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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. 

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.

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.

We present the design, assembly and demonstration of functionality for a new custom integrated robotics-based automated soil sampling technology as part of a larger vision for future edge computing- and AI- enabled bioenergy field monitoring and management technologies called

In additive manufacturing large stresses are induced in the build plate and part interface. A result of these stresses are deformations in the build plate and final component.

Materials produced via additive manufacturing, or 3D printing, can experience significant residual stress, distortion and cracking, negatively impacting the manufacturing process.

Quantifying tool wear is historically challenging task due to variable human interpretation.  This capture system will allow for an entire side and the complete end of the cutting tool to be analyzed.