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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 Model and formulation in grinding mechanism having advanced secondary rotational axis(SAGE, 2019-04-15) Adıyaman, Oktay; Demir, Zülküf‘‘Grinding Mechanism having Advanced Secondary Rotational Axis’’ is one of the newer plane surface grinding methods that has an uncommon abrasion mechanism. Unlike conventional methods, in Grinding Mechanism having Advanced Secondary Rotational Axis, there are two rotations of a wheel. The first rotation is the same as the conventional grinding methods, which is the circumferential rotation. The other rotation is the newly developed axial rotation, where the wheel rotates around itself perpendicular to its radial axis. In the grinding process, the grinding force, energy, power, and temperature are directly related to the material removal rate. In this article, the chip model in Grinding Mechanism having Advanced Secondary Rotational Axis was addressed and material removal rate was reformulated. The new chip ratio formula was adapted to the grinding force, energy, power, and temperature in the conventional plane surface grinding method. The chip formed in the conventional plane surface grinding method consists of two-dimensional xy plane. In Grinding Mechanism having Advanced Secondary Rotational Axis, on the other hand, the chips consist of threedimensional xyz plane. The reason behind this is the second rotation obtained in Grinding Mechanism having Advanced Secondary Rotational Axis (axial rotational motion). The chip model was obtained from the combination of two rotations in Grinding Mechanism having Advanced Secondary Rotational Axis. As a result, the resulting chip model increased the material removal rate only slightly and this increase was negligible. Accordingly, an increase in grinding force, energy, power, and temperature was observed at negligible rates.Öğe Küreselleştirme ısıl işlemi uygulanmış AISI 1050 çeliǧinin yüzey pürüzlülük deǧerlerinin yapay sinir aǧları ile modellenmesi(IEEE, 2017-10-30) Baday, Şehmus; Başak, Hüdayim; Sönmez, FikretEstimation of surface roughness values, which is an indication of workpiece quality, is important in terms of reducing the cost and duration of machining. In this study, the surface roughness values of the medium carbon steel subjected to the spheronization heat treatment have estimated by artificial neural networks. ANN network model have been created by being chosen feedforward back propagation network model, the adoption of network structure and learning function LEARNGDM, TRAINLM as training algorithm, MSE for assessment of network performance and two hidden layers. The value of each neuron in the network have been transferred another layer by TANSIG, LOGSIG and PURELIN transfer functions. As a result, the artificial neural networks trained and tested have been found to be easy to use for estimating surface roughness values with a high percentage of R = 0.99001 according to MSE performance.
