| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Elucidating Microstructural Evolution Under Extreme Environments
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| Presentation Title |
Application of Autonomous STEM Acquisition for High-Throughput 3D Characterization of Irradiated Materials |
| Author(s) |
Hangyu Li, Wei-Ying Chen, Matthew Olszta, Benjamin Eftink, Logan Ward, Zhi-Gang Mei, Kevin Fiedler, James Haag, Derek Hopkins, Kevin Field |
| On-Site Speaker (Planned) |
Hangyu Li |
| Abstract Scope |
3D characterization of defects in materials is essential for understanding their properties and behavior under various conditions. However, traditional methods using STEM are hindered by extensive manual adjustments to the stage and tilt series acquisition, limiting the throughput and statistical robustness of the analysis.
Autonomous STEM offers a promising solution to these challenges by automating the entire microscope operation and image acquisition. Our streamlined pipeline leverages this capability, integrating it with machine learning (ML)-powered defect detection using YOLOv8/v10, enabling rapid and accurate tracking of defects across the tilt series, which are then inputted to Obtain3D for 3D reconstruction.
Applied to nickel irradiated in-situ at 600°C, our autonomous STEM-driven approach successfully reveals the 3D distribution of dislocation loops and voids, achieving a reconstruction time of 0.44 seconds. This demonstrates the significant potential of autonomous STEM to accelerate comprehensive characterization of radiation-induced microstructural evolution, facilitating the development of radiation-resistant materials. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Characterization, Machine Learning, Nuclear Materials |