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.
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
-
Video: 5 Things You Didn’t Know About Additive Manufacturing
AM is more than a means of making parts. Here are ideas about the reach that additive manufacturing will have, and the kinds of changes and advances that AM will enable.
-
The Race to Faster, Cheaper and Better AM for Aviation
An aviation industry partner called NIAR may be the largest institute you've never heard of. But through a new materials and process database for additive, it’s promising to help aviation manufacturers produce AM parts faster, cheaper and more reliably than ever before.
-
Overcoming the Challenges of Copper
A research organization in Germany is developing a laser beam source that operates with a green light, enabling selective laser melting of a copper alloy.