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)
Share
Read Next
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
-
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.
-
10 Important Developments in Additive Manufacturing Seen at Formnext 2022 (Includes Video)
The leading trade show dedicated to the advance of industrial 3D printing returned to the scale and energy not seen since before the pandemic. More ceramics, fewer supports structures and finding opportunities in wavelengths — these are just some of the AM advances notable at the show this year.
-
DMG MORI: Build Plate “Pucks” Cut Postprocessing Time by 80%
For spinal implants and other small 3D printed parts made through laser powder bed fusion, separate clampable units resting within the build plate provide for easy transfer to a CNC lathe.
