Browsing by Author "Koksal, NS"

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    Hardness and yield strength of dentin from simulated nano-indentation tests
    Toparli, M; Koksal, NS
    The finite element method (FEM) is applied for studying the hardness (H) and yield strength (Y) of dentin subjected to a nano-indentation process. The nanoindentation experiments were simulated with the ABAQUS finite element software package. This test, performed with a spherical indenter, was simulated by axisymmetric finite element analysis. The load versus displacement was calculated during loading-unloading sequence for different elastic modulus (E) and yield strength. Hardness and maximum principal compressive and tensile stresses were plotted for different elastic modulus depending on yield strength. The dentin was assumed to be isotropic, homogenous and elasto-ptastic. The theoretical results outlined in this study were compared with the experimental works reported in the literature and then hardness and yield strength of dentin was estimated. (c) 2004 Elsevier Ireland Ltd. All rights reserved.
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    Investigation of the boronizing effect on the abrasive wear behavior in cast irons
    Meric, C; Sahin, S; Backir, B; Koksal, NS
    One of the methods used to improve the surface properties of iron and steel is boronizing. Gray iron, ductile iron and compacted graphite iron were boronized with solid boron-yielding substances by pack-boronizing method. Commercial EKabor (R) 3 powder was used as the boronizing agent and the treatment was carried out at 900 degrees C for 2, 3, 4, 5 and 6 h. Thickness, microhardness and microstructure of the boride layer are investigated. Abrasive wear behavior of the boronized and unboronized cast irons were investigated. For this purpose, the specimens were tested on a pin-on disk test apparatus. SAE 1040 steel was used as the moving surface member. Abrasive wear tests were carried out at a fixed load and a fixed sliding speed. The weight loss was measured and worn surfaces were examined. (c) 2005 Elsevier Ltd. All rights reserved.
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    Prediction of mechanical properties in magnesia based refractory materials using ANN
    Koksal, NS
    Refractory materials are heterogeneous materials having complex microstructures with different constituent's properties. The mechanical properties of these materials change depending on their chemical composition and temperature. Therefore, it is important to select a refractory material, which is suitable for working conditions and is fit to place of use. Artificial neural network (ANN) model is established to investigate the relationship among processing parameters (chemical composition, temperature) and mechanical properties (bending strength, Young's modulus) in magnesia based refractory materials. The mechanical properties of magnesia based refractory materials having four different chemical compositions were investigated using three point bending test at temperatures of 25, 400, 500, 600, 700, 800, 900, 1000 and 1400 degrees C. The bending strength (sigma) and Young's modulus (E) were theoretically calculated by ANN method and theoretical results were compared with experimental values for each temperature. There were insignificant differences between experimental values and ANN results meaning that ANN results can be used instead of experimental values. Thus, mechanical properties of refractory materials having different chemical composition can be predicted by using ANN method regardless of the treatment temperature. (C) 2009 Elsevier B.V. All rights reserved.
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    Investigation of Mechanical Properties and Impact Strength Depending on the Number of Fiber Layers in Glass Fiber-reinforced Polyester Matrix Composite Materials
    Turkmen, I; Koksal, NS
    In this study, glass fiber-reinforced composite materials were produced by hand lay-up with different number of layers of glass fiber, and tensile, flexural and impact fatigue strength of composite materials were investigated. The samples were also metallographically characterized. Depending on the number of fiber layers of samples mechanical properties and impact fatigue strength were examined. Due to the increase in the number of fiber layers, the mechanical properties of the samples were found to be improved.