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Researcher
- Diana E Hun
- Chris Tyler
- Justin West
- Som Shrestha
- Philip Boudreaux
- Ritin Mathews
- Tomonori Saito
- Bryan Maldonado Puente
- Nolan Hayes
- Yong Chae Lim
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- J.R. R Matheson
- Jaydeep Karandikar
- Jian Chen
- Mahabir Bhandari
- Rangasayee Kannan
- Scott Smith
- Shiwanka Vidarshi Wanasinghe Wanasinghe Mudiyanselage
- Venugopal K Varma
- Wei Zhang
- Achutha Tamraparni
- Adam Aaron
- Adam Stevens
- Akash Jag Prasad
- Andre O Desjarlais
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- Calen Kimmell
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- Dali Wang
- Emma Betters
- Gina Accawi
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- Gurneesh Jatana
- Jesse Heineman
- Jiheon Jun
- John Potter
- Josh B Harbin
- Karen Cortes Guzman
- Kuma Sumathipala
- Mark M Root
- Mengjia Tang
- Natasha Ghezawi
- Peeyush Nandwana
- Peter Wang
- Priyanshi Agrawal
- Roger G Miller
- Ryan Dehoff
- Sarah Graham
- Stephen M Killough
- Sudarsanam Babu
- Tomas Grejtak
- Tony L Schmitz
- Venkatakrishnan Singanallur Vaidyanathan
- Vladimir Orlyanchik
- William Peter
- Yifang Liu
- Yiyu Wang
- Yukinori Yamamoto
- Zhenglai Shen

We’ve developed a more cost-effective cable driven robot system for installing prefabricated panelized building envelopes. Traditional cable robots use eight cables, which require extra support structures, making setup complex and expensive.

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

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.

We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.

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.