Dynamic Fracture Toughness Prediction of Fiber/Epoxy Composites Using K-Nearest Neighbor (KNN) Method

Abstract

Geometrical features like size and shape of the particles which are used to reinforce the composites affect the mechanical behavior of the resulting particulate polymer composites to a great extent. The aspect ratio of the reinforcing filler is of great importance specially when such composites are subjected to impact loading. Usually, an increase in the aspect ratio results in a significant increase in the energy-absorbing ability which ultimately improves the fracture toughness of the resulting composite. However, the experimental procedure followed for determining the fracture toughness of polymer composites reinforced with particles of varying aspect ratio is very complex and time-consuming. In this view, this chapter investigates the applicability of a machine learning algorithm known as K-nearest neighbor (KNN) for determining the dynamic fracture toughness of glass-filled polymer composites. The proposed methodology aims to predict the fracture toughness in terms of stress intensity factor with limited experimentation and maximum accuracy. The current framework of machine learning utilizes time, dynamic elastic modulus, aspect ratio, and volume fraction of the glass particles as the independent model parameters. The proposed KNN model predicts the fracture behavior of these composites with an accuracy of ~96%.KeywordsK-nearest neighborAspect ratioFracture toughnessMachine learningImpact loadingStress intensity factor