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Researcher
- Andrzej Nycz
- Chris Masuo
- Peter Wang
- Alex Walters
- Singanallur Venkatakrishnan
- Venugopal K Varma
- Vincent Paquit
- Amir K Ziabari
- Brian Gibson
- Diana E Hun
- Joshua Vaughan
- Luke Meyer
- Mahabir Bhandari
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen M Killough
- Udaya C Kalluri
- William Carter
- Adam Aaron
- Akash Jag Prasad
- Amit Shyam
- Bryan Maldonado Puente
- Calen Kimmell
- Charles D Ottinger
- Chelo Chavez
- Christopher Fancher
- Chris Tyler
- Clay Leach
- Corey Cooke
- Gina Accawi
- Gordon Robertson
- Govindarajan Muralidharan
- Gurneesh Jatana
- J.R. R Matheson
- Jaydeep Karandikar
- Jay Reynolds
- Jeff Brookins
- Jesse Heineman
- John Potter
- Mark M Root
- Michael Kirka
- Nolan Hayes
- Obaid Rahman
- Riley Wallace
- Ritin Mathews
- Rose Montgomery
- Ryan Kerekes
- Sally Ghanem
- Sergey Smolentsev
- Steven J Zinkle
- Thomas R Muth
- Vladimir Orlyanchik
- Xiaohan Yang
- Yanli Wang
- Ying Yang
- Yutai Kato

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

System and method for part porosity monitoring of additively manufactured components using machining
In additive manufacturing, choice of process parameters for a given material and geometry can result in porosities in the build volume, which can result in scrap.

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

V-Cr-Ti alloys have been proposed as candidate structural materials in fusion reactor blanket concepts with operation temperatures greater than that for reduced activation ferritic martensitic steels (RAFMs).

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.

We present the design, assembly and demonstration of functionality for a new custom integrated robotics-based automated soil sampling technology as part of a larger vision for future edge computing- and AI- enabled bioenergy field monitoring and management technologies called

Creating a framework (method) for bots (agents) to autonomously, in real time, dynamically divide and execute a complex manufacturing (or any suitable) task in a collaborative, parallel-sequential way without required human interaction.

Materials produced via additive manufacturing, or 3D printing, can experience significant residual stress, distortion and cracking, negatively impacting the manufacturing process.

Fusion reactors need efficient systems to create tritium fuel and handle intense heat and radiation. Traditional liquid metal systems face challenges like high pressure losses and material breakdown in strong magnetic fields.

The traditional window installation process involves many steps. These are becoming even more complex with newer construction requirements such as installation of windows over exterior continuous insulation walls.