| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Simulations/Experiments Integration for Next Generation Hypersonic Materials
|
| Presentation Title |
Computational Design of Ni-based SX Superalloys: A Critical Assessment of Machine-learned and Thermodynamic Models in View of Experimental Properties |
| Author(s) |
Abel Rapetti, Cervellon Alice, Menou Edern, Rame Jérémy, Tancret Franck, Cormier Jonathan |
| On-Site Speaker (Planned) |
Abel Rapetti |
| Abstract Scope |
Computational design is a promising approach used in particular to predict novel compositions for new generation Ni-based single crystal superalloys. Amongst the existing approaches, some allows the exploration of new compositional fields by predicting, for eachcomposition, the ideal performances after optimization of heat treatments. Such approach does not provide, however, directions to optimize heat treatments, which remain to be determined by a classical approach. Five nickel-based single crystalline superalloys were cast with computationally designed compositions. This work was undertaken to determine heat treatments that lead to the target microstructure for the five alloys as well as improved creep strength. Once this target was reached, tensile and creep tests were carried out. In this presentation, a critical assessment of mechanical properties obtained for these five alloys will be performed with respect to computational predictions to propose some guidelines for further improvements of the computational design methodology. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, High-Temperature Materials |