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 24 of 43

AM / Where AM Meets AI 23 spot size, pass overlap, composition of the powder, melt point and boiling point of various powder constituents, powder flowability, how many times the powder has been recycled, the percentage of contaminants, the speed of the recoater blade, the thickness of the part at any location, the resulting microstructure, the pressure of the shielding gas..." and his list trailed off at this point, not because he couldn't think of more, but because he was beginning to think of factors too subtle to name concisely within a list like this. And powder-bed is just one method of making additive parts. In short, in any AM build, there is a lot going on. Which is why, to date, the successful users of additive man- ufacturing have dealt with the range of significant variables essentially through brute force—through extensive trial and error to get to a process that works. Proceeding through a long series of failures, they find a process that can reliably produce their part, then they lock in on the process for that part in a given material on a particular machine (and they frequently keep the details of this hard-won process secret). But the extensive physical trial and error is not the way A graphic interface draws on the findings of machine learning so far. Each coordinate position represents a different combination of build parameters. (Hovering over any one reveals the complete list.) The dots' different colors correspond to different predict- ed defect sizes for that parameter set. The presence of a dot indicates the model can make a confident predication in that area. More data resulting from more builds will be necessary to add dots to this grid, not to mention to expand the array of predictive grids like this. Shown in profile is the ADAPT Center's Branden Kappes. forward, Kappes says. The way forward instead is to understand, to know in advance how the many variables do interplay. Or more specifically, to know in ad- vance the answer to this question: For the part I want to build, for the material structure best suited to the function I want the part to perform, what are the machine parameters and the process choices that can get me this result with little or even no trial and error? The variables figuring into that question are certainly too numerous to understand from experimentation alone, and hopefully the choice of materials will eventually be too numer- ous as well. That is why, importantly, the equipment in the ADAPT Center represents only a portion of the re- sources vital to the work this center has undertaken. An equally significant portion consists of mathematical resources. Machine learning is being used to extract the important findings from the data being gathered in the ADAPT Center's work. And machine learning is guiding the research itself, determining what addi- tive builds and what measurements are truly necessary to the ongoing refinement of this model so that, for example, attaining mastery over a single AM material such as Inconel 718 can be the work of a year rather than the work of a decade or more. According to Kappes, we have not yet quite awakened to the extent to which big data will play an instrumental role in additive manufacturing. We will. And one long-term implication of this that he sees—since few companies could cultivate all the machine-learning- produced algorithms they might need acting independently, or even feed enough data on their own to maintain these algo- rithms—is that the very relationships between manufacturing companies will change. Additive is a process like no other that will produce a manufacturing space like no other. Cooperation will be key, he says. And in his corner of Colorado, with the founding and growth of the regional alliance that led to his center, he believes this evolution might have already begun. Too Big for Faustson That regional alliance, ADAPT, started with a question that was asked by a machine shop. Or, more accurately, two questions. The first question was: What capability will we need to have in five years in order to remain relevant and valuable to customers?

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