Predictive modelling of fracture behaviour in silica-filled polymer composite subjected to impact with varying loading rates using artificial neural network

Abstract

In the present work, the dynamic fracture toughness of silica filled polymer composites subjected to impact loading was studied using three different loading rates corresponding to different pulse shaper conditions. These loading rates were ~107 times higher as compared to the rates usually attained in quasi-static condition for the same material. The further analysis was done using the framework of artificial neural network for neat epoxy and 10% silica filled polymer composites. Multi-layer perceptron was used to predict the crack initiation toughness of resulting composites using feed forward network. Loading rate, shear wave speed, longitudinal wave speed, volume fraction of the silica fillers and time were used as the input parameters and gradient descent function was used to estimate the optimized synaptic weights. Predicted values were compared with the experimental ones and a good agreement was found between the two. After time, loading rate was found to be the most important factor in the prediction of stress intensity factor followed by shear wave speed, longitudinal wave speed and volume fraction of the fillers used.