INVESTIGATION OF SURFACE ROUGHNESS IN FINISH TURNING OF TITANIUM ALLOY TI-6AL-4V

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1 INVESTIGATION OF SURFACE ROUGHNESS IN FINISH TURNING OF TITANIUM ALLOY TI-6AL-4V V. G. Umasekar, M. Gopal, Kadivendi Rahul, Saini Saikiran and G. V. Sasanka Mowli Department of Mechanical Engineering, SRM University, Kattankulathur, Kanchipuram District, Tamil Nadu, India ABSTRACT Titanium alloys found broad applications in aviation, chemical, and automotive sectors because of its lightweight, high-temperature strength, and high corrosion resistance. This research paper investigates the influence of machining variables on the surface roughness of the machined component in finish turning of titanium alloy Ti-6Al-4V. The finish turning experiments conducted on GEEDEE WEILER CNC lathe at different speeds (v), feed (f) and depth of cut (d). Uncoated tungsten carbide insert was used as the cutting tool. Taguchi L9 orthogonal array utilized to perform the experiments. After conducting each test, surface roughness measured using surfcom tester. The optimal machining variables that provide the smaller value of surface roughness is determined based on signal to noise ratio method. Analysis of variance (ANOVA) was accomplished to identify the most affecting machining variable on surface roughness. The experimental result shows that the feed was the most dominant variable that influences the surface roughness. From the ANOVA, the surface roughness in finish turning is strongly influenced by the feed rate followed by speed and depth of cut. The minimum surface roughness was obtained by the following optimal variables via speed 75 m/min, feed rate 0.1 mm/rev and depth of cut 0.25 mm. Keywords: titanium alloy, surface roughness, tool inserts, Taguchi's design of experiment, signal-to-noise ratio, ANOVA. 1. INTRODUCTION Aviation industries use titanium alloys because of its high strength-to-weight ratio, good resistance against corrosion, and good strength at higher temperature. Ti- 6Al-4V is used in aerospace industries as turbine blade and structural components, and it is difficult to cut material due to its small thermal conductivity value and high chemical reactivity with the tool material while machining. The low thermal conductivity of the titanium alloys causes the temperature raise at the tool tip. Hence while machining, the tool insert wear quickly because of high temperature and strong adhesion of work material with tool [1]. The machined surface quality of the titanium alloy depends on various parameters like cutting speed, feed rate, cutting depth, nose radius of the tool insert, nose and flank wear, chatter, work-tool material properties [2]. The surface quality of a part is a critical one as it affects the mechanical properties. Since many times the poor surface quality of a mechanical part is a starting point for failure [3]. Hartung et al [4] research work conveys that tungsten carbide is one of the best tool insert material for machining titanium alloys. The research work of Venugopal et al [5] shows that uncoated tungsten carbide with cobalt as binder is suitable for machining titanium alloys. Cetin et al [6] investigated the influence of machining variables and coolants on surface roughness during the turning of AISI 304L. Their work shows that cutting tool feed is the most influencing parameter on surface roughness. Nithyanandam et al [7] investigated the effect of cutting variables on surface roughness employing tool insert with 0.8 mm nose radius. Their work reveals that feed rate is the dominant factor having 41.69% contribution followed by cutting speed and cutting depth. In this research work, the optimization of cutting variables investigated to get the minimum surface roughness during finish turning process of Ti-6Al-4V by employing 0.4 mm nose radius insert at dry machining condition. 2. MATERIALS AND METHODS 2.1 Workpiece material and cutting tool details Titanium alloy Ti-6Al-4V bar having 60 mm diameter and 120 mm long is utilized as workpiece material in this experiment. Figure-1(a) shows the titanium alloy bar used in this work. Figure-1(b) shows the microstructure of the grade-5 aerospace titanium alloy. Table-1 shows the titanium alloy chemical ingredients. The cutting tool used in the experiment was uncoated cemented carbide insert (CNMG SF H13A) from Sandvik with 0.4 mm nose radius. This insert has four cutting edges. Figure-1 (c) presents the tool insert employed in this experiment. Table-2 presents the specifications of the tool insert. 5029

2 (a) (b) (c) Figure1. (a) Titanium alloy bar used in the experimental work; (b) Micro structure of titanium alloy Ti-6Al-4V at 100X magnification (c) Cutting tool insert Table-1. Chemical composition of workpiece material Ti-6Al-4V. Element Element symbol Composition in weight percentage Aluminium Al 6.72% Vanadium V 3.83% Iron Fe 0.083% Carbon C 0.029% Titanium Ti 89.02% Table-2. Specifications of cutting tool insert employed in the experiments. Cutting tool insert Tool insert ISO code Inscribed circle diameter Uncoated cemented carbide CNMG SF H13A 12.7 mm Included angle 80 Clearance angle major 0 Corner radius Insert thickness Cutting edge length Cutting edge condition Material classification 2.2 Machining arrangement details The turning tests were conducted on CNC lathe (GeddeWeiler, model UNITURN-300) using cemented carbide (CNMG SF H13A) cutting tool for a cut length of 100 mm at dry machining condition. Table-5 display the list of tests in Taguchi L9 orthogonal array. After each test, the insert indexed, so that new cutting mm ±0.001 mm mm ER treated cutting edge Titanium, heat resistant alloys edge performs machining. Figure-2 shows the CNC lathe utilized to conduct the experiments. The surface roughness of the machined component is measured using the surface roughness tester (Surfcom) as shown in Figure-3. Table-3 presents the specifications of the surface roughness tester employed in this research work. 5030

3 Titanium alloy bar Cutting Tool Insert Figure-2. CNC lathe employed for conducting machining experiments. Table-3. Specifications of Surfcom apparatus for surface roughness testing. Stylus material Diamond Stylus diameter 2 µm Stylus deflection (z-axis) ± 400 µm Transverse movement of the probe (x-axis) mm Column movement (C-axis) 300 mm Measure speed mm/s Figure-3. Surface roughness tester (surfcom) used to measure the surface quality after finish turning. S. No. Table-4. Taguchi L9orthogonal array employed in the experimental work. Cutting speed (v) m/min Feed (f) mm/rev Depth of cut (d) mm Surface roughness (Ra) µm S/N Ratio values Experimental work procedure The experimental work conducted by using the machining variables through cutting speed, tool feed and cutting depth. These parameters are called control factors. Each machining parameter involves three levels, named as 1, 2, and 3. Table-4 presents the process parameters (factors) and their levels. The experimental work is designed by Taguchi L9 orthogonal array using Minitab17 and further signal-to-noise ratio analysis. In S-N ratio calculation, minimum the better option selected by the objective of the research work. Then analysis of variance was accomplished to know which machining variable have the greatest influence on the output parameter (surface roughness). 5031

4 Table-5. Machining parameters and their values at three levels. Machining parameters Level 1 Level 2 Level 3 Cutting Speed (v) in m/min Feed (f) in mm/rev Depth of cut (d) in mm The ratio between the average value and the standard deviation is referred as signal-to-noise ratio. S/N ratio is utilized to measure the quality characteristic deviations from the desired value [8, 9]. This research work looks for minimum surface roughness in the machined component. The signal-to-noise ratio for the experimental results obtained with the help of Minitab17 software. The optimum level of the machining parameters can be obtained by considering the highest S/N ratio value [10]. The objective of the experimental work is to minimize and control variation of a process. Afterward, a decision must be made about which variables influence the performance of a process. Analysis of variance is the statistical method applied to the results of the experiments in determining the contribution of individual machining variable against a stated level of confidence. The interpretation of ANOVA table for this research work assists to identify which of the variables require control and which do not. ANOVA and mean effect plots generated with the help of Minitab17 software. 4. RESULTS AND DISCUSSIONS The ANOVA tool analyzed the results of the experiments for getting the significant factors affecting the performance measures. On the basis of ANOVA results, the machining variable feed rate (f) had more effect of 44.08% on the output variable surface roughness. Cutting speed had a contribution of 37.26% on the surface roughness. The depth of cut has less effect of 11.14% on the surface roughness. Table-6 displays the result of ANOVA for the surface roughness. The error percentage is 7.52% which is lesser than the allowable limit error value of 15%. This shows that design, conduction, observation, and analysis are in the right direction. Source Table-6. ANOVA table related to surface roughness of titanium alloy. DoF Sum of squares Mean variance F-value Percentage of contribution Cutting speed v (m/min) % Feed f (mm/rev) % Depth of cut d (mm) % Error % Total % The S-N ratios for the experimental results obtained with the help of Minitab 17. Depending on the signal-to-noise ratio analysis, the optimal process parameters for surface roughness are the cutting speed 75 m/min, the feed rate 0.1 mm/rev and depth of cut 0.25mm. Table-7 presents the S-N ratio and mean value for surface roughness. Figure-4 (a) & (b) display the main effect graph for S-N ratio and means of surface roughness. From Figure-4 (a), the factor level with the largest S-N ratio is giving the smaller values of surface roughness, and it is the optimal parameters. Table-7. Response table pertaining to S-N ratio (surface roughness and mean surface roughness). Levels S-N ratio values related to surface roughness Cutting Depth of Feed speed cut Average surface roughness (Ra), µm Cutting speed Feed Depth of cut Delta Rank Optimal Parameters

5 Main Effects Plot for SN ratios Data Means Main Effects Plot for Means Data Means Cutting speed (m/min) Feed (mm/rev) Depth of cut (mm) 1.1 Cutting speed (m/min) Feed (mm/rev) Depth of cut (mm) Mean of SN ratios Mean of Means Signal-to-noise: Smaller is better (a) Figure-4. (a) Main effects graph of S-N ratio with respect to cutting speed, feed and depth of cut; (b) Main effects graph of Means with respect to cutting speed, feed and depth of cut (b) Figure-5(a) displays the surface graph between the surface roughness and input factors through cutting speed, feed. The surface plot reveals that surface roughness reduces with higher cutting speed and smaller feed. And the higher value of feed and the smaller amount of cutting speed leads to higher surface roughness. Figure- 5(b) displays the contour graph of the surface roughness and cutting speed, feed. surface roughness Cu tting speed (m/min) (a) F eed ( mm/ rev) Cutting speed (m/min) Figure-5. (a) Surface plot of surface roughness vs. cutting speed, feed; (b) Contour plot of surface roughness vs. cutting speed, feed Feed (mm/rev) Contour Plot of surface roughnes vs Cutting speed and Feed (b) surface roughness (micro meter) < > CONCLUSIONS In this research work, the influence of machining variables on surface roughness investigated using Taguchi L9 orthogonal array during finish turning of titanium alloy Ti-6Al-4V. The conclusions arrived from this experimental and analysis works are as follows: The optimal finish turning parameters have been obtained to produce minimum surface roughness. The minimum surface roughness obtained using cutting speed of 75 m/min, the feed of 0.1 mm/rev and cutting depth of 0.25 mm. The cutting tool feed is the major factor that controls (44.08%) the surface roughness of the machined component. Next to feed, the control factor cutting speed affected the surface roughness by %. The cutting depth showed a little contribution of about 11.14% to surface roughness. This shows that first feed rate next cutting speed need to be carefully controlled to have a better surface finish on the machined components. REFERENCES [1] Ramesh S, Karunamoorthy L, Palanikumar K Measurement and Analysis of Surface Roughness in Turning of Aerospace Titanium Alloy (gr5). Measurement. 45: [2] Yang Houchuan, Chen Zhitong, Zhou Zitong Influence of Cutting Speed and Tool Wear on the Surface Integrity of the Titanium Alloy Ti-1023 during Milling. International Journal of Advanced Manufacturing Technology. 78: [3] Navneet Khanna, Davim J P Design-of- Experiments Application in Machining Titanium Alloys for Aerospace Structural Components. Measurement. 61:

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