| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
2024 Technical Division Student Poster Contest
|
| Presentation Title |
SPG-19: Exploring High-temperature 7000 Series Aluminum Alloys: High-throughput DFT Calculations and Machine Learning Approaches |
| Author(s) |
Yu-ning Chiu, Chung-yi Yu, Wei-ting Lin, Chia-chia Hsieh, Shih-kang Lin |
| On-Site Speaker (Planned) |
Yu-ning Chiu |
| Abstract Scope |
The 7000 series (Al-Zn-Mg) aluminum alloys are renowned for their exceptional lightweight yet high-strength properties, attributed to a metastable strengthening phase, known as η' phase. However, their high-temperature stability is compromised as the metastable η' precipitates are prone to transforming into stable η phases. To address this issue, we investigated the segregation energies of 21 elements as potential segregants through high-throughput DFT calculations. Our machine learning analysis revealed that interfacial segregation is governed by reducing strain energy at the precipitate-matrix interface. Notably, smaller atoms like Cu, Mn, and Ni as suitable segregants, contributing to precipitate stability. Experimentally, we achieved a notable 30°C increase in the transition temperature of η' precipitates in an Al-Zn-Mg-Ni alloy compared to the standard AA7075 alloy, confirmed by DSC analyses. This research provides deeper insights into the mechanisms of interfacial segregation and offers strategic guidance for microalloying in the development of high-temperature 7000 series aluminum alloys. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Aluminum, Computational Materials Science & Engineering, Machine Learning |