ANN-aided stiffness characterization of thin membranes using droplet motion

This paper presents a novel approach for the stiffness characterization of thin membranes using the characteristics of droplet motion over it. The droplet motion over an inclined free-hanging thin compliant membrane is investigated with the help of a numerical model. The effect of substrate compliance over droplet displacement 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 a reduction in the flexural rigidity of the membrane. Also, the dependency of motion of droplets with contact angle hysteresis (CAH) is presented. It is observed that the CAH increases with a decrease in the flexural rigidity of the membrane. These characteristics of the droplet motion over inclined free-hanging thin compliant membrane have been utilized to predict Young's modulus of polydimethylsiloxane (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 maximum 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 Young’s modulus of the PDMS membrane. The developed ANN network was found to have good accuracy ( R overall = 0.99995 ) in predicting Young's modulus of the PDMS membrane. The proposed approach is a simple, low-cost method for the prediction of Young’s modulus of PDMS membranes.