About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Computer Vision and Machine Learning Methods to Characterize Recycled Powders for Additive Manufacturing |
Author(s) |
Nathan Love, Srujana Yarasi, Andrew Kitahara, Elizabeth A Holm |
On-Site Speaker (Planned) |
Nathan Love |
Abstract Scope |
The properties of additive manufacturing feedstock powders change with recycling, but the characteristics of the powder that are responsible for these changes are not known. Three different approaches were developed to represent SEM images of metal powders in order to associate their visual appearance with the degree of recycling. The performance and shortcomings of the models are compared and examined. One of these three techniques, a novel method that aggregates computer vision features, was able to predict the degree of recycling of a powder with high accuracy and also to suggest some of the changes in individual particles that may be responsible for altered properties. This high-performing method was also applied to another powder characterization task based on material type and again succeeded with high accuracy, which suggests high potential as a general powder characterization method. |
Proceedings Inclusion? |
Undecided |