About this Abstract |
Meeting |
TMS Specialty Congress 2025
|
Symposium
|
8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
|
Presentation Title |
Machine Learning for the Computational Design of Single-Crystal Superalloys |
Author(s) |
Matthieu Degeiter, Edern Menou, Armand Barbot, Patricia Klotz, Sami Ben Elhaj Salah, Didier Locq, Yohan Cosquer, Mikael Perrut |
On-Site Speaker (Planned) |
Matthieu Degeiter |
Abstract Scope |
High Pressure Turbine (HPT) blades in gas turbines are designed with single crystal nickel-base superalloys, as their microstructure provides the blades with exceptional mechanical properties at high temperature. In service, the thermomechanical loadings imposed on the blade induce microstructure evolutions which eventually degrade the blade macroscopic properties. Improving the blade service life requires to develop new superalloy grades with improved creep resistance at high temperature. The fundamental mechanisms underlying the macroscopic behavior of materials are often complex, take place over extended ranges of length and time scales, and are strongly nonlinear. When the equations driving these mechanisms are not known, data-driven methods are particularly effective in guiding metallurgists and engineers. In this context, our work focuses on the construction of gaussian process models based on experimental data to estimate the creep life of superalloys as a function of their chemical composition and testing conditions. |
Proceedings Inclusion? |
Undecided |