ANN-aided Stiffness Characterization of Thin Membranes using Droplet Motion

Abstract

This paper presents a novel approach for the stiffness characterization of thin membranes using the droplet motion characteristics over the membrane. The droplet motion over an inclined free hanging thin compliant membrane is investigated with the help of numerical model. Effect of substrate compliance over the displacement of the droplet has been studied. Further, the wall deformation of the substrate with the droplet motion has also been investigated. The numerical study has highlighted that the distance moved by a droplet over a membrane reduces with reduction in flexural rigidity of the membrane. Also, the dependency of motion of droplet with contact angle hysteresis (CAH) is presented. It was observed that the CAH increases with decrease in flexural rigidity of the membrane. These characteristics of the droplet motion over inclined free-hanging thin complaint membrane have been utilized to predict the Young's modulus of PDMS membranes using a novel Artificial Neural Network (ANN) model. The ANN model is developed for the same purpose. The input parameters for the ANN model are the droplet's displacement over a fixed time interval (𝑥), the PDMS membrane's deflection (𝑌), the angle of inclination (𝜃), the thickness of the membrane (𝑡), and the droplet's volume (𝑉). The output parameter of the ANN is the Young’s Modulus of the PDMS membrane. The created ANN network was found to have good accuracy (𝑅𝑜𝑣𝑒𝑟𝑎𝑙𝑙 = 0.992) in predicting the Young's modulus of the PDMS membrane. The proposed approach is a simple, low-cost method for prediction of Young’s modulus of PDMS membranes.