| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Materials Informatics to Accelerate Nuclear Materials Investigation
|
| Presentation Title |
Accelerating Characterization of Radiation Driven Processes using Machine Learning Tools |
| Author(s) |
Stephen Taller |
| On-Site Speaker (Planned) |
Stephen Taller |
| Abstract Scope |
Over the last half century, tools such as the electron microscope enabled insight into processes surrounding radiation induced defects and microstructure evolution. State-of-the-art electron microscopes can acquire large image datasets rapidly across multiple detectors and spectroscopy modes. Manual annotation analysis of high-resolution data is inefficient and may no longer be feasible in the era of big data. Additionally, expert identification still leads to a large variation in quantification. This presentation will focus on the development and application of a pixel-wise defect detection machine learning tool called a dynamic segmentation convolutional neural network with associated automated image acquisition and post-processing to rapidly quantify information in both static and dynamic (video) transmission electron microscopy datasets. Examples focus on precipitate, cavity, and dislocation evolution in superalloy 718 and include microstructures pre-irradiation, evolving during in-situ ion irradiations, and post-irradiation in the High Flux Isotope Reactor at nominally 300°C or 600°C. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Machine Learning, Nuclear Materials |