Data-Driven Drop Formation Modeling in Nanoliter Drop-on-Demand Inkjet Printing

Drop formation models are essential for the control of drop-on-demand (DoD) inkjet printing process. Traditional numerical models are difficult to implement in real-time control. In this paper, an equivalent circuit model (ECM) is developed to motivate the implementation of a data-driven ARX model combined with a drop volume adjuster to estimate drop volume. Drop pinch-off instants and drop velocities are modeled in polynomials with respect to the parameters of the drive waveform. All models are validated through 10-fold cross validation. The simulation of the drop volume model shows good agreement with experimental results.