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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JANUARY 2018 Additive Manufacturing FEATURE / Materials 30 When tested using the reserved samples, the computer vision system had an overall accuracy of about 95 percent, comparable to the training accuracy. The same materials were misclassified in this test as a result of similar particle morphologies and particle size distribution. Two others were misclassified because of outlier test images, pointing to fur- ther need for statistically representative image sets. The Human Factor Though the vision system did make errors in testing, it far outperformed humans faced with the same task, says Holm. In some informal testing where humans were asked to sort powder images, subjects averaged just over 50 percent accuracy. Humans are good at picking individual features out of pictures, but not at determining subtle statistical differences, she explains. And a given micrograph could contain thousands of interest points that a human might identify as significant, but that are time-consuming to analyze manually. With machine vision, these features can be analyzed quickly and objectively, no human judgment involved. But, Holm is quick to note that the purpose of her research is not to remove humans from the qualification process. Instead, this application of machine learning has the potential to relieve humans of repetitive work that doesn't require their expertise. "Our goal is not to eliminate the human; it's to empower the human to do something that requires interesting intellec- tual activity," Holm says. "Instead of an expert looking for an anomalous particle, we can let the computer do that. And then we can ask the expert, 'What do we do about it?' That's a much more interesting question." In addition to alleviating the work of classifying materials, having a comprehensive fingerprint of a batch of powder would enable humans to make smarter decisions and build better parts. Manufacturers could easily measure how closely a new batch of powder resembles previous batches or monitor the changes in a recycled batch, and connect powder fingerprints to 3D printing results. If a powder fingerprint is different than expected, it might be possible to change process parameters to compensate. Or, there might be ways of changing the powder itself (sieving to remove fines, for instance) to bring it into a qualified state. To make that happen, "What we need—desperately—is informa- tion that correlates the powder to outcomes," Holm says. How far might machine learning go in additive manufactur- ing? According to Holm, the two are a natural pair. "In additive manufacturing, we're in a situation where, by the nature of the process itself, we are going to be given a lot of data," she says. "What machine learning is really good at is taking data and making some sense of it—finding correlation and trends and directions." The application of machine learning will be key to raw ma- terial classification for additive manufacturing but also to the process control feedback loop, much in the way that autono- mous vehicles learn from every drive they take. According to Holm, "That is our goal for additive as well: that the next build will be informed by all the builds before it." Fig. 3. The computer vision system uses a three-part process to identify points of interest (a, b), match them to the most similar "visual word" (c) and construct a "fingerprint" for each sample. Thirty-two visual words (d) were identified across the powder samples, representing features such as spherical particles, a neck between particles, a cluster of particles or surface texture, for example. a b c d

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