  Generating droplets from a continuous stream of fluid requires precise tuning
of a device to find optimized control parameter conditions. It is analytically
intractable to compute the necessary control parameter values of a
droplet-generating device that produces optimized droplets. Furthermore, as the
length scale of the fluid flow changes, the formation physics and optimized
conditions that induce flow decomposition into droplets also change. Hence, a
single proportional integral derivative controller is too inflexible to
optimize devices of different length scales or different control parameters,
while classification machine learning techniques take days to train and require
millions of droplet images. Therefore, the question is posed, can a single
method be created that universally optimizes multiple length-scale droplets
using only a few data points and is faster than previous approaches? In this
paper, a Bayesian optimization and computer vision feedback loop is designed to
quickly and reliably discover the control parameter values that generate
optimized droplets within different length-scale devices. This method is
demonstrated to converge on optimum parameter values using 60 images in only
2.3 hours, 30x faster than previous approaches. Model implementation is
demonstrated for two different length-scale devices: a milliscale inkjet device
and a microfluidics device.
