Researchers at ORNL became the first to 3D-print large rotating steam turbine blades for generating energy in power plants.
Four scientists affiliated with ORNL were named Battelle Distinguished Inventors during the lab’s annual Innovation Awards on Dec. 1 in recognition of being granted 14 or more United States patents.
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
Yarom Polsky, director of the Manufacturing Science Division, or MSD, at the Department of Energy’s Oak Ridge National Laboratory, has been elected a Fellow of the American Society of Mechanical Engineers, or ASME.
Early experiments at the Department of Energy’s Oak Ridge National Laboratory have revealed significant benefits to a dry battery manufacturing process. This eliminates the use of solvents and is more affordable, while showing promise for delivering a battery that is durable, less weighed down by inactive elements, and able to maintain a high capacity after use.
Scientist-inventors from ORNL will present seven new technologies during the Technology Innovation Showcase on Friday, July 14, from 8 a.m.–4 p.m. at the Joint Institute for Computational Sciences on ORNL’s campus.
For more than 100 years, Magotteaux has provided grinding materials and castings for the mining, cement and aggregates industries. The company, based in Belgium, began its international expansion in 1968. Its second international plant has been a critical part of the Pulaski, Tennessee, economy since 1972.
On the grounds of the University of Maine’s Advanced Structures and Composites Center sits the nation’s first additively manufactured home made entirely from biobased materials - BioHome3D.
Laboratory Director Thomas Zacharia presented five Director’s Awards during Saturday night's annual Awards Night event hosted by UT-Battelle, which manages ORNL for the Department of Energy.
A new deep-learning framework developed at ORNL is speeding up the process of inspecting additively manufactured metal parts using X-ray computed tomography, or CT, while increasing the accuracy of the results. The reduced costs for time, labor, maintenance and energy are expected to accelerate expansion of additive manufacturing, or 3D printing.