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

arXiv: https://arxiv.org/abs/2105.13553 · HTML: https://mlsanity.com/paper/2105.13553/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 46 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 1 of 1)

- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)

## Built on by (most cited first, 4 of 4)

- [Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)](https://mlsanity.com/paper/2208.13771/) (2022)
- [Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization](https://mlsanity.com/paper/2304.14408/) (2023)
- [Decreasing the Computing Time of Bayesian Optimization using Generalizable Memory Pruning](https://mlsanity.com/paper/2309.04510/) (2023)
- [MIML: Multiplex Image Machine Learning for High Precision Cell Classification via Mechanical Traits within Microfluidic Systems](https://mlsanity.com/paper/2309.08421/) (2023)

## Abstract (arXiv)

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.