes Albert
Austrian Institute of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (12/12 displayed)
- 2024Optimizing the Zn and Mg contents of Al–Zn–Mg wrought alloys for high strength and industrial-scale extrudabilitycitations
- 2024Differential scanning calorimetry of age-hardenable aluminium alloys: effects of sample preparation, experimental conditions, and baseline correctioncitations
- 2024In situ conductometry for studying the homogenization of Al-Mg-Si alloys and predicting extrudate grain structure through machine learning
- 2024Mechanisms of electrically assisted deformation of an Al–Mg alloy (AA5083-H111): Portevin–Le Chatelier phenotype transformation, suppression, and prolonged necking
- 2024Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopycitations
- 2024Parameter study of extrusion simulation and grain structure prediction for 6xxx alloys with varied Fe contentcitations
- 2023Tolerance of Al–Mg–Si Wrought Alloys for High Fe Contents: The Role of Effective Sicitations
- 2022Combined Cyclic Deformation and Artificial Ageing of an Al-Mg-Si Alloy
- 2022Electrically assisted forming
- 2022Analysis of second phase particles in metals using deep learning: Segmentation of nanoscale dispersoids in 6xxx series aluminium alloys (Al-Mg-Si)citations
- 2022Influence of different homogenization heat treatments on the microstructure and hot flow stress of the aluminum alloy AA6082citations
- 2017Quantitative prediction of the mechanical properties of precipitation hardened alloys with a special application to Al-Mg-Si
Places of action
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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.