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ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements. 

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.

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.

V-Cr-Ti alloys have been proposed as candidate structural materials in fusion reactor blanket concepts with operation temperatures greater than that for reduced activation ferritic martensitic steels (RAFMs).

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.