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Öğe Milling Inconel 718 workpiece with cryogenically treated and untreated cutting tools(SpringerLink, 2021-07-17) Gürbüz, Hüseyin; Baday, ŞehmusAlthough Inconel 718 is an important material for modern aircraft and aerospace, it is a kind material, which is known to have low machinability. Especially, while these types of materials are machined, high cutting temperatures, BUE on cutting tool, high cutting forces, and work hardening occur. Therefore, in recent years, instead of producing new cutting tools that can withstand these difficult conditions, cryogenic process, which is a heat treatment method to increase the wear resistance and hardness of the cutting tool, has been applied. In this experimental study, feed force, surface roughness, vibration, cutting tool wear, hardness, and abrasive wear values that occurred as a result of milling of Inconel 718 material by means of cryogenically treated and untreated cutting tools were investigated. Three different cutting speeds (35-45-55 m/min) and three different feed rates (0.02- 0.03-0.04 mm/tooth) at constant depth of cut (0.2 mm) were used as cutting parameters in the experiments. As a result of the experiments, lower feed forces, surface roughness, vibration, and cutting tool wear were obtained with cryogenically treated cutting tools. As the feed rate and cutting speed were increased, it was seen that surface roughness, vibration, and feed force values increased. At the end of the experiments, it was established that there was a significant relation between vibration and surface roughness. However, there appeared an inverse proportion between abrasive wear and hardness values. While BUE did not occur during cryogenically treated cutting tools, it was observed that BUE occurred in cutting tools which were not cryogenically treated. Also, in this study, the statistical validity of the experimental values was tested with the help of secondorder equations and analyses of variance (ANOVA). R2 values obtained as 99.14%, 99.76%, and 97.98% for vibration, surface roughness, and feed force values were modeled statistically with the help of second-order equations, respectively.Öğe Analysis of spheroidized AISI 1050 steel in terms of cutting forces and surface quality(Slovenska Akademia Vied, 2016) Baday, Şehmus; Başak, Hüdayim; Güral, AhmetIn this study, the effects of microstructure differences obtained with the application of different spheroidizing heat treatment cycles on medium carbon steel on cutting forces and surface roughness values were investigated. For this purpose, a group of AISI 1050 materials was annealed at 700°C below Ac1 temperature for 720 min and cementite phases were spheroidized by the traditional method. Another group of materials was quenched after austenitization at 850°C for 15 min and then cementites were spheroidized in the ferrite matrix by over-tempering separately at 600°C for 15 and 60 min and at 700°C for 60 min. Machining of the samples was tested under dry cutting conditions in CNC turning center with SNMG 120408 cementite carbide cutting tool and proper PSBNR 2525M12 tool holder with 75-degree edge angle. Cutting forces of traditionally spheroidized samples were lower than the samples spheroidized after quenching. In addition, their cutting forces decreased due to the increase in the average sizes of spheroidal cementite. Minimum surface roughness value was obtained from the samples which were spheroidized at 600°C for 15 min after quenching. However, surface roughness rate of the sample increased as spheroidizing time increased.Öğe Modelling of effects of various chip breaker forms on surface roughness in turning operations by utilizing artificial neural networks(Gazi Üniversitesi, 2016-03-01) Gürbüz, Hüseyin; Baday, Şehmus; Sözen, AdnanIn this study, the effects of different chip breaker forms and cutting parameters on the surface roughness on machined surfaces were investigated experimentally in turning of AISI 1050 steel; and values of surface roughness obtained from experiments were determined with empirical equations using artificial neural networks. The utilizing of ANN was offered to determine the surface roughness depending on chip breaker forms and cutting parameters of AISI 1050 steel. The back propagation learning algorithm and fermi transfer function were used in artificial neural network. Experimental measurements data were employed as training and test data in order to train the neural network created. The best fitting training data set was attained with ten neurons in two hidden layers 6 of which were at first hidden layer and 4 of which were at second hidden layer, making it possible to predict surface roughness with precision at least as good as that of the experimental error over the entire experimental range. After network training, R2 value was found as 0.978, and average error as 0.018%. When the results of mathematical modelling are examined, the computed surface roughness is observed to be apparently within acceptable valuesÖğe Investigation of effects of different cutting and machining parameters on surface roughness and main cutting forces via response surface method(European Journal of Technique (EJT), 2020-12-30) Gürbüz, Hüseyin; Gönülaçar, Yunus EmreIn this study, the effects of cutting speed, feed rate and minimum quantity lubrication (MQL), frequently employed in machining applications, on main cutting force (Fc) and average surface roughness (Ra), resulting from turning AISI 4140, were investigated. For this purpose, analyses for Fc and Ra were performed utilizing Box-Behnken model. By using experimental parameters, the efficiency and the changes of the parameters on Fc and Ra were examined with the help of 13 experiments. In addition, the effectiveness of design models was investigated by creating different design models. The high success rate modelling for Fc and Ra was realized with 99% success as a result of analyses conducted according to Box-Behnken methods (BoxBehnken, Box-Behnken-Stepwise, Box-Behnken-Backward and BoxBehnken-Forward). The most effective parameter on Fc and Ra was found to be the feed rate according to analysis of variance (ANOVA). It was demonstrated that the estimations on the created Box-Behnken model were quite successful on the data initially entered into the system; and that R2 values obtained for Fc and Ra were 0.999 and 0.996, respectively. It was determined that optimum parameters for Fc were feed rate 0.25 mm/rev, cutting speed 125 m/min and cutting condition MQL2 ml/min, while they were feed rate 0.25 mm/rev, cutting speed 125 m/min and cutting condition MQL1 ml/min for Ra.Öğe Mathematical modelling and optimization of cutting force, tool wear and surface roughness by using artificial neural network and response surface methodology in milling of Ti-6242S(Applied Sciences-Basel, 2017-10-15) Çelik, Yahya Hışman; Kılıçkap, Erol; Yardımeden, AhmetIn this paper, an experimental study was conducted to determine the effect of different cutting parameters such as cutting speed, feed rate, and depth of cut on cutting force, surface roughness, and tool wear in the milling of Ti-6242S alloy using the cemented carbide (WC) end mills with a 10 mm diameter. Data obtained from experiments were defined both Artificial Neural Network (ANN) and Response Surface Methodology (RSM). ANN trained network using Levenberg-Marquardt (LM) and weights were trained. On the other hand, the mathematical models in RSM were created applying Box Behnken design. Values obtained from the ANN and the RSM was found to be very close to the data obtained from experimental studies. The lowest cutting force and surface roughness were obtained at high cutting speeds and low feed rate and depth of cut. The minimum tool wear was obtained at low cutting speed, feed rate, and depth of cut.Öğe Investigation of experimental study of end milling of CFRP composite(De Gruyter, 2013-12-12) Çelik, Yahya Hışman; Kılıçkap, Erol; Yardımeden, AhmetCarbon fiber-reinforced plastic (CFRP) composites are materials that are difficult to machine due to the anisotropic and heterogeneous properties of the material and poor surface quality, which can be seen during the machining process. The machining of these materials causes delamination and surface roughness owing to excessive cutting forces. This causes the material not to be used. The reduction of damage and surface roughness is an important aspect for product quality. Therefore, the experimental study carried out on milling of CFRP composite material is of great importance. End milling tests were performed at CNC milling vertical machining center. In the experiments, parameters considered for the end milling of CFRP were cutting speed, feed rate, and flute number of end mill. The results showed that damage, surface roughness, and cutting forces were affected by cutting parameters and flute number of end mill. The best machining conditions were achieved at low feed rate and four-flute end mill.Öğe Effect of MQL flow rate on machinability of AISI 4140 steel(Taylor & Francis, 2020-06-27) Gürbüz, Hüseyin; Gönülaçar, Yunus Emre; Baday, ŞehmusMany studies were performed about the influence of minimum quantity lubrication (MQL) technique on cutting performance in the literature, but there is no paper examining the effect of different MQL flow rates and cutting parameters on machinability of AISI 4140 material as a whole. In this study, the effects of different MQL flow rates and cutting parameters on surface roughness, main cutting force and cutting tool flank wear (VB), with great importance among the machinability criteria, and forming as a result of the machining of AISI 4140, were revealed. At the end of the experiments, it was determined that rise of flow rate affected main cutting forces positively to a certain extent; yet, it exhibited no significant effect on surface roughness, but reduced VB. Also, it was observed that both main cutting force and surface roughness increased with the increase of feed, while generally decreased with the increase of cutting speed. It was seen that flank wear was positively affected by the increase in flow rate; and this decreased with the increase in flow rate. R2 values obtained as 99.8% and 99.9% for main cutting forces and surface roughness values modeled statistically with the help of quadratic equations, respectively.Öğe Investigation of mechanical characteristics of GFRP composites produced from chopped glass fber and application of taguchi methods to turning operations(SpringerLink, 2020-04-08) Çelik, Yahya Hışman; Türkan, CihatGlass fber-reinforced plastic (GFRP) composites take place in engineering materials owing to their low-weight and high-mechanical properties. In some cases, they need to be shaped by machining before using in industrial applications. However, when these composites are machined, many problems such as bad surface quality, rapid tool wear are encountered. Therefore, optimization of cutting parameters is essential to eliminate or minimize these problems. In this study, GFRP composites were produced by combining polyester matrix material with glass fbers (GF) having 6 mm, 6–12 mm, 12 mm fber length, and 20%, 25%, 30% fber ratio by weight. The tensile strengths of these composites were investigated. Turning tests were also performed with cutting speeds of 40, 80, and 120 m/min, feed rates of 0.1, 0.2 and 0.3 mm/rev, and depth of cut of 1, 2, and 3 mm, according to Taguchi L27 standard orthogonal array method. The efect of fber length and ratio, and cutting parameters on cutting forces and surface roughness were analyzed. As a result of the experiments, it was observed that the reinforced polymer matrix with GF provide to increase the tensile strength. The highest tensile strength was obtained as 55.95 MPa from the composite having a fber length of 12 mm and a fber ratio of 25%. Besides, the feed rate was determined as the most efective parameter among the all parameters on both cutting force and surface roughness. Therefore, the feed rate should be chosen low for lower cutting force and surface roughness values.Öğe Estimate of cutting forces and surface roughness in end milling of glass fiber reinforced plastic composites using fuzzy logic system(Walter de Gruyter, 2014-06-01) Çelik, Yahya Hışman; Kılıçkap, Erol; Yardımeden, AhmetMilling glass fiber reinforced plastic (GFRP) composite materials are problematic, owing to, e.g., nonhomogeneous and anisotropic properties and effects of plastic deformation. To reduce these problems, the effects of cutting speed, feed rate, and the number of flutes on surface roughness and of thrust forces occurring during the milling of GFRP composite materials were investigated by both experimental and fuzzy logic models. Experiments were performed at 30 m/min, 60 m/min, and 90 m/min cutting speeds, at 0.1 mm/rev, 0.15 mm/rev, and 0.2 mm/rev feed rates and 10 mm diameters in a cemented carbide end mill, which has two, three, and four flutes without cutting fluids. The values obtained from experiments were defined by a fuzzy logic model. A fuzzy logic model was employed to estimate the surface roughness and thrust forces for different cutting parameters. As a result of both the experimental study and the fuzzy logic model, while the minimum thrust force was obtained at low cutting speeds, and feed rates and a high number of flutes end mill, the best surface quality was obtained at low feed rates, high cutting speed, and number of flutes end mill.Öğe Estimation of surface roughness and cutting speed in CNC WEDM by artificial neural network that employed trainable activation function(SAGE Journals, 2021-02-01) Gürbüz, HüseyinActivation functions are the most significant properties of artificial neural networks (ANN) because these functions are directly related with the ability of ANN in learning or modelling a system or a function. Furthermore, another reason for the significance of the fact that determination of optimal activation function in ANN is its relationship with success level. In this experimental study, the effects of different types of wire electrodes, cooling techniques and workpiece materials on surface roughness (Ra) and cutting speed (Vc) in wire electrical discharge machining (WEDM) were investigated by using trainable activation functions (AFt) and modelling them in ANNs. So far, a number of methods have been performed according to the data set in order to optimally predict Ra and Vc results. Among these methods, randomized ANN with AFt was found to be the best one for robust prediction according to RMSE values. While the value was 0.280 for Vc, it was 0.2104 for Ra. Optimum activation functions in Ra and Vc were found at first and third degree trainable functions, respectively.
