A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation

TL;DR

This paper presents a Bayesian optimization and computer vision feedback loop that rapidly and reliably optimizes droplet generation across different device scales using minimal data, significantly outperforming previous methods in speed.

Contribution

The authors develop a universal optimization method combining Bayesian optimization with computer vision that works across multiple length scales with minimal data and training time.

Findings

Converges on optimal parameters with only 60 images in 2.3 hours.

Outperforms previous approaches by a factor of 30 in speed.

Successfully applied to both milliscale and microfluidic devices.

Abstract

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.