Yazar "Ayyildiz, Mustafa" seçeneğine göre listele

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    Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy
    (Hindawi Ltd, 2021) Eser, Aykut; Ayyildiz, Elmas Askar; Ayyildiz, Mustafa; Kara, Fuat
    This study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed, depth of cut, and feed rate were evaluated as input parameters for experimental design. For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg-Marquardt, scaled conjugate gradient, quasi-Newton backpropagation, and resilient backpropagation. The best consequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures. The statistical analysis was performed with RSM-based second-order mathematics model. The influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). The ANOVA results show that the depth of cut is the most effective parameter on surface roughness. Prediction models developed using ANN and RSM were compared in terms of prediction accuracy R2, MEP, and RMSE. The data estimated from ANN and RSM were realized to be very close to the data acquired from experimental studies. The value R-2 of RSM model was higher than the values of the ANN model which demonstrated the stability and sturdiness of the RSM method.
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    Effect of machinability, microstructure and hardness of deep cryogenic treatment in hard turning of AISI D2 steel with ceramic cutting
    (Elsevier, 2020) Kara, Fuat; Karabatak, Mustafa; Ayyildiz, Mustafa; Nas, Engin
    This study examined the hard turning of AISI D2 cold work tool steel subjected to deep cryogenic processing and tempering and investigated the effects on surface roughness and tool wear. In addition, the effects of the deep cryogenic processes on mechanical properties (macro and micro hardness) and microstructure were investigated. Three groups of test samples were evaluated: conventional heat treatment (CHT), deep cryogenic treatment (DCT-36) and deep cryogenic treatment with tempering (DCTT-36). The samples in the first group were subjected to only CHT to 62 HRc hardness. The second group (DCT-36) underwent processing for 36 h at -145 degrees C after conventional heat treatment. The latter group (DCTT-36) had been subjected to both conventional heat treatment and deep cryogenic treatment followed by 2 h of tempering at 200 degrees C. In the experiments, Al2O3 + TiC matrix-based untreated mixed alumina ceramic (AB30) and Al2O3 + TiC matrix-based TiN-coated ceramic (AB2010) cutting tools were used. The artificial intelligence method known as artificial neural networks (ANNs) was used to estimate the surface roughness based on cutting speed, cutting tool, workpiece, depth of cut and feed rate. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. Three different cutting speeds (50, 100 and 150 m/min), three different feed rates (0.08, 0.16 and 0.24 mm/rev) and three different cutting depths (0.25, 0.50 and 0.75 mm) were selected. Tool wear experiments were carried out at a cutting speed of 150 m/min, a feed rate of 0.08 mm/rev and a cutting depth of 0.6 mm. As a result of the experiments, the best results for both surface roughness and tool wear were obtained with the DCTT-36 sample. When cutting tools were compared, the best results for surface roughness and tool wear were obtained with the coated ceramic tool (AB2010). The macroscopic and micro hardness values were highest for the DCT-36. From the microstructural point of view, the DCTT-36 sample showed the best results with homogeneous and thinner secondary carbide formations. (C) 2019 The Authors. Published by Elsevier B.V.
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    Optimization of Surface Roughness in Drilling Medium-Density Fiberboard with a Parallel Robot
    (Hindawi Ltd, 2021) Ayyildiz, Elmas Askar; Ayyildiz, Mustafa; Kara, Fuat
    This study focuses on the examination of the effect of cutting parameters on surface roughness when drilling medium-density fiberboard (MDF) with a parallel robot. Taguchi technique was applied to find the optimum drilling parameters and, later, the drilling processing. Experimental layout was established using the Taguchi L-18 orthogonal array, and experimental data were examined via a statistical analysis of variance (ANOVA). Experimental results were performed by multiple regression analysis (linear and quadratic). Correlation coefficient (R-2) was found 99.46% for surface roughness with the quadratic regression model. By the Taguchi analysis, the optimum values for the surface roughness were found to be a point angle of 118 degrees, a cutting speed of 47.1 m/min, and a feed rate of 0.01 mm/rev. The optimization outcomes presented that the Taguchi technique had been successfully performed to decide the optimal surface roughness of the MDF in the drilling.