Yazar "Gullu, Abdulkadir" seçeneğine göre listele

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    Analysis of cutting parameters and cooling/lubrication methods for sustainable machining in turning of Haynes 25 superalloy
    (Elsevier Sci Ltd, 2016) Sarikaya, Murat; Yilmaz, Volkan; Gullu, Abdulkadir
    While the use of cutting oils in machining operations facilitate material removal, the use of these oils are questioned based on the risks they pose for operator health and the environment. As an alternative to the excessive use of cutting oils, the Minimum Quantity Lubrication (MQL) method aims to reduce the use of cutting fluids as a step in achieving clean, environmentally friendly, and sustainable manufacturing. In this study, the machinability of cobalt-based Haynes 25 superalloy, which is a difficult-to-machine alloy used in strategic applications, was investigated under three separate-cutting methods (dry, conventional cooling and lubrication, and minimum quantity lubrication). The experiments were conducted on a CNC turning machine using uncoated carbide cutting tools using four separate cutting speeds (15 m/min, 30 m/min, 45 m/min, and 60 m/min), three separate feed, rates (0.08 mm/rev, 0.12 mm/rev and 0.16 mm/rev), and a fixed depth of cut value (1 mm). To determine the relationships among machining parameters and outputs, tool wear (VN) and surface roughness (R-a) values were measured. Additionally, the wear mechanisms acting on the cutting inserts were determined using scanning electron microscope (SEM). Following the conclusion of experiments, the Taguchi's signal to noise ratio (S/N) analysis was used to establish the optimal set of cutting parameters. In conclusion, when the MQL method was employed in conjunction with high pressure, the amount of oil used was reduced while the machinability of the material was improved. Tests conducted under all three methods of cutting revealed poor surface roughness at low cutting speeds, and high tool wear at high cutting speeds. (C) 2016 Elsevier Ltd. All rights reserved.
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    Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25
    (Elsevier Sci Ltd, 2015) Sarikaya, Murat; Gullu, Abdulkadir
    In manufacturing industry, the effect of cutting fluids has been known on the health, environment and productivity in machining operations such as turning, milling, drilling, etc. Minimum Quantity Lubrication (MQL) is an effective tool to minimize the damage of cutting fluids on health and environment in cutting processes. Thus, optimal process parameters must be determined under MQL cooling/lubrication condition to determine the maximum productivity. This paper presents an approach for optimization of machining parameters with multi-response outputs using design of experiment in turning. For experimental design, tests were planned based on Taguchi's 1.9 orthogonal array. During the turning of cobalt base super alloy Haynes 25 which is a difficult-to-cut material, process performance indicators such as flank wear, notch wear and surface roughness were measured. The process parameters which are cutting fluid (CFs), fluid flow rate (Q) and cutting speed (Vc) were simultaneously optimized by taking the multiresponse outputs by Taguchi based grey relational analysis (GRA) into consideration. Taguchi's signal to noise ratio was applied with larger-the-better approach to obtain the best combination. Three mathematical models were created using response surface regression methodology. According to the multiresponse optimization results, which were obtained from the largest signal to noise ratio of the grey relational grade (GRG), the optimum combination was vegetable base cutting fluid, 180 mL/h fluid flow rate and 30 m/min cutting speed to simultaneously minimize the tool wear patterns and surface roughness. In addition, it was found out that the percentage improvement in GRG with the multiple responses is 39.4%. It was clearly shown that the performance indicators are significantly improved using this approach. (C) 2014 Elsevier Ltd. All rights reserved.
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    Öğe
    Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL
    (Elsevier Sci Ltd, 2014) Sarikaya, Murat; Gullu, Abdulkadir
    In manufacturing industry, the effect of cutting fluids has been known on the health, environment and productivity at machining operations such as turning, milling, drilling, etc. Surface roughness is a common indicator of the quality characteristics for machining processes. The machining process is more complex, and therefore, it is very hard to determine the effects of process parameters on surface quality in all turning operations. In this study, design of experiments has been used to study the effect of the main turning parameters such as cooling condition, cutting speed, feed rate and depth of cut on arithmetic average roughness (Ra) and average maximum height of the profile (Rz) when turning of AISI 1050 steel. Experiments have been performed under dry cutting (DC), conventional wet cooling (CC) and MQL. Tests are designed according to Taguchi's L-16 (4(3) x 2(1)) orthogonal array. ANOVA analysis was performed to determine the importance of machining parameters on the Ra and Rz. The results were analyzed using 3D surface graphs, signal-to-noise ratios (S/N) and main effect graphs of means. Optimal operating parameters were determined using the S/N ratio and desirability function analysis. Mathematical models have been created for surface roughness, namely Ra and Rz, through response surface methodology (RSM). The results indicate that the most effective parameters are feed rate on the surface roughness. Cooling conditions are significantly effective on the surface roughness. MQL is a good tool in order to increase of the machined surface quality for cutting operations. (C) 2013 Elsevier Ltd. All rights reserved.