An artificial neural network (ANN) model for predicting water absorption of nanoclay-epoxy composites

Abstract

Glass fiber reinforced epoxy (GFRE) composite materials are prone to suffer from water absorption due to their heterogeneous structure. The main process governing water absorption is diffusion of water molecules through the epoxy matrix. However, hydrolytic degradation may also take place during components service life specially due high temperatures. In order to mitigate the effects of the water diffusive processes in the deterioration of in-service behavior of epoxy matrix composites, the use of chemically modified nanoclays as an additive has been proposed and studied in previous works [1]. In this work, an Artificial Neural Network (ANN) model was developed for better understanding and predicting the influence of modified and unmodified bentonite addition on the water absorption behavior of epoxy-anhydride systems. An excellent correlation between model and experimental data was found. The ANN model allowed the identification of critical points like the precise temperature at which a particular system?s water uptake goes beyond a predefined threshold, or which system will resist an immersion longer than a particular time.