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 30 of 43

AM / Machine Learning 29 a b c d f g h comparison to some established baseline. The ability to check a new batch of powder against previous batches would enable manufactures to more quickly qualify materials and more easily monitor changes. It may even be possible to associate a particular powder fingerprint with a certain behavior in the 3D printer and better predict (and adjust for) build outcomes in the future. Teaching the Machine To develop the powder characterization system, the team worked with eight different gas-atomized metal powders. Five of these materials were supplied by EOS and intended for use in the company's metal 3D printing systems: Al-EOS, In-EOS, MS-EOS, SS-EOS and Ti64-EOS. The other three samples were Ti-6Al-4V intended for use in Arcam systems; one of the batches was procured from Arcam while the other two were obtained from two different suppliers. To gather samples of each powder, researchers shook each container (to prevent sample bias from powder settling) and then gathered a small amount of powder with a spatula. A thin layer of the powder was blown over double-sided carbon tape, and then the sample was cleaned with pressurized air to remove any loose parti- cles. Researchers then took images of each sample using a microscope. A representative micrograph of each of the eight samples can be seen in Figure 2. Researchers then ad- justed the magnification of each micrograph so that the particles would appear to be similar in size across samples, and also erased the background of the images so that the com- puter would focus only on the powder itself. Removing these variables helps to prevent the system from being distracted or focusing on the wrong variables, Holm explains. "The computer learns, but we don't always know what the computer learns," Holm says. "We want to try and pre- vent it from making distinctions based on the wrong data." From these edited images, the vision system learned to characterize each of the samples in a three-part process (see Figure 3 on page 30). First, the computer decides what to look at by identifying interest points, or visual features, in each image. Next, it numerically characterizes each of these features by associating them with the most similar "visual word"—a distinctive feature such as spherical particles or necks between particles. Thirty-two of these visual words were identified across the samples. Finally, the system groups the feature descriptors together to create a distinct "finger- print" for each material. This fingerprint offers a more complete image of the powder than could be gathered manually. Measuring particle size distribution is fairly easy, explains Holm, but previously there has not been a good way to measure or capture other, more qualitative aspects of powders such as surface roughness. These qualitative characteristics influence material flow, spreadability and other factors that affect the final build. The team withheld one sample of each of the eight powders to be used for testing, and used the remaining samples as a training set for machine learning. When working with the training set, the vision system had a validation accuracy of 96.5 ±2.5 percent. Similarities between the MS-EOS and In-EOS powders and between the Ti64#1 and Ti64#2 powders caused the system to misclassify some samples. Fig. 2. Researchers took multiple samples from each of eight different materials for metal additive manufacturing to first teach and then test the machine learning system. These micrographs depict representative samples for each of the eight powders: (a) Al-EOS, (b) In-EOS, (c) MS-EOS, (d) SS-EOS, (e) Ti64-#1, (f) Ti64-#2, (g) Ti64-#3 and (h) Ti64-EOS. e

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