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
E-Mail: umasekar.g@ktr.srmuniv.ac.in
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.
(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 weight percentage Composition in
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 Uncoated cemented carbide
Tool insert ISO code CNMG120404-SF H13A Inscribed circle diameter 12.7 mm
Included angle 80°
Clearance angle major 0° Corner radius 0.3969 mm Insert thickness 4.7625 ±0.001 mm Cutting edge length 12.8959 mm Cutting edge condition ER treated cutting edge
Material classification Titanium, heat resistant alloys
2.2 Machining arrangement details
The turning tests were conducted on CNC lathe (GeddeWeiler, model UNITURN-300) using cemented carbide (CNMG120404-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
Figure-2. CNC lathe employed for conducting machining experiments.
Figure-3. Surface roughness tester (surfcom) used to
measure the surface quality after finish turning.
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) 0-100 mm Column movement (C-axis) 300 mm
Measure speed 0.300 mm/s
Table-4. Taguchi L9orthogonal array employed in the experimental work.
S. No. Cutting speed (v) m/min mm/rev Feed (f) Depth of cut (d) mm Surface roughness (Ra) µm S/N Ratio values
1 25 0.10 0.25 0.6765 3.3946
2 25 0.15 0.50 0.9355 0.5791
3 25 0.20 0.75 1.5098 -3.5784
4 50 0.10 0.50 0.6220 4.1242
5 50 0.15 0.75 0.7566 5.0891
6 50 0.20 0.25 1.0193 -0.1660
7 75 0.10 0.75 0.5929 4.5404
8 75 0.15 0.25 0.5545 5.1219
9 75 0.20 0.50 0.7021 3.0720
2.3 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).
Titanium alloy
bar
Table-5. Machining parameters and their values at three levels.
Machining parameters Level 1 Level 2 Level 3
Cutting Speed (v) in m/min 25 50 75 Feed (f) in mm/rev 0.1 0.15 0.2 Depth of cut (d) in mm 0.25 0.50 0.75
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.
Table-6. ANOVA table related to surface roughness of titanium alloy.
Source DoF squares Sum of variance Mean F-value Percentage of contribution
Cutting speed v (m/min) 2 0.27150 0.13575 4.96 37.26% Feed f (mm/rev) 2 0.32119 0.16059 5.86 44.08% Depth of cut d (mm) 2 0.08118 0.04059 1.48 11.14%
Error 2 0.05479 0.02739 - 7.52%
Total 8 0.72865 - - 100%
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 Average surface roughness (Ra), µm
Cutting
speed Feed Depth of cut Cutting speed Feed Depth of cut
1 0.1318 4.0197 2.7835 1.0406 0.6305 0.7501 2 2.1269 2.7079 2.5918 0.7993 0.7489 0.7532 3 4.2448 -0.2241 1.1282 0.6165 1.0771 0.9531 Delta 4.1130 4.2439 1.6553 0.4241 0.4466 0.2030
Rank 2 1 3 2 1 3
(a) (b)
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.
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.
(a) (b)
Figure-5. (a) Surface plot of surface roughness vs. cutting speed, feed; (b) Contour plot of surface
roughness vs. cutting speed, feed
5. 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 37.26 %. 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.
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75 50 25 4 3 2 1 0 0.20 0.1 5
0.1 0 0.25 0.50 0.75
Cutting speed (m/min)
M ean o f S N rat io s
Feed (mm/rev) Depth of cut (mm) Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Smaller is better 25 50 75
1 .1 1 .0 0.9 0.8 0.7 0.6 0.20 0.1 5
0.1 0 0.25 0.50 0.75
Cutting speed (m/min)
M
ean
of
M
eans
Feed (mm/rev) Depth of cut (mm)
Main Effects Plot for Means Data Means hness g u o r e c a f r u s ( d e e F mm/rev)
uttigspeed(m/min)
C n Cutting speed (m/min)
Fe ed (mm /r ev ) 75 65 55 45 35 25 0.20 0.1 8 0.1 6 0.1 4 0.1 2 0.1 0 > – – – – < 0.6
0.6 0.8
0.8 1 .0 1 .0 1 .2 1 .2 1 .4 1 .4 meter) (micro roughnesssurface
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