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Researcher
- Vivek Sujan
- Amit Shyam
- Adam Siekmann
- Alex Plotkowski
- Omer Onar
- Subho Mukherjee
- Yong Chae Lim
- Zhili Feng
- Erdem Asa
- Isabelle Snyder
- James A Haynes
- Jian Chen
- Peeyush Nandwana
- Rangasayee Kannan
- Ryan Dehoff
- Sumit Bahl
- Wei Zhang
- Adam Stevens
- Alice Perrin
- Andres Marquez Rossy
- Brian Post
- Bryan Lim
- Christopher Fancher
- Dali Wang
- Dean T Pierce
- Gerry Knapp
- Gordon Robertson
- Hyeonsup Lim
- Jay Reynolds
- Jeff Brookins
- Jiheon Jun
- Jovid Rakhmonov
- Nicholas Richter
- Peter Wang
- Priyanshi Agrawal
- Roger G Miller
- Sarah Graham
- Shajjad Chowdhury
- Sudarsanam Babu
- Sunyong Kwon
- Tomas Grejtak
- William Peter
- 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.

The growing demand for electric vehicles (EVs) has necessitated significant advancements in EV charging technologies to ensure efficient and reliable operation.

The growing demand for renewable energy sources has propelled the development of advanced power conversion systems, particularly in applications involving fuel cells.

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.