Artificial neural network technique to predict dynamic fracture of particulate composite
Vinod Kushvaha, S. Anand Kumar, Priyanka Madhushri, Aanchna Sharma
Abstract
In this paper, the artificial neural network technique using a multi-layer perceptron feed forward scheme was used to model and predict the mode-I fracture behaviour of particulate polymer composites when subjected to impact loading. A neural network consisting of three-layers was employed to develop the network. Artificial neural network was constructed using six input parameters such as shear wave speed ( C S ), density ( D), elastic modulus ( E d ), longitudinal wave speed ( C L ), volume fraction ( V f ) and time ( t). The influence of input parameters on the output stress intensity factor and crack-initiation fracture toughness were found to be in the order of t > C S > D > E d > C L > V f . The degree of accuracy of prediction was 92.7% for stress intensity factor. In this regard, artificial neural network can be used in the modelling and prediction of fracture behaviour of particulate polymer composites under impact loading.