ARTANA Guillermo Osvaldo
A machine learning-based framework to design capillary-driven networks
GARCIA EIJO, PEDRO M.; DURIEZ, THOMAS; CABALEIRO JUAN MARTIN; ARTANA GUILLERMO
LAB ON A CHIP
ROYAL SOC CHEMISTRY
Lugar: CAMBRIDGE; Año: 2022 vol. 22
We present a novel approach for the design of capillary-driven microfluidic networks using a machinelearning genetic algorithm (ML-GA). This strategy relies on a user-friendly 1D numerical tool specificallydeveloped to generate the necessary data to train the ML-GA. This 1D model was validated using analyticalresults 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 theclosest matching results against two prescribed curves of delivered volume against time. We performedmore than 20 generations of 10 000 simulations to train the ML-GA and achieved the optimal solution ofthe inverse design problem. The optimisation took less than 6 hours, and the results were successfullyvalidated using experimental data. This work establishes the utility of the presented method for the fast andreliable design of complex capillary-driven devices, enabling users to optimise their designs via an easy-to-use 1D numerical tool and machine learning technique.