Carnegie Mellon develops machine vision autonomous system for metal 3D printing

3D Printing Industry | 6/8/2017 | Staff
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Research from Carnegie Mellon University’s (CMU) College of Engineering has developed an autonomous system for classifying the metal powders used for 3D printing. Using machine vision technology, the system can identify specific microstructures in the additive manufacturing metal powders with an accuracy of greater than 95%.

Metal powders are used in powder bed fusion 3D printers. Understanding the quality of the material is essential to the integrity of the resulting parts. The CMU engineers expect their system to be applied by the 3D printing industry within the next five years as part of the Carnegie Mellon University’s NextManufacturing Center aims.

Machine - Vision - Approach - Type - Standardization

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

The powder micrographs for each of the eight metal powders evaluated in the research project. Image via JOM.

CMU - Engineers - System - Computer - Microstructures

The CMU engineers developed the system by training a computer to identify microstructures in powder micrographs. As the research paper explains, “feature detection and description algorithms are applied to create a microstructural scale image representation that can be used to cluster, compare, and analyze powder micrographs.”

Following this, the powders can then be classified as to whether they possess sufficient qualities for strength, fatigue life, or toughness. The CMU researchers refer to the analyzed powders characteristics as microstructural fingerprints and believe certain fingerprints will bear well for certain applications.

Company - Metalysis - Flow - Metal - Powders

British company Metalysis demonstrating the flow of their metal powders. Photo via Metalysis.

Not only can this system facilitate classification and qualification of metal powders in order to ensure print quality, the technology can also support the recycling of metal powders. Since the microstructural fingerprints can then be reassessed to determine how far they have strayed from the original structure.

Manufacturing - Parts

In traditional manufacturing, parts are often...
(Excerpt) Read more at: 3D Printing Industry
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