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Researcher
- Andrzej Nycz
- Amit Shyam
- Chris Masuo
- Peter Wang
- Alex Plotkowski
- Alex Walters
- Yong Chae Lim
- Zhili Feng
- Brian Gibson
- Brian Post
- James A Haynes
- Jian Chen
- Joshua Vaughan
- Luke Meyer
- Peeyush Nandwana
- Rangasayee Kannan
- Ryan Dehoff
- Sumit Bahl
- Udaya C Kalluri
- Wei Zhang
- William Carter
- Adam Stevens
- Akash Jag Prasad
- Alice Perrin
- Andres Marquez Rossy
- Bryan Lim
- Calen Kimmell
- Chelo Chavez
- Christopher Fancher
- Chris Tyler
- Clay Leach
- Dali Wang
- Dean T Pierce
- Gerry Knapp
- Gordon Robertson
- J.R. R Matheson
- Jaydeep Karandikar
- Jay Reynolds
- Jeff Brookins
- Jesse Heineman
- Jiheon Jun
- John Potter
- Jovid Rakhmonov
- Nicholas Richter
- Priyanshi Agrawal
- Riley Wallace
- Ritin Mathews
- Roger G Miller
- Sarah Graham
- Sudarsanam Babu
- Sunyong Kwon
- Tomas Grejtak
- Vincent Paquit
- Vladimir Orlyanchik
- William Peter
- Xiaohan Yang
- Ying Yang
- Yiyu Wang
- Yukinori Yamamoto

A finite element approach integrated with a novel constitute model to predict phase change, residual stresses and part deformation.

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.

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

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.