| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Aluminum Alloys: Development and Manufacturing
|
| Presentation Title |
A Study on New Precipitates in Al-Cu Alloys with La or Sr Additions Designed Using Machine Learning Based on OQMD |
| Author(s) |
Seoyeon Jeon, Suwon Park, Ahyeon Cho, Sehoon Kim, Yongjoo Kim, Hyunjoo Choi |
| On-Site Speaker (Planned) |
Seoyeon Jeon |
| Abstract Scope |
Aluminum alloys are known for their strong yet lightweight properties and excellent thermal/electrical conductivity. Strengthening aluminum alloys often involves precipitation hardening, where solid solution treatment and artificial aging create reinforcing phases for increased strength. Recent research has focused on enhancing mechanical properties by introducing new elements to form novel precipitates. Machine learning using data from the Open Quantum Materials Database predicts that Al-Cu precipitates with Lanthanum (La) or Strontium (Sr) additions could offer a balance between thermal/electrical conductivity and mechanical strength. Analysis of alloy microstructures and phases utilized techniques like TEM, SEM, and XRD. Mechanical properties were assessed through Vickers hardness testing to understand the link between precipitation phases and mechanical characteristics under various heat treatment conditions. |
| Proceedings Inclusion? |
Planned: Light Metals |
| Keywords |
Aluminum, Machine Learning, Mechanical Properties |