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 / Metal Additive Manufacturing 34 scientists on staff like Bill Carter, who co-wrote a research article in 1992 about how additive manufac- turing will be standard practice, or Marshall Jones, who at 76 still works at the Global Research Center after a career developing and perfecting high-powered lasers, you may fairly be considered ahead of the curve. Still, machine learning for additive is a new application within a new field, and the work being done at the GRC reflects that. And while you could say that the use of GE's Predix Edge and Cloud systems won't cost GE Additive anything since the technology is already in use for wind and gas tur- bines, MRI machines and other GE assets, the data that will inform those systems still needs to be generated. In other words, GE is performing the same kinds of tests that you can read about elsewhere. The big difference, of course, is that it's GE, and GE has a very deep bench. 100 Percent Yield Brent Brunell, the Edge Mission Leader at Global Research, leads the team that works across GE's industrial businesses to secure and connect the company's assets from the Edge to the Predix Cloud. Connecting additive machines—preparing them to access and interact with digital libraries of additive information and AI-driven decision making—is also a key focus of this mission. During my visit, members of GE's Additive team at the GRC demonstrated their side of the equation—stocking the library itself. As the additive materials mission leader at GRC, Joseph Vinciquerra helps lead the Additive Research Lab team respon- sible for populating a digital library of additive build parameters that will reside in the Predix Cloud. These parameters, along with those collected from Concept Laser machines at the facility, will be analyzed through machine learning to inform additive machine operators during their build operations. The goal, Vin- ciquerra says, is to achieve "100 percent yield," a state of additive perfection in which every build is without flaw. To accomplish this, Vinciquerra and his team members, which during my visit included Laura Dial, senior engineer, and Scott Oppenheimer, process engineer, print simple geo- metric shapes such as metal test bars and rods. High-resolution cameras record each layer of the build, catching flaws and im- perfections that would be imperceptible to the human eye. CT scans then record any flaws within the finished part, and all of that data is uploaded to the Predix system, which is tasked with using machine-learning algorithms to correlate those flaws to conditions on the powder bed that existed at the moment the flawed layer was produced. These kinds of tests are run time and time again as a means of training the system. But the ultimate goal is larger, and more complex. Vinciquerra and his team, along with scientists and engi- neers from GE's laser group, the computational fluid dynamics group, the materials group and others, are setting out to create a closed-loop system with ingrained defect-spotting capabilities that corrects flaws and potential flaws in real time. If the powder layer is slightly ridged, and if that ridged condition is known to result in unfused powder, for example, the printer will pause to adjust the layer before continuing. "The idea is that the machine has a compensation strategy based on what the computer vision sees," Vinciquerra says. "That's the long-term goal here." In other words, GE's ultimate goal is to operationalize the "see, think, do" closed-loop system for additive manufacturing. To illustrate an example of the tests being conducted to popu- late this additive parameter library, Oppenheimer shows me a test rig that has been set up to replicate the Concept Laser powder-bed system. This unit lacks a laser and powder feed system within the chamber, and contains only the build plate, the recoater blade and the powder bed. A camera is positioned downward at the top of the chamber, and LED lights are positioned at a low, oblique angle to the powder bed. Oppenheimer manually pours a measured amount of powder on the bed, and programs the recoater blade to spread the powder at a high speed. With all of the lab lights turned on, the layer looks even and smooth. But when the lights are turned off, and when Oppenheimer switches on the blue LED lights that are angled low on the powder bed, ripples along the powder suddenly reveal themselves. This is a direct result of running the recoater blade too quickly. "When we do this exercise on an actual machine," Vin- ciquerra says, "we're capturing images every time we recoat during a build. Those images get correlated to post-build A tower of computers at the GE Global Research Center. Standing at six-and-a-half-feet tall, this tower is populated with 148 machines, each run with the Predix Edge operating system that serves as GE's brain for controlling industrial machines. This same operating system also will be used to run power plants, wind farms, hospitals, manufacturing machines and oil fields.

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