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Researcher
- Andrzej Nycz
- Chris Masuo
- Peter Wang
- Rafal Wojda
- Alex Walters
- Prasad Kandula
- Singanallur Venkatakrishnan
- Vincent Paquit
- Amir K Ziabari
- Brian Gibson
- Christopher Fancher
- Diana E Hun
- Joshua Vaughan
- Luke Meyer
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen M Killough
- Udaya C Kalluri
- Vandana Rallabandi
- William Carter
- Akash Jag Prasad
- Alex Plotkowski
- Amit Shyam
- Bryan Maldonado Puente
- Calen Kimmell
- Chelo Chavez
- Chris Tyler
- Clay Leach
- Corey Cooke
- Gina Accawi
- Gordon Robertson
- Gurneesh Jatana
- J.R. R Matheson
- Jaydeep Karandikar
- Jay Reynolds
- Jeff Brookins
- Jesse Heineman
- John Potter
- Marcio Magri Kimpara
- Mark M Root
- Michael Kirka
- Mostak Mohammad
- Nolan Hayes
- Obaid Rahman
- Omer Onar
- Praveen Kumar
- Riley Wallace
- Ritin Mathews
- Ryan Kerekes
- Sally Ghanem
- Shajjad Chowdhury
- Subho Mukherjee
- Suman Debnath
- Vladimir Orlyanchik
- Xiaohan Yang

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

Misalignment issues of the PWPT system have been addressed. The intercell power transformer has been introduced in order to improve load sharing of the system during a mismatch of the primary single-phase coil and the secondary multi-phase coils.

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.

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.

An ORNL invention proposes using 3D printing to make conductors with space-filling thin-wall cross sections. Space-filling thin-wall profiles will maximize the conductor volume while restricting the path for eddy currents induction.