A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation
Name
2105.13553.pdf
Description
Accepted version
Size
26.6 MB
Format
Adobe PDF
Checksum (MD5)
6a905bd391bd2ce3e13979ee7627f2b1
Author(s)
Siemenn, Alexander E
Shaulsky, Evyatar
Beveridge, Matthew
Buonassisi, Tonio
Hashmi, Sara M
Drori, Iddo
Date Issued
2022
Journal
ACS Applied Materials & Interfaces
Publisher
American Chemical Society (ACS)
Citation
Siemenn, Alexander E, Shaulsky, Evyatar, Beveridge, Matthew, Buonassisi, Tonio, Hashmi, Sara M et al. 2022. "A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation." ACS Applied Materials & Interfaces, 14 (3).
Version
Author's final manuscript
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 h, 30× faster than previous approaches. Model implementation is demonstrated for two different length-scale devices: a milliscale inkjet device and a microfluidics device.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/ACSAMI.1C19276
