| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Materials Research in Reduced Gravity
|
| Presentation Title |
Analysis of In-Situ Microgravity Equiaxed Solidification Experiment using Machine Learning and Advanced Ground-Based Characterization Techniques |
| Author(s) |
Jonathan Mullen, Shashidhara Marathe, Saranarayanan Ramachandran, Wajira Mirihanage, David J. Browne |
| On-Site Speaker (Planned) |
David J. Browne |
| Abstract Scope |
A combined Machine Learning and modular image analysis approach has been used to automatically process and interrogate quantitatively an in situ X-ray video-microscopy sequence of equiaxed solidification of an Al-20wt.% Cu alloy, executed under microgravity conditions on board the Maser-13 sounding rocket. The 3D dendritic structure of the as-flown microgravity sample was then revealed by synchrotron X-ray computed microtomography. Crystallographic orientations of the dendrites were also extracted using electron backscattered diffraction, yielding extended microstructural details at micron resolution. In this way the nucleation, growth, orientation and final inclinations of all the equiaxed dendrites have been quantified and correlated, providing high quality benchmark data on the evolution of the equiaxed microstructure under diffusion-controlled conditions. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Aluminum, Solidification |