Oak Ridge National Laboratory researchers, in collaboration with Cincinnati Inc., demonstrated the potential for using multimaterials and recycled composites in large-scale applications by 3D printing a mold that replicated a single facet of a
The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.
Oak Ridge National Laboratory researchers combined additive manufacturing with conventional compression molding to produce high-performance thermoplastic composites reinforced with short carbon fibers.
Six scientists at the Department of Energy’s Oak Ridge National Laboratory were named Battelle Distinguished Inventors, in recognition of obtaining 14 or more patents during their careers at the lab.
ORNL has added 10 virtual tours to its campus map, each with multiple views to show floor plans, rotating dollhouse views and 360-degree navigation. As a user travels through a map, pop-out informational windows deliver facts, videos, graphics and links to other related content.
ORNL and Department of Energy officials dedicated the launch of two clean energy research initiatives that focus on the recycling and recovery of advanced manufacturing materials and on connected and
Oak Ridge National Laboratory researchers have developed artificial intelligence software for powder bed 3D printers that assesses the quality of parts in real time, without the need for expensive characterization equipment.
Oak Ridge National Laboratory researchers have developed a machine learning model that could help predict the impact pandemics such as COVID-19 have on fuel demand in the United States.
From materials science and earth system modeling to quantum information science and cybersecurity, experts in many fields run simulations and conduct experiments to collect the abundance of data necessary for scientific progress.
Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.