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Researcher
- Ryan Dehoff
- Vincent Paquit
- Hongbin Sun
- Michael Kirka
- Adam Stevens
- Ahmed Hassen
- Akash Jag Prasad
- Alex Plotkowski
- Alice Perrin
- Amir K Ziabari
- Amit Shyam
- Andres Marquez Rossy
- Blane Fillingim
- Brian Post
- Calen Kimmell
- Canhai Lai
- Christopher Ledford
- Chris Tyler
- Clay Leach
- Costas Tsouris
- David Nuttall
- Ilias Belharouak
- James Haley
- James Parks II
- Jaydeep Karandikar
- Patxi Fernandez-Zelaia
- Peeyush Nandwana
- Philip Bingham
- Pradeep Ramuhalli
- Praveen Cheekatamarla
- Rangasayee Kannan
- Roger G Miller
- Ruhul Amin
- Sarah Graham
- Singanallur Venkatakrishnan
- Sudarsanam Babu
- Vipin Kumar
- Vishaldeep Sharma
- Vladimir Orlyanchik
- Vlastimil Kunc
- William Peter
- Yan-Ru Lin
- Ying Yang
- Yukinori Yamamoto
- Zackary Snow

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 invention presented here addresses key challenges associated with counterfeit refrigerants by ensuring safety, maintaining system performance, supporting environmental compliance, and mitigating health and legal risks.

Sensing of additive manufacturing processes promises to facilitate detailed quality inspection at scales that have seldom been seen in traditional manufacturing processes.

High strength, oxidation resistant refractory alloys are difficult to fabricate for commercial use in extreme environments.

Knowing the state of charge of lithium-ion batteries, used to power applications from electric vehicles to medical diagnostic equipment, is critical for long-term battery operation.

In manufacturing parts for industry using traditional molds and dies, about 70 percent to 80 percent of the time it takes to create a part is a result of a relatively slow cooling process.

This technology combines 3D printing and compression molding to produce high-strength, low-porosity composite articles.

Simurgh revolutionizes industrial CT imaging with AI, enhancing speed and accuracy in nondestructive testing for complex parts, reducing costs.

An innovative low-cost system for in-situ monitoring of strain and temperature during directed energy deposition.