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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 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.
