Predictive modelling of fracture behaviour in silica-filled polymer composite subjected to impact with varying loading rates using artificial neural network. (November 2020)

• Record Type: Journal Article
• Authors: Sharma, Aanchna; Kushvaha, Vinod
• Is Part Of: Engineering fracture mechanics. Volume 239(2020)
• Publication Date: 2020-11
• Subjects: Artificial neural network -- Loading rate -- Stress intensity factor -- Crack initiation toughness -- Fracture toughness; Fracture mechanics -- Periodicals; Rupture, Mécanique de la -- Périodiques; Fracture mechanics; Periodicals; 620.112605
• DOI: 10.1016/j.engfracmech.2020.107328
• Languages: English
• ISSNs: 0013-7944

Abstract

Highlights: The fracture toughness of silica filled polymer composites is investigated using varying loading rates. Artificial Neural Network is employed to predict the stress intensity factor history. Loading rate is found to be the most important parameter in the prediction of stress intensity factor. The results obtained from the ANN model and the experiments are in good agreement. 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 ~10 7 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.