Learning to Shape Liquid Droplets on an Air-Ferrofluid Interface with Sequences of Actuation
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A4 Artikkeli konferenssijulkaisussa
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Date
2023-10-13
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Language
en
Pages
6
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2023 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), pp. 1-6
Abstract
Shape morphing of liquid droplets is important for advances in both medical and industrial applications. However current manipulation techniques lack methods to control shapes other than elliptical-shaped droplets. Here we propose using Long Short-Term Memory (LSTM) based model to learn and predict the evolution of the shape of a non-magnetic liquid droplet at an air-ferrofluid interface deformed with programmed sequential actuation of electromagnets. The resulting droplet shapes can be convex or concave. We can also predict the actuation sequences for a given shape sequence with an accuracy of 79.1 %. The proposed method could also be applied to a variety of other liquid droplet shape-morphing systems which utilize arrays of electromagnetic or electric actuators.Description
Keywords
Adaptation models, Magnetic flux density, Actuators, Liquids, Shape, Atmospheric modeling, Magnetic liquids
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Citation
Patikiri Arachchige, D & Zhou, Q 2023, Learning to Shape Liquid Droplets on an Air-Ferrofluid Interface with Sequences of Actuation . in S Haliyo, M Boudaoud, M A Qasaimeh & S Fatikow (eds), 2023 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS) ., 10294154, IEEE, pp. 1-6, International Conference on Manipulation, Automation and Robotics at Small Scales, Abu Dhabi, United Arab Emirates, 09/10/2023 . https://doi.org/10.1109/MARSS58567.2023.10294154