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 |
AMPIS: Automated Materials Particle Instance Segmentation |
Author(s) |
Ryan Cohn, Elizabeth Holm |
On-Site Speaker (Planned) |
Ryan Cohn |
Abstract Scope |
We present AMPIS, an open-source framework designed to make instance segmentation more accessible to materials scientists. Instance segmentation generates individual segmentation masks for every recognized object in an image. This facilitates the characterization of materials with multiple components from images, automating image processing and enabling new characterization techniques. AMPIS was originally developed to measure satellites, which influence powder rheology but cannot be experimentally characterized, on additive manufacturing feedstock powders. Leveraging transfer learning with Mask R-CNN enabled instance segmentation of individual powder particles and satellites from images, despite only having a small labeled dataset to train the model with. The results demonstrated the ability of this approach to generate the first quantitative, repeatable measurements of satellites in metal powders. However, the benefits of instance segmentation and AMPIS are not limited to powder characterization. AMPIS is a flexible tool and may be applied to a variety of material systems and applications. |
Proceedings Inclusion? |
Undecided |