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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.
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.
High strength, oxidation resistant refractory alloys are difficult to fabricate for commercial use in extreme environments.
In manufacturing parts for industry using traditional molds and dies, about 70 percent to 80 percent of the time it takes to create a part is a result of a relatively slow cooling process.
This technology combines 3D printing and compression molding to produce high-strength, low-porosity composite articles.
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.