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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 Optimization and evaluation of dry and minimum quantity lubricating methods on machinability of AISI 4140 using Taguchi design and ANOVA(SAGE Journals, 2020-07-05) Gürbüz, Hüseyin; Gönülaçar, Yunus EmreIn this work, it is aimed to study the effects of dry machining and minimum quantity lubrication application on machinability in turning AISI 4140 steel by utilizing different cutting parameters. Also, this study contains effects and optimization of cutting conditions (dry and minimum quantity lubricating), feed rate, and cutting speed on surface roughness (Ra) and main cutting forces (Fc) determined by employing the Taguchi method. At the end of experiments, it was established that compared to dry machining operations, minimum quantity lubricating significantly reduced cutting tool wear, while Fc and Ra decreased in general. Analyses of variance, regression analysis, signal-to-noise ratio, and orthogonal array were employed to analyze the effects and contributions of independent variables on dependent variables. The optimum levels of the dependent variables for reducing Fc and Ra using signal-to-noise rates were established. According to signal-to-noise ratios, minimum quantity lubricating had a more important effect on Fc and Ra than dry machining. The optimal conditions for Fc and Ra were at 0.16 mm/rev feed rate, 125 m/min cutting speed at minimum quantity lubricating. Analysis of variance results demonstrated that the feed rate is the most influential independent variable on Fc (93.976 %) and Ra (89.352 %). Validation test results exhibited that the Taguchi method and regression analysis were highly achieved methods in the optimization of independent variables for dependent variables. Taguchi optimization technique and regression analysis obtained from Fc (R2Tag. = 0.972 and R2Rag. = 0.997) and Ra (R2Tag. = 0.985 and R2Rag. = 0.996) measurements match really well with the experimental dataÖğe 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.
