Voltage Waveform Optimization Through Data-Driven Modeling in Electrohydrodynamic Jet Printing

Micro-additive manufacturing techniques pertaining to material jetting have demonstrated strong applicability in the fields of printed electronic and photonic devices. Nano-scale patterning has been achieved using electrohydrodynamic jet (e-jet) printing, which utilizes a series of high voltage pulses to precisely deposit ink in the form of printed patterns. Previous studies have successfully implemented learning control frameworks to achieve desired printing performance by adjusting the magnitude and timing of the voltage pulse. However, optimization of the input shape from a process-driven perspective has not been analyzed, and the risks of nozzle clogging and printing regime fluctuations remain as challenges in maintaining stable performance. Additionally, a knowledge gap persists in characterizing the effects of modulating the baseline voltage on temporal and volumetric dynamics of the jetting process, which serve to provide critical time constants in the design of the input shape. This work implements data-driven modeling techniques to quantify the effects of varying the pulsed and baseline voltages while developing a process-driven optimization algorithm for promoting stable jetting, furthering the foundation for implementation of control strategies for e-jet printing.