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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.
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.
Manufacturing - Pressure - Suppliers - Metal - Parts
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.
Paper - Computer - Vision - Machine - Vision
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.
Manufacturing - Parts - Testing - Elizabeth - Holm
“In traditional manufacturing, parts are often qualified through destructive testing,” explained Elizabeth Holm, professor of materials science and engineering at Carnegie...
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