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Researcher
- Yong Chae Lim
- Zhili Feng
- Jian Chen
- Rangasayee Kannan
- Vlastimil Kunc
- Wei Zhang
- Adam Stevens
- Ahmed Hassen
- Brian Post
- Bryan Lim
- Callie Goetz
- Dali Wang
- Dan Coughlin
- Jiheon Jun
- Jim Tobin
- Josh Crabtree
- Kim Sitzlar
- Merlin Theodore
- Peeyush Nandwana
- Priyanshi Agrawal
- Roger G Miller
- Ryan Dehoff
- Sarah Graham
- Steven Guzorek
- Subhabrata Saha
- Sudarsanam Babu
- Tomas Grejtak
- Vipin Kumar
- William Peter
- Yiyu Wang
- Yukinori Yamamoto

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

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

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.

Through the use of splicing methods, joining two different fiber types in the tow stage of the process enables great benefits to the strength of the material change.

The technologies provide a coating method to produce corrosion resistant and electrically conductive coating layer on metallic bipolar plates for hydrogen fuel cell and hydrogen electrolyzer applications.

Welding high temperature and/or high strength materials for aerospace or automobile manufacturing is challenging.

This technology is a strategy for decreasing electromagnetic interference and boosting signal fidelity for low signal-to-noise sensors transmitting over long distances in extreme environments, such as nuclear energy generation applications, particularly for particle detection.