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Researcher
- Andrzej Nycz
- Amit Shyam
- Chris Masuo
- Peeyush Nandwana
- Peter Wang
- Alex Plotkowski
- Alex Walters
- Brian Post
- Rangasayee Kannan
- Sudarsanam Babu
- Blane Fillingim
- Brian Gibson
- James A Haynes
- Joshua Vaughan
- Lauren Heinrich
- Luke Meyer
- Ryan Dehoff
- Sumit Bahl
- Thomas Feldhausen
- Udaya C Kalluri
- William Carter
- Ying Yang
- Yousub Lee
- Adam Stevens
- Akash Jag Prasad
- Alice Perrin
- Andres Marquez Rossy
- Bruce A Pint
- Bryan Lim
- Calen Kimmell
- Chelo Chavez
- Christopher Fancher
- Chris Tyler
- Clay Leach
- Dean T Pierce
- Gerry Knapp
- Gordon Robertson
- J.R. R Matheson
- Jaydeep Karandikar
- Jay Reynolds
- Jeff Brookins
- Jesse Heineman
- John Potter
- Jovid Rakhmonov
- Nicholas Richter
- Riley Wallace
- Ritin Mathews
- Roger G Miller
- Sarah Graham
- Steven J Zinkle
- Sunyong Kwon
- Tim Graening Seibert
- Tomas Grejtak
- Vincent Paquit
- Vladimir Orlyanchik
- Weicheng Zhong
- Wei Tang
- William Peter
- Xiang Chen
- Xiaohan Yang
- Yanli Wang
- Yiyu Wang
- Yukinori Yamamoto
- Yutai Kato

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.

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.

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.

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.

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