Drop-on-demand inkjet printing is one of the most widespread applications of microflu

idics. A typical inkjet printhead is composed of several microchannels and nozzles. Piezo

electric actuators placed at the microchannel walls are used to force ink droplets through

the nozzle. After droplet injection, acoustic waves travel through the microchannels until

they are damped by viscous dissipation. These reverberations must be cancelled before

jetting the next droplet so that all droplets are uniform. Open-loop control of the actuator

has been designed to damp these reverberations faster and increase the jetting frequency

[1, 2]. However, the pressure waves generated can also deform the microchannel walls [3].

This deformation produces pressure waves in the adjacent microchannels. The amount

of acoustic energy transmitted mainly depends on the structural properties of the shared

boundaries. This phenomenon makes it challenging to control the reverberations effectively in the whole printhead. In this study, we formulate a Bayesian inverse problem to infer the mechanical properties of the compliant microchannel walls. The experiments are

performed by selectively deforming one actuator while leaving the rest passive. The am

plitude of the deformation is small enough to avoid droplet jetting. High-speed cameras

capture the velocity of the meniscus attached to the nozzle outlets. We solve the data

assimilation problem using a gradient-based optimization algorithm accelerated with the

adjoint method for gradient computation. As a result, we obtain the wall properties that

most likely fit the experimental data.

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== Acknowledgements ==

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 955923MARIE SKŁODOWSKA-CURIE ACTIONS Innovative Training Networks (ITN) Call: H2020-MSCA-ITN-2020

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