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 27 of 43

JANUARY 2018 Additive Manufacturing FEATURE / Metal Additive Manufacturing 26 is now being revealed to be a valuable manufacturing-related capability after all. Pooling Knowledge So far, the investigation in search of a predictive model for AM has focused on Inconel 718. And so far, test parts have been built on Faustson's Concept Laser machine. As a result, ADAPT members have access to a model that can describe the behavior of this alloy on this platform. The members are well ahead on any need for trial-and-error within this context, but only in this context. The model obviously needs to be bigger than this. Kappes says there will be other metals investigated this same way, and other machines. And just making the mod- el bigger will make it stronger. Seemingly fundamental differences in the machine and the material will provide the chance for the model to find commonalities, even generalities, in terms of process pa- rameter choices that extend from metal to metal and from machine to machine, he says. Already, the ADAPT Center has done some work studying AM parts made on the Sciaky machine employed by alliance member Lockheed Martin. This is a directed energy deposition machine (not powder- bed fusion like the Concept Laser) that uses an electron beam (not a laser). And yet, says Kappes, both machines are melting metal. Obviously the machines will differ in their responses to various inputs, but there will also be real power in discovering how their responses are the same—and how discoveries in the mathematical model around one platform might lend strength to the model around another. Big data will benefit additive manufacturing, he says, be- cause the AM process is inherently so complex. Indeed, big data will provide a vital advantage to AM users able to tap into that data, but the data sets truly big enough to be useful will only be able to come from groups of manufacturers pooling their knowledge. The result might well be that AM is most successfully performed by manufacturing commu- nities or networks that look very much like ADAPT, Kappes says—that is, very much like the alliance begun by Faustson Tool that allowed the center he now serves to be created. Thus, the ADAPT Center, with machine learning, is mod- eling how best to apply AM. But before that, by drawing on their contact lists, the founders of ADAPT might have been doing the same thing. They are demonstrating—by means of their cooperation—what may prove to be the best model by which additive manufacturing can advance.

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