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Researcher
- Amit Shyam
- Ryan Dehoff
- Alex Plotkowski
- Brian Post
- Peeyush Nandwana
- Singanallur Venkatakrishnan
- Sudarsanam Babu
- Amir K Ziabari
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- Peter Wang
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- Stephen M Killough
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- Vincent Paquit
- Yousub Lee
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- Alice Perrin
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- Nolan Hayes
- Obaid Rahman
- Ramanan Sankaran
- Rangasayee Kannan
- Roger G Miller
- Ryan Kerekes
- Sally Ghanem
- Sarah Graham
- Sunyong Kwon
- Vimal Ramanuj
- Wenjun Ge
- William Peter
- Ying Yang
- Yukinori Yamamoto

ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.

Currently available cast Al alloys are not suitable for various high-performance conductor applications, such as rotor, inverter, windings, busbar, heat exchangers/sinks, etc.

The invented alloys are a new family of Al-Mg alloys. This new family of Al-based alloys demonstrate an excellent ductility (10 ± 2 % elongation) despite the high content of impurities commonly observed in recycled aluminum.

We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.

The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.

This work seeks to alter the interface condition through thermal history modification, deposition energy density, and interface surface preparation to prevent interface cracking.

Additive manufacturing (AM) enables the incremental buildup of monolithic components with a variety of materials, and material deposition locations.

Ceramic matrix composites are used in several industries, such as aerospace, for lightweight, high quality and high strength materials. But producing them is time consuming and often low quality.

This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.