Surface Wettability Modeling and Predicting via Artificial Neural Networks
Katarzyna Peta
Abstract
Surface wettability, defined by the contact angle, describes the ability of a liquid to spread over, absorb or adhere to a solid surface. Surface wetting analysis is important in many applications, such as lubrication, heat transfer, painting and wherever liquids interact with solid surfaces. The behavior of liquids on surfaces depends mainly on the texture and chemical properties of the surface. Therefore, these studies show the possibility of modeling surface wettability by adjusting the parameters of the surface texturing process. The prediction of the contact angle describing the wettability of the surface was performed using artificial neural networks. In order to select the most effective prediction model, the activation functions of neurons, the number of hidden layers and the network training algorithms were changed. The neural network model presented in these studies is capable of predicting the contact angle with an efficiency defined by the coefficient of determination R2 between real and predicted contact angles of over 0.9.