1 - 9 of 9 Results
ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.
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.
Among the methods for point source carbon capture, the absorption of CO2 using aqueous amines (namely MEA) from the post-combustion gas stream is currently considered the most promising.
Sensing of additive manufacturing processes promises to facilitate detailed quality inspection at scales that have seldom been seen in traditional manufacturing processes.
Simurgh revolutionizes industrial CT imaging with AI, enhancing speed and accuracy in nondestructive testing for complex parts, reducing costs.
An innovative low-cost system for in-situ monitoring of strain and temperature during directed energy deposition.
This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.
This invention is about a multifunctional structured packing device that can simultaneously facilitate heat and mass transfer in packed distillation, absorption, and liquid extraction columns, as well as in multiphase reactors.