JANUARY 2018 Additive Manufacturing
FEATURE
/
Materials
28
By Stephanie Hendrixson
Machine Learning Applied
to Metal Powder
A computer vision system that has
learned to classify metal powders
could speed material qualification
and advance machine learning in
additive manufacturing.
In powder-bed additive manufacturing (AM), the
quality of the part begins with the quality of the pow-
der. Methods for characterizing AM powder feedstock
have typically relied on direct measurements of material
properties such as particle size and aspect ratio distribu-
tions. However, a team of researchers at Carnegie Mellon
University, led by professor of materials science and engineer-
ing Elizabeth Holm, has developed an automated method that is
said to identify metal AM powders with 95 percent accuracy.
Using micrograph images of sample powders, Holm's team has
been able to teach a computer vision system to characterize material
batches based on their qualitative, as well as quantitative, properties.
A paper published in the Journal of the Minerals, Metals and Materials
Society (JOM) details the system's development and initial testing based on
the characterization of eight metal powders.
The point of this classification exercise is not so much to demonstrate
that the system can tell different powders apart, but that it could enable
Fig. 1. A team led by Elizabeth Holm (right), professor of
materials science and engineering at Carnegie Mellon
University, has developed a computer vision system
that can classify metal powder with 95 percent ac-
curacy. This application of machine learning could
help additive manufacturers quickly analyze and
qualify powder for 3D printing.