Prediction of tensile modulus of PA-6 nanocomposites using adaptive neuro-fuzzy inference system learned by the shuffled frog leaping algorithm

Abstract

In this work, PA-6 nanocomposites containing different amounts of nanoclay (NC) were prepared using a corotating twin-screw extruder. In practice, it is hard task to identify the relationship between the extrusion process parameters and the tensile modulus of PA-6 nanocomposites by performing several experiments. One approach to map the relationship between the process parameters and the tensile modulus of PA-6 nanocomposites is the use of a non-linear system identification tool called the adaptive-neuro fuzzy inference system (ANFIS). In this study, to achieve high modeling accuracy and generalization capability, an efficient shuffled frog leaping (SFL) algorithm is proposed to learn all the parameters of the network. A multi-input single-output (MISO) ANFIS model is constructed and learned to predict the tensile modulus of PA-6 nanocomposites. The ANFIS model is constructed, trained and tested based on a collection of experimental data sets. Acceptable agreement has been observed between the experimental results and the predicted results by the proposed model. The statistical quality of the proposed model is significant due to its good correlation coefficient R2 values >0.98 between predicted values and experimental ones during the training and testing phase. Also, comparison results indicate the superior performance of the proposed scheme over the conventional reported methods due to its high approximation accuracy and good generalization capability.