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Researcher
- Andrzej Nycz
- Chris Masuo
- Peter Wang
- Alex Walters
- Brian Gibson
- Joshua Vaughan
- Luke Meyer
- Soydan Ozcan
- Udaya C Kalluri
- William Carter
- Xianhui Zhao
- Akash Jag Prasad
- Alex Roschli
- Amit Shyam
- Annetta Burger
- Brian Post
- Calen Kimmell
- Carter Christopher
- Chance C Brown
- Chelo Chavez
- Christopher Fancher
- Chris Tyler
- Clay Leach
- Dali Wang
- Debraj De
- Erin Webb
- Evin Carter
- Gautam Malviya Thakur
- Gordon Robertson
- Halil Tekinalp
- J.R. R Matheson
- James Gaboardi
- Jaydeep Karandikar
- Jay Reynolds
- Jeff Brookins
- Jeremy Malmstead
- Jesse Heineman
- Jesse McGaha
- Jian Chen
- John Potter
- Kevin Sparks
- Kitty K Mccracken
- Liz McBride
- Mengdawn Cheng
- Oluwafemi Oyedeji
- Paula Cable-Dunlap
- Riley Wallace
- Ritin Mathews
- Sanjita Wasti
- Todd Thomas
- Tyler Smith
- Vincent Paquit
- Vladimir Orlyanchik
- Wei Zhang
- Xiaohan Yang
- Xiuling Nie
- Zhili Feng

We have developed a novel extrusion-based 3D printing technique that can achieve a resolution of 0.51 mm layer thickness, and catalyst loading of 44% and 90.5% before and after drying, respectively.

Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.

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.

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 invention is directed to a machine leaning methodology to quantify the association of a set of input variables to a set of output variables, specifically for the one-to-many scenarios in which the output exhibits a range of variations under the same replicated input condi

The use of biomass fiber reinforcement for polymer composite applications, like those in buildings or automotive, has expanded rapidly due to the low cost, high stiffness, and inherent renewability of these materials. Biomass are commonly disposed of as waste.

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.

In additive printing that utilizes multiple robotic agents to build, each agent, or “arm”, is currently limited to a prescribed path determined by the user.