Physics-informed machine learning for optimizing the coating conditions of blade coating

Abstract

Large-scale liquid coating has been used in various industrial fields, such as the fabrication of functional panels or surfaces. Blade coating with continuous liquid supply is a cost-effective method for large-scale coatings. To coat a liquid without defects, it is essential to maintain the coating bead trapped under the blade stably. However, numerous experiments are required to obtain the optimal conditions. We developed a novel strategy for acquiring coating conditions using physics-informed neural networks (PINNs) to avoid this laborious effort. Whereas standard neural networks (NNs) predict the coating performance directly from the operating parameters, PINNs predict parameters related to the state of coating bead to enhance its predictive performance. Our results revealed that the PINNs performed better than the standard NNs. Furthermore, we derived a damped harmonic oscillator model that provides physical insight into the correlation between the coating performance and coating conditions. Finally, a parametric study was performed using the PINN-based model to determine the optimal coating conditional zones, and we experimentally demonstrated the stable coating with the optimized operating parameters.

Control of the Drying Patterns for Complex Colloidal Solutions and Their Applications

“…To overcome the limitations in applications, novel strategies to control the coffee-ring effect have recently been developed using ice drying, bubbles, and so on, but the studies on manipulating deposit patterns without any modification of the properties are still progressing. Furthermore, for a wide range of advanced technologies, the methods to control particle migration in a large area coating [ 153 ] and multi droplets or patterns [ 154 ] would be needed.…”

“…The formation of high-quality micro-patterns through solvent evaporation is noted to enhance the performance of optical/electric devices [ 157 ]. How the solute deposition morphology pans out during drying is imperative in understanding and monitoring the deposition processes in several manufacturing applications of printing or coating process [ 12 , 13 , 14 , 153 ], fabrication of functional nanomaterials [ 158 , 159 ], colloidal crystals [ 160 ] and ordered structures [ 161 ].…”