Additive Manufacturing

JAN 2018

ADDITIVE MANUFACTURING is the magazine devoted to industrial applications of 3D printing and digital layering technology. We cover the promise and the challenges of this technology for making functional tooling and end-use production parts.

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Page 29 of 43

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.

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