| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Data Science and Analytics for Materials Imaging and Quantification
|
| Presentation Title |
Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys |
| Author(s) |
Yue Li, Leigh Stephenson, Raabe Dierk, Baptiste Gault |
| On-Site Speaker (Planned) |
Yue Li |
| Abstract Scope |
Nano-size L12-type ordered structures are commonly used in FCC-based alloys to improve mechanical properties. They are often coherent with matrix, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is inefficient to manually analyse a large APT data and make crystal structure recognitions. Here, we introduce an intelligent L12-ordered structure recognition method based on convolutional neural network (CNN). The SDMs of simulated L12-ordered structure and FCC matrix were generated as training and validation datasets. These simulated images were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was tested on of L12–type δ'–Al3(LiMg) particles with an average radius of 3nm in an FCC-based Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 0.5nm. The proposed method is promising to be extended to other ordered structures in future. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Characterization, Other |