Predicting Quality of Coated Layer Based on Meniscus Features Associated with Thickness for Sustainable Roll-to-Roll Processes

Meniscus formation in roll-to-roll slot die coating systems has a decisive impact on the thickness and uniformity of the coated layer. As the demand for high-quality, uniform-thickness coatings has increased, accurately determining the quality of the layer being coated in real time is essential. In this study, a coated-layer thickness prediction model was developed based on the vision data of meniscus formation between a slot die coater and substrate. Increasing the tension led to a decrease in the coating layer thickness due to enhanced ink spreading, while increasing the coating gap led to an increase in the coating layer thickness due to the shear thinning effect. Comparisons of the differences between the vision data obtained at different process variables showed that the interaction between the ink and the substrate changed the meniscus shape, which affected the coating quality. Based on the analysis of the slot die-coating meniscus, four key features that affect thickness were defined as meniscus features: dynamic flow rate, downstream meniscus curvature, downstream meniscus angle, and upstream meniscus angle. A predictive model developed using these features was 85.9% more accurate than a mathematical model. This new approach for meniscus analysis provides guidance for understanding the mechanism of thickness variation and could facilitate advances in roll-to-roll green manufacturing.