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- Femi Omitaomu
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- Haowen Xu
- Harper Jordan
- Isha Bhandari
- Jaswinder Sharma
- Jay Reynolds
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- Jiheon Jun
- Joel Asiamah
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- Roger G Miller
- Sarah Graham
- Scott Smith
- Sheng Dai
- Steven Guzorek
- Sunyong Kwon
- Tomas Grejtak
- Varisara Tansakul
- Vlastimil Kunc
- William Carter
- William Peter
- Ying Yang
- Yiyu Wang
- Yukinori Yamamoto

The eDICEML digital twin is proposed which emulates networks and hosts of an instrument-computing ecosystem. It runs natively on an ecosystem’s host or as a portable virtual machine.

Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.

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.

This manufacturing method uses multifunctional materials distributed volumetrically to generate a stiffness-based architecture, where continuous surfaces can be created from flat, rapidly produced geometries.

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

We developed and incorporated two innovative mPET/Cu and mPET/Al foils as current collectors in LIBs to enhance cell energy density under XFC conditions.

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.