Issue 24, 2022

A machine learning-based framework to design capillary-driven networks

Abstract

We present a novel approach for the design of capillary-driven microfluidic networks using a machine learning genetic algorithm (ML-GA). This strategy relies on a user-friendly 1D numerical tool specifically developed to generate the necessary data to train the ML-GA. This 1D model was validated using analytical results issued from a Y-shaped capillary network and experimental data. For a given microfluidic network, we defined the objective of the ML-GA to obtain the set of geometric parameters that produces the closest matching results against two prescribed curves of delivered volume against time. We performed more than 20 generations of 10 000 simulations to train the ML-GA and achieved the optimal solution of the inverse design problem. The optimisation took less than 6 hours, and the results were successfully validated using experimental data. This work establishes the utility of the presented method for the fast and reliable design of complex capillary-driven devices, enabling users to optimise their designs via an easy-to-use 1D numerical tool and machine learning technique.

Graphical abstract: A machine learning-based framework to design capillary-driven networks

Article information

Article type
Paper
Submitted
10 Ira. 2022
Accepted
07 Aza. 2022
First published
08 Aza. 2022

Lab Chip, 2022,22, 4860-4870

A machine learning-based framework to design capillary-driven networks

P. M. Garcia Eijo, T. Duriez, J. M. Cabaleiro and G. Artana, Lab Chip, 2022, 22, 4860 DOI: 10.1039/D2LC00843B

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