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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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Theissing, Moritz
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Publications (6/6 displayed)
- 2024Modeling the concurrent growth of inter- and intragranular Si precipitates during slow cooling of the alloy AA6016
- 2024How to properly investigate recrystallization in wrought aluminum alloys
- 2024Investigation of the texture development of rolled aluminum alloy sheets during constant heating using in situ EBSD
- 2024Recrystallization in Wrought Aluminum Alloys - A Critical Evaluation of Characterization Methods
- 2023Microstructural evolution in cold rolled aluminum alloys during recrystallization – an in situ electron backscatter diffraction study
- 2023Investigation of recrystallization processes in aluminum alloys - in situ electron backscatter diffraction optimized for annealing at constant heating rates
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article
Modeling the concurrent growth of inter- and intragranular Si precipitates during slow cooling of the alloy AA6016
Abstract
This work combines two established models for precipitate growth in the bulk and at the grain boundary to investigate the growth of pure Si precipitates in the alloy AA6016 during slow cooling after hot rolling. Despite the simplicity of the approach, the predicted phase fractions and the predicted width of the precipitate-free zone are in agreement with the measured values. The model predictions suggest that raising the Si content and increasing the density of primary Fe-based particles reduces the fraction of grain boundary precipitates. The model correctly reproduces the technologically important effect of the cooling rate on intra- and intergranular growth. The model predictions can be used to optimize hot rolling schedules and the alloy design.