About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
Accelerated Discovery and Qualification of Nuclear Materials for Energy Applications
|
Presentation Title |
Characterization of As-Fabricated Additively Manufactured Alloy 718 Enhanced by Modern Tools and Machine Learning |
Author(s) |
Stephen Taller, Luke Scime, Kurt Terrani |
On-Site Speaker (Planned) |
Stephen Taller |
Abstract Scope |
The design of new materials for nuclear energy applications has increased with computing power, and manufacturing has seen revolutionary improvements with advanced manufacturing (AM) techniques. To keep pace with these developments, characterization and post irradiation examination procedures must be improved to accelerate nuclear materials evaluation. This presentation discusses the tools being developed for automated transmission electron microscopy (TEM) image and energy dispersive spectroscopy (EDS) spectra acquisition. These tools are being used to enable operator-free data collection using a FEI F200X Talos scanning TEM. The resulting images are used to train a platform-agnostic artificial intelligence and machine learning (AI/ML) tool with pixel-wise defect-detection algorithms. These techniques have been demonstrated using high-throughput characterization of an as-fabricated AM Ni-Fe-Cr alloy 718. Precipitate morphology, density, and elemental composition were characterized in anticipation of irradiated specimens to determine precipitate stability under irradiation. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Additive Manufacturing, Characterization |