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In additive manufacturing, the microstructure of a part is critical to that part’s properties, performance and quality. The better microstructure can be understood and controlled, the more repeatable the AM process becomes. Research currently underway at Carnegie Mellon University (CMU) is exploring how computer vision—the use of computational algorithms to sense visual information—could be applied to advance understanding of microstructures in powder-bed metal 3D printing.
In the video below, Elizabeth Holm, professor of materials science and engineering at CMU, shares three applications for computer vision in AM materials, process control and quality control. Learn more about Holm’s research in applying computer vision and machine learning to metal powders.
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