A surrogate modelling strategy to improve the surface morphology quality of inkjet printing applications

Abstract

Current trends in manufacturing electronics feature digital inkjet printing as a key technology to enable the production of customised and microscale functional devices. However, electrical device performance depends on the accuracy and uniformity of the printed-track morphology, which presents significant quality challenges in current applications. Several studies to predict the morphology of printed features have been developed using computationally expensive physics-based simulations, but little attention has been paid to reduced order models suitable for fast production conditions. Here we propose a surrogate modelling framework to improve the inkjet-printed track morphology created by the sequential deposition of microdroplets on non-porous substrates. Assuming physical properties of a UV-curable dielectric ink made from tripropylene glycol diacrylate (TPGDA), a set of response surface equations built from a validated 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 surrogate model is able to capture transient effects observed in experiments and builds track morphology in seconds, enabling efficient optimisation of printing and wetting parameters. The simplicity of the proposed technique makes it a promising tool for model driven inkjet printing process optimization, including real time process control and paves the way for better quality devices in the printed electronics industry.