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Researcher
- Chris Tyler
- Beth L Armstrong
- Justin West
- Ritin Mathews
- Jun Qu
- Yong Chae Lim
- Zhili Feng
- Alex Plotkowski
- Amit Shyam
- Brian Post
- Corson Cramer
- David Olvera Trejo
- J.R. R Matheson
- James A Haynes
- Jaydeep Karandikar
- Jian Chen
- Meghan Lamm
- Rangasayee Kannan
- Scott Smith
- Steve Bullock
- Sumit Bahl
- Tomas Grejtak
- Wei Zhang
- Adam Stevens
- Akash Jag Prasad
- Alice Perrin
- Ben Lamm
- Brian Gibson
- Bryan Lim
- Calen Kimmell
- Christopher Ledford
- Dali Wang
- David J Mitchell
- Emma Betters
- Ethan Self
- Gabriel Veith
- Gerry Knapp
- Greg Corson
- James Klett
- Jesse Heineman
- Jiheon Jun
- John Potter
- Jordan Wright
- Josh B Harbin
- Jovid Rakhmonov
- Khryslyn G Araño
- Marm Dixit
- Matthew S Chambers
- Michael Kirka
- Nancy Dudney
- Nicholas Richter
- Peeyush Nandwana
- Priyanshi Agrawal
- Roger G Miller
- Ryan Dehoff
- Sarah Graham
- Sergiy Kalnaus
- Shajjad Chowdhury
- Sudarsanam Babu
- Sunyong Kwon
- Tolga Aytug
- Tony L Schmitz
- Trevor Aguirre
- 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.

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.

In additive manufacturing large stresses are induced in the build plate and part interface. A result of these stresses are deformations in the build plate and final component.