ORNL, ZEISS sign licensing agreement for technology to evaluate 3D-printed components
The agreement is part of a five-year research collaboration between ORNL and ZEISS, supported by the U.S. Department of Energy’s Advanced Materials and Manufacturing Technologies Office in addition to a Technology Commercialization Fund award.
A licensing agreement between Oak Ridge National Laboratory (ORNL) and research partner ZEISS will enable industrial X-ray computed tomography (CT) to perform rapid evaluations of 3D-printed components using ORNL’s machine learning algorithm, Simurgh. Incorporating machine learning into CT scanning is expected to reduce the time and cost of inspections by more than 10x while improving quality.
The licensing is part of a five-year research collaboration between ORNL and ZEISS, supported by the U.S. Department of Energy’s Advanced Materials and Manufacturing Technologies Office in addition to a Technology Commercialization Fund award. The research has focused on using CT scanners and other measuring devices to see inside 3D-printed parts to check for cracks and other defects during the manufacturing process.
According to the news release announcing the agreement, ORNL noted that one of the challenges to broader adoption of 3D printing is how to examine a part to ensure it contains no hidden flaws that could affect performance. Nearly all products have some level of material flaws; however, traditional manufacturing techniques are backed up by decades of experience that let manufacturers know what to expect from items they make using casting, forging, machining, and similar techniques. But the unique nature of 3D printing requires a different approach to examining parts, using advanced characterization techniques to understand the distinct features inside an item.
ZEISS Industrial Quality Solutions is a leading manufacturer of multidimensional metrology solutions. These include coordinate measuring machines, optical and multisensor systems, 3D X-ray metrology, and microscopy systems for industrial quality assurance.