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Meeting MS&T23: Materials Science & Technology
Symposium Advances in Dielectric Materials and Electronic Devices
Presentation Title Design for in-situ Computer Vision-based Automation of Drop-on-Demand Inkjet Drop Formation Optimization
Author(s) Maximilian Estrada, Ruyan Guo, Amar Bhalla
On-Site Speaker (Planned) Maximilian Estrada
Abstract Scope The process of drop optimization in a drop-on-demand inkjet printing process was automated using various neural network architectures. Currently, drop optimization is performed manually: an operator modifies voltage waveform parameters in response to real-time (stroboscopic) macro-lens camera footage of the printhead during jetting. The automation task was subdivided into three tasks: intrinsic representation, extrinsic representation, and property-relation representation. A Convolutional Recurrent Neural Network and a 3D Convolutional Neural Network were compared for intrinsic representation (interpreting drop quality from video); a fully connected network and an Long Short Term Memory (LSTM) will be compared for extrinsic representation (voltage waveform recommendation), and a Graph Neural Network and an LSTM will be compared for property-relation representation (relating the voltage waveform to drop quality).

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D-5: Influence of the Synthesis Conditions on the Structural and Microstructural Properties of PbTiO3 Ferroelectric Thin Films
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D-7: Investigation of the Dielectric Response in AgNbO3-based Ferroelectric Ceramics
D-8: Transition Metal Chalcogenide Perovskites for Energy Applications
Design for in-situ Computer Vision-based Automation of Drop-on-Demand Inkjet Drop Formation Optimization
Domain Tailoring in Magnetic ZnFe2O4 Ferrite by Reactive Flash Sintering Technique
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