Predicting The Fracture Toughness of PNCs: A Stochastic Approach Based on ANN and ANFIS
Predicting The Fracture Toughness of PNCs: A Stochastic Approach Based on ANN and ANFIS
In this paper, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have been employed to predict the fracture energy of polymer nanocomposites. The ANN and ANFIS models were constructed, trained, and tested based on a collection of 115 experimental datasets gathered from the literature. Five input parameters were considered: the volume fraction of the nano filler, the diameter of the nano particle, the fracture energy of the matrix, the elastic modulus of the matrix, and its yield strength. The performance evaluation indices calculated for the developed models were compared with the results obtained by the Huang and Kinloch model and three other linear regression models. The ANN and ANFIS models produced considerable superior outputs with higher coefficients of determination (R