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Media Contacts
Craig Blue, Defense Manufacturing Program Director at the Department of Energy’s Oak Ridge National Laboratory, was recently elected to a two-year term on the Institute for Advanced Composites Manufacturing Innovation Consortium Council, a body of professionals from academia, state governments, and national laboratories that provides strategic direction and oversight to IACMI.
A new report published by ORNL assessed how advanced manufacturing and materials, such as 3D printing and novel component coatings, could offer solutions to modernize the existing fleet and design new approaches to hydropower.
Merlin Theodore is one of eight new board members announced by President Biden; she will join the 25-member board for a six-year term.
ORNL researchers have identified a mechanism in a 3D-printed alloy – termed “load shuffling” — that could enable the design of better-performing lightweight materials for vehicles.
The presence of minerals called ash in plants makes little difference to the fitness of new naturally derived compound materials designed for additive manufacturing, an Oak Ridge National Laboratory-led team found.
Oak Ridge National Laboratory researchers serendipitously discovered when they automated the beam of an electron microscope to precisely drill holes in the atomically thin lattice of graphene, the drilled holes closed up.
Eight ORNL scientists are among the world’s most highly cited researchers, according to a bibliometric analysis conducted by the scientific publication analytics firm Clarivate.
Oak Ridge National Laboratory scientists designed a recyclable polymer for carbon-fiber composites to enable circular manufacturing of parts that boost energy efficiency in automotive, wind power and aerospace applications.
Rama Vasudevan, a research scientist at the Department of Energy’s Oak Ridge National Laboratory, has been elected a Fellow of the American Physical Society, or APS. The honor recognizes members who have made significant contributions to physics and its application to science and technology.
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.