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    Estimation of adhesive wear behavior of the glass fiber reinforced polyester composite materials using ANFIS model
    (SAGE Publications Ltd, 2021) Yilmaz, Serhat; İlhan, Recep; Feyzullahoglu, Erol
    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.
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    Investigation of adhesive wear properties of glass fiber reinforced polyester composites having different chemical compositions
    (SAGE Publications Ltd, 2021) İlhan, Recep; Feyzullahoglu, Erol
    Glass fiber reinforced polyester composite materials are widely used in various areas due to their high specific strength, low weight, excellent elasticity, high corrosion resistance, and high thermal stability. This study aims to investigate the effects of resin materials and various fillers and wear parameters such as different loads and speeds on the tribological properties of glass fiber reinforced polyester composite materials. In this experimental study, various resins (tensile additive orthophthalic polyester and plain orthophthalic polyester), fillers (alumina and glass beads), and reinforcing materials were used during the sample preparation. The samples were subjected to an adhesive wear test at two different speeds (n = 100 r/min and n = 200 r/min) and different loads (F = 10 N and F = 20 N) at 150 m sliding distance. The friction coefficient and friction force were measured by the tribometer. The thickness of the wear trace was later measured and the wear rate was calculated. Wear surfaces of samples were visualized with a three-dimensional laser profilometer in order to obtain surface topographies and surface roughness values. The sample surfaces were examined by scanning electron microscopy in order to understand the wear mechanisms and to characterize the morphology of worn surfaces. Experimental results have shown that alumina or glass beads fillers can reduce the average friction coefficient when used in the correct amounts. The use of glass bead filler in orthophthalic polyester resin with tensile additive is more effective than reducing the wear rate compared to alumina filler. The load on the wear behavior of glass fiber reinforced polyester composite materials is more effective than the speed.