Browsing by Author "Çetinel H."

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    Artificial neural networks modeling of mechanical property and microstructure evolution in the Tempcore process
    (2002) Çetinel H.; Özyiǧit H.A.; Özsoyeller L.
    In this study, the microstructures and the mechanical properties of steel bars treated by the Tempcore process have been investigated. In the Tempcore process, AISI 1020 steel bars of various diameters were used. In bars, unlike the self-tempering temperature and the extent of elongation, an increase in the amount of martensite was observed, which caused a consequential increase in yield and tensile strength as a function of quenching duration. The amounts of martensite, bainite, pearlite and the values of elongation, self-tempering temperature, yield and tensile strength could be obtained by a new and fast method, by using artificial neural networks. A PASCAL computer program has been developed for this study. In the numerical method, bar diameters and quenching durations were chosen as variable parameters. The numerical results obtained via the neural networks were compared with the experimental results. It appears that the agreement is reasonably good. © 2002 Elsevier Science Ltd. All rights reserved.
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    Artificial neural network-based prediction technique for wear loss quantities in Mo coatings
    (2006) Çetinel H.; Öztürk H.; Çelik E.; Karlik B.
    Mo coated materials are used in automotive, aerospace, pulp and paper industries in order to protect machine parts against wear and corrosion. In this study, the wear amounts of Mo coatings deposited on ductile iron substrates using an atmospheric plasma-spray system were investigated for different loads and environment conditions. The Mo coatings were subjected to sliding wear against AISI 303 counter bodies under dry and acid environments. In a theoretical study, cross-sectional microhardness from the surface of the coatings, loads, environment and friction test durations were chosen as variable parameters in order to determine the amount of wear loss. The numerical results obtained via a neural network model were compared with the experimental results. Agreement between the experimental and numerical results is reasonably good. © 2006 Elsevier B.V. All rights reserved.
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    Flexural strength and defect behaviour of polygranular graphite under different states of stress
    (2013) Mostafavi M.; McDonald S.A.; Çetinel H.; Mummery P.M.; Marrow T.J.
    The effect of stress state on the fracture behaviour of Gilsocarbon, an isotropic nuclear grade polygranular graphite, has been studied by employing four-point bend and ring-on-ring loading configurations to achieve uniaxial and equi-biaxial flexural stress states, respectively. Optical images of the specimens' tensile surface were analysed by digital image correlation to measure the full-field displacements: these were used to identify the fracture initiation sites, analyse crack geometry (surface length and opening displacements) and also to calculate the J-integral strain energy release rate associated with surface crack propagation. Surface cracks that did not propagate to failure were identified and subsequently examined by X-ray computed tomography combined with digital volume correlation: measurements were made of their three-dimensional displacement fields when subjected to an opening tensile stress using a modified (flat) Brazilian Disk test geometry. The crack opening behaviour is explained by an effect of stress state on the development of the crack tip fracture process zone, which is in agreement with the effect of stress state on the measured strain energy release rates of sub-critical crack propagation. Both are attributed to the plastic constraint effect, which varies with the stress state in materials that can undergo inelastic deformation. © 2013 Elsevier Ltd.