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Öğe Investigate the effect of pre-drilling in friction drilling of A7075-T651(Taylor & Francis, 2014-04-28) Demir, Zülküf; Özek, CebeliFriction drilling is a non-traditional hole achieving method that is a clean, chip-less process, which is called thermal drilling, form drilling, flow drilling, and friction stir drilling. In this study pre-drilling friction drilling was investigated for improving the bushing shape of A7075-T651, which is a brittle cast material. During the process, surface roughness and bushing shapes were analyzed and generated frictional heat was measured by the virtue of thermocouples. Experiments were carried out to 4mm and 6mm in thicknesses of A7075-T651 aluminum alloy at 1200, 1800, 2400, 3000, and 3600 rpm spindle speeds, 20, 40, 60, 80, and 100 mm/min feed rates with using high-speed steel rotating conical tool, whose diameter is 8 mm. Consequently, the bushing shapes were advanced without cracks and petal formation in pre-drilling Friction drilling in comparison with without pre-drilling process. With increasing pre-drilled hole diameter the generated frictional heat was decreased. The achieved temperature was realized to be 1/2-1/3 of the melting temperature of the workpiece. Surface roughness values were decreased with decreasing or increasing both spindle speed and feed rate correspondingly.Öğe Calculating molding parameters in plastic injection molds with ANN and developing software(Taylor & Francis, 2012-02) Çelik, Yahya Hışman; Özek, CebeliIn recent years, plastic injection molds are widely used for producing products in various areas, such as aerospace, automotive, medical, electronics, and toys. The quality of these products depends on correctly chosen molding parameters. In this study, a new package program (NPP)-Software that calculates various injection molding parameters was developed to mold plastic products obtained by plastic injection molding techniques using the model of artificial neural network (ANN). The Delphi programming language was used in the develop the (NPP)-Software. The developed (NPP)-Software was trained and tested using the Levenberg–Marquardt (LM) algorithm, the ANN. One-thousand three-hunderd pieces of data were collected, out of which 250 were used to train the network. The ANN is employed to find optimum molding parameters that enable minimum defects in the injection-molded part, such as volumetric shrinkage, injection time, and cooling time. The three parameters predicted, using the (NPP)-Software, were compared using experimental results.
