writes copy 24 Oct 2017

Metal 3D Printing with Machine Learning: GE Tells Us About Smarter Additive Manufacturing

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It’s no secret that global giant GE has a lot at stake as metal 3D printing makes headway as an advanced manufacturing technique in such part-critical industries as aerospace. Tight tolerances in applications that don’t allow for room for error, as well as heavy investment into metal additive manufacturing, have made advances in the technology a key area of focus for GE, evidenced by last year’s acquisitions of Concept Laser and Arcam, as well as the introduction of its 3D printing-centered GE Additive business. Particularly following a weak Q3 performance for the company as a whole, advanced technologies including the industrial internet and additive manufacturing represent bright spots for focused investment at the restructuring GE.

With the unveiling of the world’s largest laser-powder metal 3D printing system anticipated at formnext in just a few weeks, metal is definitely on GE’s mind — and the Additive Materials Lab at  GE Global Research  in Niskayuna, New York has been hard at work in advancing materials offerings and machine performance.

Vinciquerra (right) and Andy Deal, a metallurgist in the Additive Materials Lab, load sets of sample 3D printed metal parts in a vacuum oven for post-processing at GE Global Research. [Image: GE Global Research.]

When we checked in earlier this year,  Joe Vinciquerra,  Senior Principal Engineer and Additive Technology Platform Leader with GE Global Research and  Manager of the Additive Materials Lab, told us more about their approach to metal additive manufacturing materials as a ‘cookbook’ of information. Research has been ongoing at the facility, and some of the latest has been working to increase performance of both machine and material with an approach of greater intelligence — artificial intelligence, at that. Machine learning is coming into play at the Additive Materials Lab as the team work to enhance the process and quality of additive manufacturing, aiming to produce better parts with fewer quality hiccups along the way.

Machine learning allows for in-process detection of any areas of quality concern, enabling operators to ensure proper adjustment is made, limiting waste of time and materials. The ultimate goal is for a perfect score: 100 percent yield. Hitting this target would mean a perfect print, every time, with no wasted material and no failed prints. In manufacturing, this often remains a far-flung dream; with machine learning, a smarter system may bear witness to machines nearing the goal themselves, learning as they go. A digital twin comes into the picture as well, creating a simulated, predictive model.

GE Global Research and GE Additive scientists explain their approach to zero waste in this video from GE Reports:

To understand more of the work with a new proprietary algorithm, I turned to Vinciquerra for more detail. What exactly is the idea of 100 percent yield in terms of additive manufacturing, I wanted to know, and what would this look like in a metal system?

“The key to 100 percent yield is enabling the 3D metal machine itself to act as its own inspector,” he told me.

“We want to enable 100% visibility into each layer of a part build and train the machine over time to recognize any issues with the build itself.   And this is where GE's Digital Twins enter the picture. GE's Twins are living, learning models that become smarter and more capable just like people. They learn from experience, taking in new part build data and cataloging it, as they observe new and different scenarios. If they observe a part build going wrong in a  way that resembles something they've already seen before, the machine can flag it for an operator to respond to. The operator could respond by discontinuing the build or by making adjustments on the fly to correct it and keep going.  Further out, we envision the machine making these corrections on their own, without operator intervention.”

[Image: Avio Aero]

The idea of the digital twin is one we are hearing about more frequently, as simulations such as those created using  Dassault  Systèmes technologies allow for a deeper look at the exact mechanics and structure of a given build. In-process comparison of the real-world build taking place with the digital twin can immediately show warnings of any deviations from the plan. Digital twins factor into the smart inspection process in a few ways, as Vinciquerra explained.

“Part build data from the smart inspection process feeds into a digital twin, or digital model of the build itself. By continuously contrasting these data to our proprietary ‘gold standards,’ the digital twin is envisioned to recognize deviations early on that may signal physical abnormalities we would otherwise detect later in the manufacturing process by post-build inspection. Knowing earlier in the process that issues may arise allows us to compensate,” he said.