Accelerating 3D Printing with Deep Learning: X-Ray CT Inspection of Metal Parts Gets Faster and More Accurate

Deep learning improves the speed and accuracy of X-ray CT inspections for 3D-printed components
The Department of Energy’s Oak Ridge National Laboratory has developed a new deep-learning framework that speeds up the inspection of metal parts made using additive manufacturing, and increases the accuracy of results. Reduced costs in time, labor and maintenance, as well as energy, are expected to increase the expansion of 3D printing, or additive manufacturing.

Amir Ziabari, ORNL’s lead researcher, said that the scan speed reduced costs by a significant amount. The quality of the scans is better, which makes the analysis much easier.

The framework has already been incorporated into the software that ZEISS uses in its machines at DOE’s Manufacturing Demonstration Facility, ORNL. This facility is where companies hone their 3D printing methods.

Source:
https://techxplore.com/news/2022-10-deep-x-ray-ct-3d-printed-faster.html