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 36 of 43

AM / Machine Learning Ecosystem 35 Helping companies increase through-put, save money, and improve quality for more than 65 years. Serving the additive manufacturing community with dry-blast and wet-blast cabinet solutions in standard equipment, many options, or engineered automated systems tailored to your application. Put the power of a ZERO Blast Cabinet to work in your production line – they are seriously industrial, come standard in various sizes, and are supported by experienced authorized distributors to guide your selection and work with you to achieve your production-enhancing goals. inspection. And we're using machine learning to correlate observations in a powder-bed recoating to abnormalities in the final product. Abnormalities like porosity, cracks, lack of fusion—the types of things that debit the material properties that we're after." Laura Dial is quick to point out that not every condition like this will result in a flaw, and that it's important to conduct these tests across a range of settings in order to make those de- terminations. "If you look just in the ambient light, not much pops out to the eye," Dial says. "But if you use the oblique angle lighting, you can very clearly see very, very small things in your build. These small things are not necessarily something that causes a problem, but to be able to track them and correlate them to what you see in a microstructure allows you to modify your process with a lot of confidence. To be more production ready. In other words, you can see a lot, and a lot of the things we see actually don't matter." Vinciquerra notes that the first order of his team's work is to train a machine-learning-based model that will allow the real-time imaging to predict the material properties at the end of a build. If the camera captures an image of streaking, pitting or any condition among the library of features that indicates a potential problem, a signal can be sent to the machine operator to stop the build and make a correction. But down the road, GE aims to incorporate the informatics that are being relayed from the image analysis into the control system of the machine. Using a machine-learning-based model, if the machine detects a flaw on a single recoat, it can make a fine adjustment on the next layer to fix it. Combined with the research being conduct- ed across laser, materials, computational fluid dynamics and other GE groups that are conducting similar tests, the work represents just a small portion of the resources being placed into machine learning here in Niskayuna. "At GE," Vinciquerra says, "we spend a tremendous amount of time asking, how do we master a broad set of material systems for additive? How do we understand them enough so we can build high-quality parts using the processes we have in powder-bed AM? Machine learning, AI-driven decision making with material science is a channel that we're forging specifically because of that challenge. There are so many different things to understand in additive today. What are the shortcuts that we can take to get there? We need to leverage these shortcuts, which in no way implies less rigor. It's just as rigorous as our traditional material science. It's just that we've got to do it faster." Photos provided by GE Global Research

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