Deep learning study of induced stochastic pattern formation in the gravure printing fluid splitting process

Abstract

We use deep learning (DL) algorithms for the phenomenological classification of Saffman-Taylor-instability-driven spontaneous pattern formation at the liquid meniscus in the fluid splitting in a gravure printing press. The DL algorithms are applied to high-speed video recordings of the fluid splitting process between the rotating gravure cylinder and the co-moving planar target substrate. Depending on rotation velocity or printing velocity and gravure raster of the engraved printing cylinder, a variety of transient liquid wetting patterns, e.g., a raster of separate drops, viscous fingers, or more complex, branched liquid bridges appear in the printing nip. We discuss how these patterns are classified with DL methods, and how this could serve the identification of different hydrodynamic flow regimes in the nip, e.g., point or lamella splitting.