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 34 of 43

AM / Machine Learning Ecosystem 33 This context is important not as an illustration of GE's size and scope, but for understanding where machine learning fits into the company's business strategy, and how GE is laying the groundwork for it today. It all begins with what GE calls Predix. In short, Predix is GE's operating system for the industrial in- ternet. It has been open and available to outside companies since 2015, and is the core digital platform on which data from industrial assets—from oil pumps to locomotives to wind turbines and now to 3D printers—resides. It is also where data processing and analy- sis take place, either in the cloud or on a computer control system located on or near the asset itself. At GE, the latter configuration is referred to as the Predix Edge. (The computations and analysis take place locally, at the "edge" of the network.) To understand how these platforms and capabilities are used at GE today, and how they are beginning to operate on Concept Laser machines, let's illustrate this digital ecosystem using a long-tested GE asset as an example: a gas turbine engine for a power plant. The main characteristic that drives the profitability of a gas turbine is reliability. At a typical electric power plant, service outages for a single gas turbine are typically scheduled when the weather is neither too hot nor too cold—a condition that minimizes the amount of electricity that will be lost during the outage. In other words, the service outage is a fixed event, and the operator wants to use up all of the life in the turbine until that scheduled outage. The operator also wants the turbine to run as efficiently as it can until that event. The hotter the tur- bine runs, the more overall efficiency the operator gains from it. Of course, the hotter it runs, the faster its life degrades. But if you have sophisticated controls acting as the brain of the turbine, you can ask those controls to perform three es- sential tasks that can inform decisions about when to schedule the outage, how hot to run the turbine and other variables that may affect your decision. In short, you can ask the controls to see, think and do. Sensors located on the turbine can collect information (see); the CPU can process the information and compare it to how the machine should be behaving (think); and then send signals to the actuators on the machine that affect motor speed, torque, and so on (do). The data from the local controls can then be combined with data in the cloud that has been collected from other gas turbines around the world. Machine learning now can use all of that data to inform decisions, such as: Should the turbine burn hotter or cooler? Should it burn a little bit below baseload, or should it be over-fired to squeeze out more electricity? What is the current price of electricity? What is the weather forecast? What are the other turbines doing? How much life does the turbine have left? In this example, machine learning and the digital platform on which it resides are key components. But so is the industry domain knowledge—the engineers who understand the materials of the turbine blade, and who understand creep and spallation and degradation. It's this same combination of processing power, ma- chine functionality and industry domain expertise that GE says it's bringing to the field of additive manufacturing. The key difference, of course, is that additive technologies are nascent, especially in comparison to gas turbines or locomotives. But when you have A test powder-bed machine shown under ambient lights at GE's Global Research Center. 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 photo of the same test rig illuminated with red and blue LEDs. The low, oblique angles of the lights reveal hidden aspects of the powder layer, such as streaking or ridging. Flaws correlated to those conditions can be stored and used to make AI-driven decisions.

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