Estimation of adhesive wear behavior of the glass fiber reinforced polyester composite materials using ANFIS model

Abstract

Glass fiber reinforced polyester (GFRP) composite materials are widely used in various applications. The prediction of wear values for composite materials is very complex and nonlinear phenomena. Artificial intelligence methods (AI) and expert systems such as artificial neural networks (ANNs) and fuzzy inference systems (FIS) have a series of properties on modeling nonlinear systems. In some situations, ANNs are insufficient under abrupt changes in input variables. Adaptive Neuro Fuzzy Inference System (ANFIS) is capable of integrating the linguistic expressions of FIS with the adaptation and learning skills of the ANNs. The aim of this study is to determine the optimum material content and working conditions in terms of wear resistance. This study proposes an ANFIS sub-clustering based prediction model for estimation of wear behavior of GFRP composites within various concentrations of materials and under diverse loads and speeds. Proposed ANFIS model extracted optimum concentrations and operating parameters to obtain the minimum wear rate. Due to the wear rate estimation model, optimum wear rate value is reached to 25.0013 (mm(3)/Nm)*10(-6) at CaCO3, polystyrene, glass fiber, glass bead, alumina, load and speed values of 49%, 0%, 11%, 10%, 0.8%, 10 N and 100 rpm respectively. A high estimation capability (R-2 = 0.964) has been achieved using ANFIS Model.