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    Öğe
    Mathematical Modeling and Optimization of Milling Parameters in AA 5083 Aluminum Alloy
    (2019) Basar, Gokhan; Kahraman, Funda; Önder, Ganime Tuğba
    An experimental study was carried out to determine the effect of different cutting parameters such as feed,spindle speed and depth of cut on surface roughness in face milling of 5083 aluminum alloy. The mathematicalmodel was developed to estimate surface roughness using Response Surface Methodology. The significantcontribution of cutting parameters was detected by analysis of variance. Statistical analysis indicated that feedand spindle speed have the most considerable influence on surface roughness. After developed mathematicalmodel, desirability function analysis was performed to minimize the surface roughness. The lowest surfaceroughness (0.41 µm) was acquired at a feed of 3008 mm/min, a spindle speed of 5981 rpm and a depth of cut of0.54 mm.
  • [ X ]
    Öğe
    PREDICTION OF SURFACE HARDNESS IN A BURNISHING PROCESS USING TAGUCHI METHOD, FUZZY LOGIC MODEL AND REGRESSION ANALYSIS
    (Yildiz Technical Univ, 2018) Basar, Gokhan; Kahraman, Funda
    The available work is aimed for comparison and estimation of surface hardness in ball burnishing process of aluminum alloy based upon the Taguchi technique, Fuzzy logic and regression models. The ball burnishing parameters like burnishing speed, force, feed rate and number of passes were designed using Taguchi L-25 orthogonal design matrix. Taguchi's signal to noise ratio was used to optimize the surface hardness. The effect of burnishing parameters on surface hardness was established by analysis of variance. Fuzzy logic was conducted using Matlab Toolbox. Taguchi technique, second order regression model and variance analysis were developed using MINITAB 17. The predicted hardness values of performance parameters were operated to compare the distinct models. The results of predicted models indicated that the consistent predictive model is the fuzzy logic model. With high correlation coefficient (R-2 = 97.52 %), the model was regarded adequately accurate.