| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
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
Places of action
| Organizations | Location | People |
|---|
article
Analysis of second phase particles in metals using deep learning: Segmentation of nanoscale dispersoids in 6xxx series aluminium alloys (Al-Mg-Si)
Abstract
During the homogenization heat treatment of 6xxx series aluminum alloys, nanoscale precipitates—commonly named dispersoids—are formed that influence material properties during further processing by extrusion, forging, or rolling, as well as final product quality. Obtaining dispersoid size distributions is commonly accomplished by manually counting and measuring the diameter of the particles in metallographic sections investigated by means of electron microscopy. An automatization of this process, while desired, is difficult due to varying backgrounds, brightness and contrast levels, noise, dispersoid morphologies as well as scratches and interference from other types of intermetallic phases. In order to segment dispersoids in a wide range of 6xxx series aluminum alloys, a neural network is trained on the basis of electron micrographs of different alloy samples that include various possible separation artifacts and is compared to several benchmark models. The neural network evaluated in this work shows promising results, consistent over all analyzed samples, with a maximum error of roughly 20% while the benchmark models show errors of up to 85%.