Video: 3 Roles for Computer Vision in Metal 3D Printing
Carnegie Mellon University professor Elizabeth Holm explains three ways computer vision can advance powder-bed metal 3D printing.
.jpg;width=70;height=70;mode=crop;format=webp)
In additive manufacturing, the microstructure of a part is critical to that part’s properties, performance and quality. The better microstructure can be understood and controlled, the more repeatable the AM process becomes. Research currently underway at Carnegie Mellon University (CMU) is exploring how computer vision—the use of computational algorithms to sense visual information—could be applied to advance understanding of microstructures in powder-bed metal 3D printing.
In the video below, Elizabeth Holm, professor of materials science and engineering at CMU, shares three applications for computer vision in AM materials, process control and quality control. Learn more about Holm’s research in applying computer vision and machine learning to metal powders.
Related Content
-
How Norsk Titanium Is Scaling Up AM Production — and Employment — in New York State
New opportunities for part production via the company’s forging-like additive process are coming from the aerospace industry as well as a different sector, the semiconductor industry.
-
Postprocessing Steps and Costs for Metal 3D Printing
When your metal part is done 3D printing, you just pull it out of the machine and start using it, right? Not exactly.
-
3D Printed NASA Thrust Chamber Assembly Combines Two Metal Processes: The Cool Parts Show #71
Laser powder bed fusion and directed energy deposition combine for an integrated multimetal rocket propulsion system that will save cost and time for NASA. The Cool Parts Show visits NASA’s Marshall Space Flight Center.
