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Researcher
- Chris Tyler
- Amit Shyam
- Justin West
- Ritin Mathews
- Alex Plotkowski
- Yong Chae Lim
- Zhili Feng
- Brian Post
- David Olvera Trejo
- J.R. R Matheson
- James A Haynes
- Jaydeep Karandikar
- Jian Chen
- Peeyush Nandwana
- Rangasayee Kannan
- Ryan Dehoff
- Scott Smith
- Sumit Bahl
- Wei Zhang
- Adam Stevens
- Akash Jag Prasad
- Alice Perrin
- Andres Marquez Rossy
- Brian Gibson
- Bryan Lim
- Calen Kimmell
- Christopher Fancher
- Dali Wang
- Dean T Pierce
- Emma Betters
- Gerry Knapp
- Gordon Robertson
- Greg Corson
- Jay Reynolds
- Jeff Brookins
- Jesse Heineman
- Jiheon Jun
- John Potter
- Josh B Harbin
- Jovid Rakhmonov
- Nicholas Richter
- Peter Wang
- Priyanshi Agrawal
- Roger G Miller
- Sarah Graham
- Sudarsanam Babu
- Sunyong Kwon
- Tomas Grejtak
- Tony L Schmitz
- Vladimir Orlyanchik
- 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.

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

Distortion generated during additive manufacturing of metallic components affect the build as well as the baseplate geometries. These distortions are significant enough to disqualify components for functional purposes.

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.

For additive manufacturing of large-scale parts, significant distortion can result from residual stresses during deposition and cooling. This can result in part scraps if the final part geometry is not contained in the additively manufactured preform.