  Drop-on-demand (DOD) inkjet printing has been considered as one of promising
technologies for the fabrication of advanced functional materials. For a DOD
printer, high-precision dispensing techniques for achieving satellite-free
smaller droplets, have long been desired for patterning thin-film structures.
The present study considers the inlet velocity of a liquid chamber located
upstream of a dispensing nozzle as a control variable and aims to optimize its
waveform using a sample-efficient Bayesian optimization algorithm. Firstly, the
droplet dispensing dynamics are numerically reproduced by using an open-source
OpenFOAM solver, interFoam, and the results are passed on to another code based
on pyFoam. Then, the parameters characterizing the actuation waveform driving a
DOD printer are determined by the Bayesian optimization (BO) algorithm so as to
maximize a prescribed multi-objective function expressed as the sum of two
factors, i.e., the size of a primary droplet and the presence of satellite
droplets. The results show that the present BO algorithm can successfully find
high-precision dispensing waveforms within 150 simulations. Specifically,
satellite droplets can be effectively eliminated and the droplet diameter can
be significantly reduced to 24.9% of the nozzle diameter by applying the
optimal waveform.
