About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Mechanical Behavior at the Nanoscale VIII
|
| Presentation Title |
Non-Conventional Nano-Precipitates Formation in Fast Solidified Al Alloys: A Machine Learning-Augmented Modeling Study |
| Author(s) |
Yue Fan |
| On-Site Speaker (Planned) |
Yue Fan |
| Abstract Scope |
The mechanical behaviors of Al alloys are dictated by the precipitates formed during processing and/or heat treatment. Recent experiments have shown that Al-Si-Mg alloys solidified under high cooling rates contain Si-enriched nano-clusters that are remarkably different from the Mg-Si co-clusters (e.g. β" particles) in conventionally cast alloys. However, the responsible mechanism remains unknown. Here by integrating energy landscape sampling within complex local chemistry, machine learning techniques, and a kMC framework, we discovered that the actual vacancy-Si migration barriers are much lower than those assumed in the classic linear interpolation approximation, and we further uncovered new microstructural evolution pathways leading to the formation of non-conventional nanoscale precipitates. Our findings help explain the experiments in Al alloys processed via high-pressure die casting or selective laser melting. These results may have important implications for the strengthening mechanisms in hardenable Al alloys, particularly through the lens of nanoscale structural evolution and non-equilibrium processing pathways. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Computational Materials Science & Engineering, Machine Learning |