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 26 of 43

AM / Where AM Meets AI 25 build parameters but also the ways combinations of parameters work together. Machine learning is the means of finding these effects and finding their causal relationships. According to Bryce Meredig, chief scientist and co-founder of machine learning soft- ware developer Citrine Informatics (a bit more on this company below), machine learning can be thought of as a way to train a computer using examples. Where a convention- al computer program executes a defined set of instructions, machine learning consists of algorithms that aim to find usable patterns in data. These patterns are used to build predictive models, which are incrementally refined through com- parisons with real data. In a problem in which the number of inputs and outputs number in the tens, human beings working by experiment can find these predictive relationships. But where the number of inputs and outputs number in the hundreds or thousands, machine learning offers a means of finding what those data reveal. Dr. Meredig refers to the capability using the term "artificial intelligence," but he uses the term as much to describe the capability's limitations as its power. The AI still needs "HI," the human intelligence, he says. AI or machine learning can take one billion possible relationships among a given data set and narrow them down to the ten or twenty that are potentially strong, but then human discernment and intuition are needed to determine which two or three of those ten or twenty are really worth exploring. The data and the findings resulting from analyzing the data in this way are already paying off. Here is an illustration of how that is playing out, and how the knowledge the ADAPT Center has developed can be used: Assume an ADAPT member needs to additively produce a component in Inconel 718, and one of the critical requirements is that the maximum allowable internal defect size is, say, 90 microns. A model based on the many Inconel 718 test pieces that have been analyzed so far can predict parts' maximum pore sizes for given sets of parameters. Indeed, the photo on page 23 shows the graphical interface for this prediction. The dots in the screen display indicate where the mathematical model's prediction is confident—that is, at which combinations of factors (including laser parameters, location on the build plate and orientation relative to the recoater blade) the model has a prediction that is statistically strong. For any outcome that is sought, the model might al- ready be able to offer the set of process parameters able to win that outcome. But more likely, the outcome needed for a criti- cal part will be so specific that the model as it stands will not be able to predict all the right inputs, yet will be able to with just a little further testing. Through machine learning, the existing data can be analyzed to determine precisely what further test parts are needed to make the predictive model more confident. That is, instead of a trial-and-error process involving anywhere from 20 to 50 iterations (that is, 20 to 50 builds of the part), the process likely can be proven with only three or four such itera- tions because the model can tell where the model itself is weak. This kind of analysis required expertise beyond what ADAPT possessed when it was founded. The additive alliance discov- ered it needed a computational partner—a partner for the machine learning or the AI. The partner the alliance members found is the previously mentioned Citrine Informatics. Based in Redwood City, California, this ADAPT member exemplifies how the alliance now extends past its Colorado roots. This firm is also a non-manufacturing member—except, as the very need to involve Citrine makes clear, additive expands the scope of disciplines needed to realize its promise. Machine learning A tensile test is performed on the specimen illuminated here. As this issue's cover photo shows, tensile specimens for the ADAPT Center are also printed with varied parameters including various build angles. The photo above captures another important piece of equipment as well, the X-ray diffractometer in the background.

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