About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advanced Characterization Techniques for Quantifying and Modeling Deformation
|
| Presentation Title |
Microstructure Informatics: Insights into Selective Grain Growth in Polycrystalline Ni-Base Superalloys |
| Author(s) |
Pascal Thome, Luis Arciniaga, Sammy Tin |
| On-Site Speaker (Planned) |
Pascal Thome |
| Abstract Scope |
The advancement of Microstructure Informatics (MI) has led to significant improvements in the characterization of advanced polycrystalline Ni-base superalloys during hot forging and subsequent heat treatment by combining Electron Backscatter Diffraction (EBSD) and Energy Dispersive X-Ray Spectroscopy (EDS) with Computer Vision (CV) and Machine Learning (ML). We present an automated procedure that extracts a rich array of physical, crystallographic and geometric microstructure descriptors which can be used to characterize microstructure evolution during processing. This enabled unprecedented insights into phenomena related to selective grain growth (SGG) in powder metallurgy processed turbine discs. We demonstrate that localized plastic strain and specific spatial arrangements of γ’ precipitates significantly influence grain growth kinetics and recrystallization mechanisms. The quantitative analysis of those features enables the identification of precursors for SGG. Using automated Microstructure Informatics provides robust, physically based metrics for optimizing alloy processing and performance of components for the application in advanced turbine engines. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, High-Temperature Materials, Machine Learning |