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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 technology will offer supportless DIW of complex structures using vinyl ester resin, facilitated by multidirectional 6 axis printing.

We have developed a novel extrusion-based 3D printing technique that can achieve a resolution of 0.51 mm layer thickness, and catalyst loading of 44% and 90.5% before and after drying, respectively.

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.

The technologies provide additively manufactured thermal protection system.

Wind turbine blades face a harsh environment in which erosion of the leading edge is a major factor for in-use maintenance. Current industrial practices to address this leading edge erosion are replacement of reinforcing materials upon significant damage infliction.

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

Through utilizing a two function splice we can increase the splice strength for opposing tows.

Contact:

To learn more about this technology, email partnerships@ornl.gov or call 865-574-1051.