Updated software improves slicing for large-format 3D printing
Each year, approximately 6 billion gallons of fuel are wasted as vehicles wait at stop lights or sit in dense traffic with engines idling, according to US Department of Energy estimates.
As the second-leading cause of death in the United States, cancer is a public health crisis that afflicts nearly one in two people during their lifetime.
Peter Wang is focused on robotics and automation at the Department of Energy’s Manufacturing Demonstration Facility at ORNL, working on high-profile projects such as the MedUSA, a large-scale hybrid additive manufacturing machine.
A typhoon strikes an island in the Pacific Ocean, downing power lines and cell towers. An earthquake hits a remote mountainous region, destroying structures and leaving no communication infrastructure behind.
Students often participate in internships and receive formal training in their chosen career fields during college, but some pursue professional development opportunities even earlier.
Artificial intelligence (AI) techniques have the potential to support medical decision-making, from diagnosing diseases to prescribing treatments. But to prioritize patient safety, researchers and practitioners must first ensure such methods are accurate.
Materials scientists, electrical engineers, computer scientists, and other members of the neuromorphic computing community from industry, academia, and government agencies gathered in downtown Knoxville July 23–25 to talk about what comes next in
In the shifting landscape of global manufacturing, American ingenuity is once again giving U.S companies an edge with radical productivity improvements as a result of advanced materials and robotic systems developed at the Department of Energy’s Manufacturing Demonstration Facility (MDF) at Oak Ridge National Laboratory.
When Scott Smith looks at a machine tool, he thinks not about what the powerful equipment used to shape metal can do – he’s imagining what it could do with the right added parts and strategies. As ORNL’s leader for a newly formed group, Machining and Machine Tool Research, Smith will have the opportunity to do just that.
Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.