About this Abstract |
Meeting |
2025 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2025)
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Symposium
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2025 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2025)
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Presentation Title |
Predictive Modeling of Particle Loading for Functional Grading in Reactive Extrusion Additive Manufacturing |
Author(s) |
Brandon Yu, Hongato Song, Carolyn Seepersad |
On-Site Speaker (Planned) |
Brandon Yu |
Abstract Scope |
Reactive Extrusion Additive Manufacturing (REAM) is a process in which parts are fabricated in a layer-wise manner by dosing a multi-part thermoset resin through a motion-controlled mixing nozzle. The initial liquid state of the feedstock enables spatial or functional grading of material composition by mixing filled and unfilled resins in prescribed ratios. For example, incorporating magnetic particles into shape memory polymers enables shape programming via thermal and magnetic fields, where actuated deformation is proportional to particle concentration. However, system lag creates a mismatch between commanded and actual deposition. This research models the relationship between input signals and deposited additive concentrations over time to enable predictive modeling and control, improving spatial accuracy and repeatability of REAM parts. |
Proceedings Inclusion? |
Planned: Post-meeting proceedings |