Yazar "Isik, Birhan" seçeneğine göre listele

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    CUTTING FORCES IN ORTHOGONAL TURNING OF UNIDIRECTIONAL GLASS FIBRE REINFORCED PLASTIC COMPOSITES
    (Adcotec Ltd, 2011) Isik, Birhan; Altan, Erhan
    Experimental investigation of machining is cost prohibitive. The number of parameters to control, the exhaustive material characterisation and the time consuming procedure to determine the mechanical responses like cutting forces restricts experimental studies. In this context, mathematical modelling can be a feasible tool for studying the various responses in machining. This paper presents an attempt to investigate orthogonal machining of unidirectional Glass Fibre Reinforced Plastic Composite (UD-GFRP) materials using mathematical modelling. The model entailing fibre orientation, shear strength, depth of cut, feed, friction angle and rake angle is constructed for investigating the tangential cutting and feed cutting force developed during machining. The numerical results are compared to the experimental results. The comparison indicates that the model provides satisfactory prediction of the cutting forces. The relations between process parameters are discussed.
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    Experimental investigations of damage analysis in drilling of woven glass fiber-reinforced plastic composites
    (Springer London Ltd, 2010) Isik, Birhan; Ekici, Erguen
    This paper presented a new comprehensive approach to select cutting parameters for damage factor in drilling of glass fiber-reinforced polymer (GFRP) composite material. The influence of drilling on surface quality of woven GFRP plastic composite material was investigated experimentally. Drilling tests were carried out using carbide drills of 8 mm in diameter at 50, 70, and 90 m/min cutting speeds and at 0.06, 0.12, and 0.18 mm/rev feed rates. Damage factor was investigated based on hole entrance and exit. Analysis of variance (ANOVA) test was applied to the experimental results. The compared values were employed by Duncan test to identify which groups were significantly different from other groups.
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    Experimental investigations of surface roughness in orthogonal turning of unidirectional glass-fiber reinforced plastic composites
    (Springer London Ltd, 2008) Isik, Birhan
    Cutting parameters and the resulting cutting forces have a great effect on the machinability of materials during the turning process. The effects of cutting parameters on machinability have been examined by many researchers and studies on determination of suitable cutting conditions for various materials are still under investigation. In this study, surface roughness of unidirectional glass-fiber reinforced plastic composite was examined on the basis of cutting parameters such as depth of cut, feed rate, tool geometry, and cutting speed. The surface quality was found to relate closely to the feed rate, cutting speed, and cutting tool.
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    Prediction of Damage Factor in end Milling of Glass Fibre Reinforced Plastic Composites Using Artificial Neural Network
    (Springer, 2013) Erkan, Omer; Isik, Birhan; Cicek, Adem; Kara, Fuat
    Glass fibre reinforced plastic (GFRP) composites are an economic alternative to engineering materials because of their superior properties. Some damages on the surface occur due to their complex cutting mechanics in cutting process. Minimisation of the damages is fairly important in terms of product quality. In this study, a GFRP composite material was milled to experimentally minimise the damages on the machined surfaces, using two, three and four flute end mills at different combinations of cutting parameters. Experimental results showed that the damage factor increased with increasing cutting speed and feed rate, on the other hand, it was found that the damage factor decreased with increasing depth of cut and number of the flutes. In addition, analysis of variance (ANOVA) results clearly revealed that the feed rate was the most influential parameter affecting the damage factor in end milling of GFRP composites. Also, in present study, Artificial Neural Network (ANN) models with five learning algorithms were used in predicting the damage factor to reduce number of expensive and time-consuming experiments. The highest performance was obtained by 4-10-1 network structure with LM learning algorithm. ANN was notably successful in predicting the damage factor due to higher R-2 and lower RMSE and MEP.