About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
|
Additive Manufacturing Modeling, Simulation and Machine Learning
|
Presentation Title |
Additive Manufacturing Guided with High-Speed Photography and Machine Learning |
Author(s) |
Stanford White, Samrat Choudhury, Yiwei Han |
On-Site Speaker (Planned) |
Stanford White |
Abstract Scope |
The extrusion dynamics in additive manufacturing (AM) processes such as electrohydrodynamic (EHD) printing are dependent on a large (>15) set of processing parameters, material properties, and environmental conditions. In EHD printing, these parameters affect the process stability, printing behavior (droplet or filament), and quality of the deposited microstructures. In this work, we have utilized high-speed photography to capture the ink flow dynamics at the EHD printer nozzle tip. Later, machine learning tools such as gated recurrent unit (GRU) networks were trained on high-speed video data to predict the ink flow dynamics for a new set of processing and material parameters. It was shown that combined high-speed imaging and machine learning can establish the processing-property relationship during AM process while laying the foundation for real-time control of printing behavior and process stability. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Machine Learning, Other |