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ORNL’s additive manufacturing compression molding, or AMCM, technology can produce composite-based, lightweight finished parts for airplanes, drones or vehicles in minutes and could acclerate decarbonization for the automobile and aeropsace industries. 

An Oak Ridge National Laboratory-developed advanced manufacturing technology, AMCM, was recently licensed by Orbital Composites and enables the rapid production of composite-based components, which could accelerate the decarbonization of vehicles

3d prnited lunar rover wheel based on a NASA design

Researchers at the Department of Energy’s Oak Ridge National Laboratory, in collaboration with NASA, are taking additive manufacturing to the final frontier by 3D printing the same kind of wheel as the design used by NASA for its robotic lunar rover, demonstrating the technology for specialized parts needed for space exploration.

ORNL’s David Sholl is director of the new DOE Energy Earthshot Non-Equilibrium Energy Transfer for Efficient Reactions center to help decarbonize the industrial chemical industry. Credit: Genevieve Martin, ORNL/U.S. Dept. of Energy

ORNL has been selected to lead an Energy Earthshot Research Center, or EERC, focused on developing chemical processes that use sustainable methods instead of burning fossil fuels to radically reduce industrial greenhouse gas emissions to stem climate change and limit the crisis of a rapidly warming planet.
 

The Department of Energy’s Oak Ridge National Laboratory announced the establishment of its Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making. Credit: Rachel Green/ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory announced the establishment of the Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making.

Xiaohan Yang is using his expertise in synthetic biology and capabilities like the Advanced Plant Phenotyping Laboratory at Oak Ridge National Laboratory to accelerate the development of drought-tolerant, fast-growing bioenergy crops suited for conversion into clean jet fuels. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Scientist Xiaohan Yang’s research at the Department of Energy’s Oak Ridge National Laboratory focuses on transforming plants to make them better sources of renewable energy and carbon storage.

Attendees of SMC23 pose for their annual group photo in downtown Knoxville, TN.

ORNL hosted its annual Smoky Mountains Computational Sciences and Engineering Conference in person for the first time since the COVID-19 pandemic.

Benefit breakdown, 3D printed vs. wood molds

Oak Ridge National Laboratory researchers have conducted a comprehensive life cycle, cost and carbon emissions analysis on 3D-printed molds for precast concrete and determined the method is economically beneficial compared to conventional wood molds.

Oak Ridge National Laboratory entrance sign

The Department of Energy’s Office of Science has selected three ORNL research teams to receive funding through DOE’s new Biopreparedness Research Virtual Environment initiative.

The DEMAND single crystal diffractometer at the High Flux Isotope Reactor, or HFIR, is the latest neutron instrument at the Department of Energy’s Oak Ridge National Laboratory to be equipped with machine learning-assisted software, called ReTIA. Credit: Jeremy Rumsey/ORNL, U.S. Dept. of Energy

Neutron experiments can take days to complete, requiring researchers to work long shifts to monitor progress and make necessary adjustments. But thanks to advances in artificial intelligence and machine learning, experiments can now be done remotely and in half the time.

ZEISS Head of Additive Manufacturing Technology Claus Hermannstaedter, left, and ORNL Interim Associate Laboratory Director for Energy Science and Technology Rick Raines sign a licensing agreement that allows ORNL’s machine-learning algorithm, Simurgh, to be used for rapid evaluations of 3D-printed components with industrial X-ray computed tomography, or CT. Using machine learning in CT scanning is expected to reduce the time and cost of inspections of 3D-printed parts by more than ten times.

A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory and research partner ZEISS will enable industrial X-ray computed tomography, or CT, to perform rapid evaluations of 3D-printed components using ORNL’s machine