About this Abstract |
Meeting |
2022 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2022)
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Symposium
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2022 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2022)
|
Presentation Title |
An Efficient Surrogate-based Model Optimisation to Predict the Morphology of Inkjet-printed Dielectric Tracks |
Author(s) |
Juan Francisco Reyes Luna, Sean Chang, Christopher Tuck, Ian Ashcroft |
On-Site Speaker (Planned) |
Juan Francisco Reyes Luna |
Abstract Scope |
Current trends in manufacturing electronics feature digital inkjet printing as a key technology to enable the production of microscale functional devices. However, significant printed morphology quality challenges are present in current applications. Several studies predict the morphology of printed features using computationally expensive simulations, but little attention has been paid to reduced order models. Here we propose a framework to predict the inkjet-printed track morphology created by the sequential deposition of microdroplets on non-porous substrates. Assuming physical properties of a dielectric ink, a set of response surface equations built from lattice Boltzmann simulation predict the track morphology as a function of drop spacing and contact angle hysteresis with an error percentage less than 10%. Furthermore, the model captures fluid transient effects and builds morphology in seconds, enabling efficient optimisation of printing parameters. The simplicity of the proposed technique paves the way for better quality devices in the printed electronics industry. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |