Laboratory measuring of contact angles for liquid/solid surface and analysis with artificial neural network

Abstract

In this study, the advancing and receding contact angles of three types of pure liquid on four different surfaces have been measured. The contact angles of the droplet have been measured by analysing the captured photo by tilted plate method. Droplet behaviour has been modelled by two experimental equations. Additionally, the data has been modelled by an artificial neural network using genetic algorithm and a hyperbolic tangent activation function. The input parameters are density, molecular weight of pure liquid and solid, viscosity and surface tension of pure liquid, roughness of the solid surface and the two outputs are advancing and receding contact angles of the droplet. Number of 81 data points were used for training, 27 data for validation and 28 data for testing. The topography of {7,7,2} for artificial neural network has been proposed. The resulting RMS errors were 8%, 8% and 7% for training, validation and testing, respectively.