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Researcher
- Amit Shyam
- Peeyush Nandwana
- Alex Plotkowski
- Brian Post
- Sudarsanam Babu
- Yong Chae Lim
- Zhili Feng
- Blane Fillingim
- James A Haynes
- Jian Chen
- Lauren Heinrich
- Rangasayee Kannan
- Ryan Dehoff
- Sumit Bahl
- Thomas Feldhausen
- Wei Zhang
- Yousub Lee
- Adam Stevens
- Alexander I Wiechert
- Alice Perrin
- Andres Marquez Rossy
- Bryan Lim
- Christopher Fancher
- Costas Tsouris
- Dali Wang
- Dean T Pierce
- Debangshu Mukherjee
- Gerry Knapp
- Gordon Robertson
- Gs Jung
- Gyoung Gug Jang
- Jay Reynolds
- Jeff Brookins
- Jiheon Jun
- Jovid Rakhmonov
- Md Inzamam Ul Haque
- Nicholas Richter
- Olga S Ovchinnikova
- Peter Wang
- Priyanshi Agrawal
- Radu Custelcean
- Ramanan Sankaran
- Roger G Miller
- Sarah Graham
- Sunyong Kwon
- Tomas Grejtak
- Vimal Ramanuj
- Wenjun Ge
- 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.

Among the methods for point source carbon capture, the absorption of CO2 using aqueous amines (namely MEA) from the post-combustion gas stream is currently considered the most promising.

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.

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

Additive manufacturing (AM) enables the incremental buildup of monolithic components with a variety of materials, and material deposition locations.