About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Designing High-Temperature Multicomponent NiTiHfPd SMAs Using Machine Learning |
Author(s) |
Hatim Raji, Soheil Saedi |
On-Site Speaker (Planned) |
Hatim Raji |
Abstract Scope |
Machine learning (ML) has become an attractive approach for designing multicomponent alloys where the design space is extensive. Quaternary NiTiHfPd shape memory alloys (SMAs) are ultra-strong with a high potential for high-temperature actuation and damping application, yet they are significantly less studied compared to the other members of the SMA family. This study aims to accelerate the slow pace of the research on NiTiHfPd SMAs that is mostly arisen from the high cost of the Pd element using a data-mining approach. To this end, a database created by compiling all published and unpublished data and extended through principles of high entropy alloy design is used. An ML algorithm is developed to predict the transformation temperatures (TTs) and hysteresis of NiTiHfPd of certain compositions and assists to understand the trend in the relationship between the compositions and TTs. Several classifiers are employed, and the predicted data is validated by experiment. |
Proceedings Inclusion? |
Planned: |
Keywords |
Phase Transformations, High-Temperature Materials, High-Entropy Alloys |