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| − | == Abstract == | + | == DOCUMENT == |
| − | Drop-on-demand inkjet printing is one of the most widespread applications of microflu
| + | <pdf>Media:Draft_Lorente_Macias_227532749_2057_ECCOMAS_LorenteMacias_Juniper.pdf</pdf> |
| − | idics. A typical inkjet printhead is composed of several microchannels and nozzles. Piezo
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| − | electric actuators placed at the microchannel walls are used to force ink droplets through
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| − | the nozzle. After droplet injection, acoustic waves travel through the microchannels until
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| − | they are damped by viscous dissipation. These reverberations must be cancelled before
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| − | jetting the next droplet so that all droplets are uniform. Open-loop control of the actuator
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| − | has been designed to damp these reverberations faster and increase the jetting frequency
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| − | [1, 2]. However, the pressure waves generated can also deform the microchannel walls [3].
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| − | This deformation produces pressure waves in the adjacent microchannels. The amount
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| − | of acoustic energy transmitted mainly depends on the structural properties of the shared
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| − | 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
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| − | performed by selectively deforming one actuator while leaving the rest passive. The am
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| − | plitude of the deformation is small enough to avoid droplet jetting. High-speed cameras
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| − | capture the velocity of the meniscus attached to the nozzle outlets. We solve the data
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| − | assimilation problem using a gradient-based optimization algorithm accelerated with the
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| − | adjoint method for gradient computation. As a result, we obtain the wall properties that
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| − | most likely fit the experimental data.
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| − | == Acknowledgements ==
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| − | 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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