1 - 10 of 41 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.
The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.
The use of biomass fiber reinforcement for polymer composite applications, like those in buildings or automotive, has expanded rapidly due to the low cost, high stiffness, and inherent renewability of these materials. Biomass are commonly disposed of as waste.
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
Creating a framework (method) for bots (agents) to autonomously, in real time, dynamically divide and execute a complex manufacturing (or any suitable) task in a collaborative, parallel-sequential way without required human interaction.
The incorporation of low embodied carbon building materials in the enclosure is increasing the fuel load for fire, increasing the demand for fire/flame retardants.