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Machine Learning Cuts 3D-printed Component Scanning Time by Ten-fold

A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory (ORNL) 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 learning algorithm, Simurgh. Incorporating machine learning into CT scanning is expected to reduce the time and cost of inspections by more than ten times while improving quality.

The licensing is part of a five-year research collaboration between ORNL and Zeiss, supported by DOE’s Advanced Materials and Manufacturing Technologies Office and 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.

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.

That’s where CT comes into play.

“CT is a standard nondestructive technique used in a multitude of different industries to ensure the quality of the component that is being produced,” said ORNL researcher Amir Ziabari. “But CT is traditionally an expensive and time-consuming process. The challenge is how can we leverage what we know of physics and technology to speed up the CT process to allow it to be more broadly adopted by industry.”

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