Rheological identification of jetted fluid using machine learning

Abstract

The understanding of flowing properties of fluids and the knowledge of the related rheological properties are crucial from both a research and industrial point of view. To determine the complex rheological properties of fluids, many devices have thus been developed, the so-called rheometers. The main objective of the present paper is to identify the rheological properties of a fluid jetted using continuous inkjet (CIJ) printing process by comparing the morphology of the aforementioned jetted fluid to a dataset of known (rheologically speaking) fluid jet morphologies and properties of a fluid by the viscosity, the surface tension, and the density of fluids using large datasets and a CIJ printing process. When ejecting a fluid, the CIJ ejection process competes among several forces: inertial, viscous, surface tension, and elasticity, which affect the morphology of the resulting jet. Also, under certain conditions, the morphology of the jet is unique and directly related to the rheological properties of the fluid. We want to use this uniqueness to identify the fluid among a large dataset of known fluid jet morphologies to be compared with, to obtain its rheological properties. Using a large numerically generated dataset of Newtonian fluid jets, we show in this article that the identification of the viscosity using neural network is not only feasible but also proves to be very accurate with an average error of less than 1% for a large range of viscosities.