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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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Arnoldt, Aurel Ramon
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (9/9 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
- 2024Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopycitations
- 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
- 2023Tolerance of Al–Mg–Si Wrought Alloys for High Fe Contents: The Role of Effective Sicitations
- 2022Investigations on a ternary Mg-Ca-Si wrought alloy extruded at moderate temperaturescitations
- 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
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article
Differential scanning calorimetry of age-hardenable aluminium alloys: effects of sample preparation, experimental conditions, and baseline correction
Abstract
Precipitation processes in age hardenable aluminium alloys are often investigated by differential scanning calorimetry (DSC). The endothermic and exothermic peaks of the DSC signal correspond to the dissolution and formation of phases, respectively. However, parasitic effects can lead to an unintended curvature of the DSC signal. Although a baseline correction can be used, some imperfections typically remain. Additionally, sample preparation and experimental conditions can influence the precipitation sequence itself and, therefore, the DSC curve. In this study, we investigate the influence of sample preparation by milling and punching on DSC curves for three different aluminium alloys: EN AW-2024, EN AW-6082, and EN AW-7075. Additionally, the influence of quenching and natural ageing was investigated for EN AW-6082. We found that deformation introduced by punching the DSC samples with a piercing die after heat treatment leads to a change in the precipitation kinetics in 2xxx, 6xxx, and 7xxx series alloys. The influence was strongest for punching the samples after solution heat treatment and less significant for punching after artificial ageing. The influence of sample preparation can be avoided by punching the samples before solution heat treatment. If this is not practicable, milling of the samples is a good alternative. The choice of quenching medium and short storage at room temperature before measurement (5 min) had only small effects on the DSC curves. Moreover, the start temperature of the measurement was found to be crucial. For observing phases forming below 100 °C and for low-bias baseline correction, the measurement should start at low-temperatures (i.e.~ −50 °C).