Artificial neural network technique to predict dynamic fracture of particulate composite. (September 2020)

• Record Type: Journal Article
• Authors: Kushvaha, Vinod; Kumar, S Anand; Madhushri, Priyanka; Sharma, Aanchna
• Is Part Of: Journal of composite materials. Volume 54:Number 22(2020)
• Page Start: 3099
• Page End: 3108
• Publication Date: 2020-09
• Subjects: Artificial neural network -- dynamic fracture toughness -- stress intensity factor -- impact loading -- polymer composite -- prediction -- modelling; Composite materials -- Periodicals; Composites -- Périodiques; 620.118
• DOI: 10.1177/0021998320911418
• Languages: English
• ISSNs: 0021-9983

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.