Jun 7, 2017 | By Benedict

Researchers from Carnegie Mellon University's College of Engineering have developed machine vision technology that can autonomously identify and sort different kinds of metal 3D printing powders with an accuracy of more than 95 percent. The technology could be commonplace within five years.

Carnegie Mellon researchers tested their machine vision tech on eight metal powders

Those in the business of manufacturing know that all metal parts, 3D printed or otherwise, need to undergo rigorous testing to ensure their quality and usability. And on the whole, this is a good thing: it would be disastrous for both supplier and customer if a functional part fell short of its expected standard.

But in additive manufacturing, there is a huge pressure on suppliers to get their 3D printed metal parts out and ready as soon as possible. After all, speed is seen as one of the technology’s biggest selling points; remove the element of speed, and customers may resort to other options.

Confronted with the slow testing of 3D printed metal parts, a team of researchers at Carnegie Mellon University's College of Engineering decided to develop a new kind of technology that could be used to massively speed up and improve the testing of 3D printed parts.

In a paper titled “Computer vision and machine vision for autonomous characterization of AM powder feedstocks,” which has been published in the Journal of the Minerals, Metals, and Materials Society, the researchers explain how their new machine vision technology can autonomously identify and sort metal 3D printing powder types with an accuracy of more than 95 percent.

They say that this powder-identifying ability could actually reduce the need for much of the physical testing that 3D printed parts are generally subjected to once they are printed.

Machine vision technology could reduce the need for destructive testing of 3D printed parts

(Image: Neil Richmond)

“In traditional manufacturing, parts are often qualified through destructive testing,” explained Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon, and research lead for the study. “A company might produce multiple parts and physically test them to see how they hold up to stress and fatigue.”

But Holm thinks that, by accurately sorting the powders that go into the 3D printer, some of that destructive testing becomes redundant. She says that destructive testing “costs a lot of time and money, so it should be avoided in additive manufacturing in order to preserve the on-demand nature of 3D printing,” adding that her research looks at “new qualification concepts like machine learning to guarantee successful 3D printed builds.”

The machine learning in question involves training a computer to identify and sort powders without manual supervision. This kind of computer can then see whether or not a metal powder has the microstructural qualities—strength, fatigue life, toughness, and so on—required by a part. If it does, it is much less likely to break or malfunction once 3D printed.

Holm and her research team tested their machine vision powder-sorting system on eight different commercial feedstock powders, and found that their system captures more about metal 3D printing powder than normal manual measurement is able to.

The system can even recognize many different features about the powders: how big its particles are, how the particles group together, the surface roughness of the particles, and the shape of them too. And amazingly, the computer is actually better than trained humans at differentiating powders.

"Importantly, the machine vision approach is autonomous, objective, and repeatable,” Holm concludes. “This type of standardization is necessary to advance quality assurance in the field.”

The researchers believe their work holds promise for future research in autonomous microstructural analysis. And if it makes metal 3D printing even faster, that can’t be a bad thing.



Posted in 3D Printing Technology



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