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Last updateMon, 11 Dec 2017 3am
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GE researchers work on making 3D printers smarter with computer vision and machine learning

Researchers at GE are building a system that could speed up the 3D printing process and eventually achieve ‘100 percent yield,’ where machines only produce good parts, beginning with the very first build.

Even though companies like GE already print parts for jet engines, additive manufacturing is still a young field. It can take days to weeks to print large parts such as a compressor blade. If something goes wrong near the end of the process, precious machine time and money could go to waste. 3D printing and other additive manufacturing methods print parts directly from a computer file. They can shape a component by fusing together thin layers of metal powder with a laser, for example. But even these highly advanced machines are prone to variability.

Scientists working at GE labs in New York have spent decades building computer vision systems that can study diseased tissue, and hunt for microscopic cracks in machine parts and other features often invisible to the naked eye. Now they are using their insights to improve the way 3D printers work.

Joseph Vinciquerra, who runs the Additive Research Lab at GE Global Research in Niskayuna, New York, indicates there are a number of culprits that can make the difference between a good build and a build that has sub-optimal properties. They include variation in the size of the powder particles as well as the complex dynamic of adding new powder layers, which can be as thin as a human hair.

“We know that things happen during the re-coating process that you cannot control mechanically. We also know that the more we reuse powder, the more opportunities exist for that powder to change and behave differently over time,” says Vinciquerra.

“We do a tremendous amount of work on additive powders to understand what characteristics lead to a good build,” says materials scientist Kate Gurnon, a member of the team. “We want to apply this automatically to the machines and, in real time, observe the dynamic behavior of the powder delivery to the build plate. In this way, we will have a better chance of getting to the 100 percent yield, faster.”

Artificial intelligence and machine learning can turn 3D printers into their own inspectors

The team starts by printing simple geometric shapes like flat bars and cylinders. They use high-resolution cameras to film every layer and record streaks, pits, divots and other patterns in the powder practically invisible to humans. Next, they run the samples through a powerful CT scanner and hunt for flaws. All of the data is stored in computer memory and a proprietary machine-learning algorithm correlates defects revealed by the scanners with powder patterns recorded on the particular layer.

When the computer vision spots a familiar streak that it knows will lead to cavities, for example, the printer can automatically add more power or speed to the laser beam to adjust, or change the thickness of the next layer.

“Computer vision can be used to find things we either can’t see or may not know to look for. The more often you do it, the smarter the system gets. The computer vision alone will eventually have enough training to tell us whether we are going to have a problem. By eliminating the need to inspect parts after they’re completely built, we can shave days, even weeks off the entire manufacturing process and lead to a breakthrough in productivity,” says Vinciquerra.
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