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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. 

This technology can help to increase number of application areas of Wireless Power Transfer systems. It can be applied to consumer electronics, defense industry, automotive industry etc.

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.

A pressure burst feature has been designed and demonstrated for relieving potentially hazardous excess pressure within irradiation capsules used in the ORNL High Flux Isotope Reactor (HFIR).

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

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.

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.

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.

Faults in the power grid cause many problems that can result in catastrophic failures. Real-time fault detection in the power grid system is crucial to sustain the power systems' reliability, stability, and quality.