Workshop for AI-Powered Materials Discovery at Great Plains

Additive Manufacturing Guided with High-Speed Photography and Machine Learning

Not scheduled

20m

University of South Dakota

AI-Driven Platforms and Digital Twins for Material Discovery and Manufacturing

Speaker

S. Choudhury (University of Mississippi)

Description

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 Long Short-Term Memory (LSTM) networks and transformers 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.