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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.
How fast is a vehicle traveling? For different reasons, this basic question is of interest to other motorists, insurance companies, law enforcement, traffic planners, and security personnel. Solutions to this measurement problem suffer from a number of constraints.
System and method for part porosity monitoring of additively manufactured components using machining
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.
ORNL has developed a large area thermal neutron detector based on 6LiF/ZnS(Ag) scintillator coupled with wavelength shifting fibers. The detector uses resistive charge divider-based position encoding.
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.
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.