About this Abstract |
Meeting |
2021 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2021)
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Symposium
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Physical Modeling
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Presentation Title |
A Method of Predicting Powder Flowability for SLS |
Author(s) |
Doug Sassaman, Tim Phillips, Joseph Beaman, Matthew Ide, Craig Milroy |
On-Site Speaker (Planned) |
Doug Sassaman |
Abstract Scope |
In this work, we propose a method for pre-screening material systems for Selective Laser Sintering (SLS) using a combination of Revolution Powder Analysis (RPA) and machine learning. To develop this method, nylon was mixed with alumina or carbon fibers in different wt.% to form material systems with varying flowability. The materials were measured in a custom RPA device and the results compared with powder bed density measured by spreading a layer of powder into a pocket of known volume using a counter-rotating roller. Machine learning was used to draw correlations between the RPA data and powder bed density, allowing RPA to pre-screen low volumes of material and determine their suitability for SLS. The result is a material-agnostic method to predict if a powder will spread to adequate density in a SLS machine using a counter-rotating roller. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |