Post on 19-Mar-2023
International Journal of Applied Engineering Research
ISSN 0973-4562 Volume 10, Number 2 (2015) pp. 4191-4200
© Research India Publications
http://www.ripublication.com
Study on Surface Integrity of High Speed Turning of Inconel
718 Using Taguchi DOE Approach
Dr.M.Nataraj1, M.Ramamoorthy
2*, Dr. M.Pradeep Kumar
3
1Associate Professor, Government College of Technology, Coimbatore, India
2Research Scholar, Anna University, Chennai, India
3Associate Professor, Anna University, Chennai, India
1m_natanuragct@yahoo.com,
2ramus04_me@yahoo.co.in,
3pradeep@annauniv.edu
Abstract
In High Speed Machining Processes, a major quality related output is surface
roughness of the machined surfaces. Machining of the difficult-to-cut
materials under high speed machining conditions results a drastic decrease in
tool-life. Huge cutting force and higher cut edge temperature leads to poor
surface finish. An attempt has been made in this paper to study the influence
of cutting parameters on surface integrity of Inconel 718 nickel based super
alloy under cryogenic cooling conditions during high speed machining.
Experiments were carried out using Taguchi‟s L18 mixed orthogonal array
based quality design concept to arrive the best optimal parametric combination
by varying the cutting speed, feed, depth of cut, tool nose radius, air / LN2
pressure and the head level of LN2 in the cryocan. Analysis of variance
(ANOVA) was done to find out the significance of the machining parameters
while machining Inconel 718. Confirmation experiments were conducted to
validate the experimental investigation.
Keywords: Cryogenic cooling, cryocan, surface integrity, Inconel 718, DoE,
Orthogonal Array
Introduction Nowadays manufacturing industries are facing challenges in enhancing the surface
integrity along with increased productivity of the machined parts particularly for the
heat resistant super-alloys that are employed in aerospace applications. Machining
super-alloys to the desired configuration continues to be a challenge in the shop-floor.
Researchers have been focusing on such challenges to come up with innovative
solutions [1]. Surface qualities of the machined components are being characterized
by the degree of roughness and metallurgical damage which are together termed as
„surface integrity‟. Surface integrity had significant influence on the surface sensitive
4192 Dr.M.Nataraj
properties like fatigue, stress corrosion resistance and creep strength that are directly
related to the endurance life of the components [2]. Surface roughness is the most
important factor that affect the quality of a machined product. The poor surface
quality during machining is caused by the high cutting temperature developed during
machining at high cutting velocity and feed rate when the work material is difficult-
to-machine. Such high temperature causes dimensional deviation and premature
failure of cutting tools [3]. Exploring higher cutting speeds relay on the cutting tool
materials to a greater extent. The machining of heat resistant super alloys classified as
difficult-to-machine materials and hence the selection of cooling lubricant during the
machining operations is very important [4].
The surface finish of Inconel / Titanium alloys is very poor during the high speed
machining as the cutting fluids have no influence on cutting temperature and the tool
life. However high pressure soluble oil jet when applied at the chip–tool interface has
reduced the temperature in cutting zone and tool life is improved to some extent [5].
The conventional cutting fluids posed major ecological problem like environmental
pollution due to chemical break-down of the cutting fluid at high cutting temperature,
besides providing some marginal technological benefits. Further biologically
hazardous to the operator due to bacterial growth, requirements of additional system
for pumping, local storage, filtration, recycling, chilling and large space, water
pollution and soil contamination during final disposal were some other critical issues
to be faced [6]. Some works have been reported recently on cryogenic cooling by
liquid nitrogen jet in machining and grinding of various grades of steel for common
use. Cryogenic cooling provided less cutting forces, better surface finish and
improved tool life compared to dry machining [7]. Coated carbide and ceramics tools
were normally used for high speed machining of Inconel 718. But the right selection
of tungsten carbide cobalt alloy for substrate, associated coating materials, coating
procedure and cutting conditions are the main problems for the coated carbide tools
[8]. Many researchers have done investigation on the performance of TiALN coated
carbide as cutting tool for machining of Inconel 718 with LN2 as coolant [9].
Methodology
Workpiece and tool materials
In this research study Inconel 718 Nickel-based superalloy, a high-strength and
thermal-resistant material was chosen as workmaterial as it found applications in
aerospace, petroleum and nuclear energy industries and noted for its outstanding
corrosion resistance [10]. It possesses excellent mechanical properties at low and
intermediate temperatures (−250°C to 700 °C). High powered (6.6 kW) NAGMAT
175 universal lathe was used to machine the workpiece. Figure 1 illustrates the
experimental set up used for this research study.
Study on Surface Integrity of High Speed Turning of Inconel 718 Using et.al. 4193
Figure 1: Experimental setup with cryogenic cooling LN2 setup
A storage tank called cryocan was used to store the liquid nitrogen (LN2), the head
level of the tank is maintained at 0.6 m and 0.9 m during the experiments in order to
use Taguchi‟s mixed orthogonal array based design of experiments. The compressed
air is admitted into the cryocan and constant pressure is maintained using pressure
regulator mounted on the top of the cryocan. The removable insert was used which
encompass four working edges. For machining the work material, cutting tools with
three different tool nose radius were used viz., CNMP120404WM25CT (0.4 mm nose
radius) CNMP120408WM25PT (0.8 mm nose radius) and CNMP120412WM25PT
(1.2 mm nose radius). Tool holder is codified as PCLNR20-20K22 with common
active part tool geometry. Surface roughness (Ra) was measured off-line [11], Ra
value was measured for each of the cutting condition using a Mitutoyo Surftest 201
roughness meter. The length examined was 2.4 mm with a basic span of 3mm. The
values of Ra were measured within the range of 0.05–40 µm. This roughness was
directly measured on the workpiece without dismantling from the lathe in order to
reduce uncertainties due to resumption operations [11,12]. Measurements were made
three times at three reference lines equally positioned at 120o and an average of the
observation was taken into account.
Experimental design
Taguchi methods which combine the experiment design theory and the quality loss
function concept have been applied in process design since it has solved confusing
problems in manufacturing arena [13]. L18 Orthogonal Array (OA) based Design of
Experimental (DoE) approach was chosen for the experimental trail; it has good even
distribution of factorial interactions over the control factors [14]. Among the seven
influential cutting parameters, six parameters namely cut speed (ν), feed rate (f), depth
of cut (ap), tool nose radius (r), LN2 pressure (p), cutting time (t) each one was
4194 Dr.M.Nataraj
assigned with three levels and the remaining LN2 cryocan head level was assigned
with two level as shown in Table 1.
Table 1: Control Factors and its levels
Sl. No. Variables Control
factors
Levels
L 1 L 2 L 3
1 Cut speed (v) m/min A 44 73 102
2 Feed rate (f) mm/rev B 0.05 0.10 0.15
3 Depth of cut (ap) mm C 0.5 1.0 1.5
4 Nose radius (r) mm D 0.4 0.8 1.2
5 LN2 Pressure (p) bar E 0.5 1.0 1.5
6 Cutting time (t) sec F 4 7 10
7 Head of LN2 (h) m G 0.6 0.9 -
The surface roughness was measured as quality characteristics by adjusting the
parametric combinations. Experiments were analyzed using ANOVA to find the
significance of the machining variables.
Data Analysis Taguchi quality concept was used for the experiment plan as it employs a generic
Signal-to-Noise (S/N) ratio to quantify the variation. S/N ratios may be applicable
depending on the type of quality characteristics involved like “Smaller the Better”
(LTB), “Larger the Better” (LTB) and Nominal is the Best (NTP). The S/N ratios
were calculated using the equations [15]:
(1)
(2)
(3)
where η is the characteristic property, is standard deviation, y is observations
and n is the repeated number of the experiment which denotes the observed value in
decibel (dB). Table 2 shows the experimental plan showing the levels of control factor
and the corresponding S/N ratio obtained using Equation (3) for measuring the surface
roughness as response. The mean S/N ratio for each level of the cutting parameters is
summarized and is shown in Table 3. Figure 3 plotted using Table 3 shows the
response graph which indicates how far the changes in the control factor influence the
surface roughness. Further S/N curve enable to decide the optimal level of machining
variables. The optimal level of machining variables for better surface finish (least
roughness) obtained from the experiments is A3B1C3D2E3F1G2.
Study on Surface Integrity of High Speed Turning of Inconel 718 Using et.al. 4195
Table 2: Experimental results for surface roughness and S/N ratio
Sl No A B C D E F G
Surface roughness
(Ra) µm S/N
Ratio I II III
1 1 1 1 1 1 1 1 0.42 0.62 0.49 5.74
2 1 2 2 2 2 2 1 1.62 1.68 1.70 -4.44
3 1 3 3 3 3 3 1 1.90 1.67 1.98 -5.37
4 2 1 1 2 2 3 1 1.87 1.38 1.88 -4.73
5 2 2 2 3 3 1 1 1.89 1.46 1.86 -4.85
6 2 3 3 1 1 2 1 1.09 1.01 1.12 -0.62
7 3 1 2 1 3 2 1 0.98 0.94 0.93 0.45
8 3 2 3 2 1 3 1 0.91 1.09 0.96 0.09
9 3 3 1 3 2 1 1 1.67 1.62 1.76 -4.53
10 1 1 3 3 2 2 2 1.96 1.86 1.50 -5.02
11 1 2 1 1 3 3 2 1.08 1.10 1.02 -0.56
12 1 3 2 2 1 1 2 1.10 1.19 1.09 -1.05
13 2 1 2 3 1 3 2 0.62 0.54 0.74 3.88
14 2 2 3 1 2 1 2 1.80 1.12 1.80 -4.11
15 2 3 1 2 3 2 2 1.92 1.39 1.32 -3.90
16 3 1 3 2 3 1 2 0.39 0.32 0.47 8.00
17 3 2 1 3 1 2 2 0.50 0.55 0.53 5.57
18 3 3 2 1 2 3 2 1.25 1.76 1.25 -3.17
The experimental trail run 3 and 16 in Table 2 gives the minimum and maximum
surface roughness respectively. Table 2 shows the values of the surface roughness Ra
which falls within the range of 0.32 – 1.98 µm.
Table 3: Mean S/N ratio for each level of the cutting parameters
Level A B C D E F G
1 -10.71 8.31 -2.42 -2.28 13.61 -0.80 -18.27
2 -14.34 -8.30 -9.18 -6.03 -26.00 -7.96 -0.36
3 6.42 -18.63 -7.03 -10.31 -6.23 -9.86 -
Sum of SN ratio -18.63 -18.63 -18.63 -18.63 -18.63 -18.63 -18.63
Sum of squares of
SN ratio 361.54 485.07 139.52 147.99 900.09 161.33 333.77
Rank 3 2 7 6 1 5 4
4196 Dr.M.Nataraj
Figure.3 S/N Ratio graph for surface roughness
Results and Discussion
Analysis of variance
Table 4 gives ANOVA information for surface roughness obtained for all seven
control factors with the confidence level of 95%. The variance Fratio is evaluated from
the variance of each control factor (V) and the variance of error factor (e). The
expected value of the sum of square (SSe) and the total square sum (SSt) were used to
find the percent contribution (P %) of various control factors and error factors (Pe).
The contribution of the error factor (Pe) falls below 15% implies that the quality
characteristics of the experiment are under a precise control [15]. In contrast if the
percent contribution of the error factor exceeds 50%, it means that the certain
significant factor is overlooked and the experiments must be reviewed again [16].
Table 4: The ANOVA table for surface roughness
Sl
No
Source of
variation
Sum of
squares DoF
Mean
square Fo
Pure
var
%
Cont.
1 Cutting speed 40.98 2 20.49 2.049 20.98 10.59#
2 Feed rate 61.57 2 30.78 3.078 41.57 20.99#
3 Depth of cut 3.97 2 1.99 0.199
4 Nose radius 5.39 2 2.69 0.269
5 LN2 pressure 130.74 2 65.37 6.537 110.74 55.91#
6 Cutting time 7.61 2 3.80 0.380
7 Head of LN2 17.81 1 17.81 1.781 7.81 3.94
8 Error 40.00 4 10.00
9 e-pooled 16.97 10 1.70 16.97 8.57
TOTAL 308.05 17 198.06 100
# - Significant
Me
an
of
SN
ra
tio
s 21
3
0
-3
321 321
321
3
0
-3
321 321
321
3
0
-3
1-Dry / 2-LN2 Cut Speed Feed
Depth of Cut Nose radius LN2 pressure
Cutting time
Main Effects Plot (data means) for SN ratios
Signal-to-noise: Smaller is better
Study on Surface Integrity of High Speed Turning of Inconel 718 Using et.al. 4197
Statistical analysis
Variance analysis of the surface roughness (Ra) was made with the objective of
analyzing the influence of cutting speed, feed rate, depth of cut, tool nose radius,
Air/LN2 pressure, cutting time and head of LN2 on the results. This analysis was
arrived for a 5% significance level (95% confidence level). The last column of table
shows the factor contribution (%) on the total variation, indicating the degree of
influence. It is obvious from Table 4, the effect of feed rate (f) and Air/LN2 pressure
(p) seems to be the most significant factor allied with surface roughness having
55.91% and 20.99% contribution respectively. It is understood from the experimental
investigation that the increase in Air / LN2 pressure provide more coolant over the
machining zone particularly at the shearing zone leads to chip brittleness and
formation of discontinuous chips with tiny pieces. Similarly increase in feed rate
generates helicoids furrow ensuing helicoids movement tool–workpiece [17, 18]. For
this reason weak feed rate have to be employed with low pressurized Air/LN2 mixture
during turning operation. On other part, the depth of cut (ap), nose radius (r), cutting
time (t) and Head level of LN2 in cryocan do not show significant contribution on the
surface roughness evolution.
Mathematical Modeling of Surface Roughness The mathematical model (Eqn 4) which establish the relationship between the
machining variables and the surface roughness was obtained using Design Expert 9.0
software
Ra = 0.874-9.378x10-3
H -5.843x10-3
ν+4.156f
+0.102ap+0.335r+0.447p+0.018t (4)
In the above equation it is understood that the variables H, ν, f, ap, r, p and t will
not be equal to zero at any time during the cutting operation (H, ν, f, ap, r, p, t ≥ 0).
The predicted values are compared with the corresponding experimental values
depicted in Figure 4. The regression coefficient for the model is 91.89% which
indicates the effectiveness of experimental plan. Using the model, one can ascertain
the Ra value for a specific combination of the machining variable under consideration
for the high speed turning of Inconel 718.
Figure 4: Comparison of experimental and predicted values of surface roughness
0.00
1.00
2.00
3.00
1 3 5 7 9 11 13 15 17
Ra
valu
e
Experimental run orderExperimental Predicted
4198 Dr.M.Nataraj
Confirmation test
The selected optimal level of the design parameters have been validated through
confirmation tests. The estimated S/N ratio of the optimal level of the design
parameters Ypredicted is calculated using Equation (5)
(5)
where Ym is total mean S/N ratio, Yi is mean S/N ratio at the optimal level, and k is
number of main design parameters that affect the quality characteristics [19, 20]. The
predicted SN ratio from the confirmation tests was = 8.7626 dB which
shows that the optimal parametric combination obtained from the experimental study
close to the confirmation test.
Conclusion In this study the machining was done on Inconel 718 using TiALN carbide coated tool
with pressurized Air/LN2 as coolant by applying Taguchi‟s quality design concept
approach. Machining parameters were studied using Taguchi‟s OA based DoE
concept for investigating the influence of cutting variables on the machined surface
roughness. Comparison of experimental and predicted values of the surface roughness
shows a good agreement between them. This study shows that cut speed (ν), feed rate
(f), Air / LN2 pressure (p) have significant influences on the surface roughness while
the depth of cut (ap), tool nose radius (r), cutting time (t) and head level of LN2 (H) in
cryocan have insignificant contribution on the surface roughness evolution. During
the experiment, it is observed that increase in Air/LN2 mixture pressure results more
coolant over the shearing zone which leads to brittleness to the chip and formed
discontinuous chips with tiny pieces resulted in poor surface finish.
Acknowledgements The experimental work was carried in the Department of Mechanical Engineering,
Anna University, Chennai and authors would thank for their help and support during
the execution of this research work.
References
[1] Ezugwu, E. O., 2005, “Key improvements in the machining of difficult-to-
cut aerospace superalloys,” International Journal of Machine Tools and
Manufacture, 45(12/13), pp. 1353–1367.
[2] Devillez, A., Schneider, F., Dominiak, S., Dudzinski, D., and Larrouquere,
D., 2007, “Cutting forces and wear in dry machining of Inconel 718 with
coated carbide tools,” Wear, 262, pp. 931–942.
[3] Axinte, D. A., and Dewes, R. C., 2000, “Surface integrity of hot work tool
steel after high speed milling experimental data and empirical models,”
Journal of Materials Processing Technology, 127(3), pp. 325–335.
Study on Surface Integrity of High Speed Turning of Inconel 718 Using et.al. 4199
[4] Durul Ulutan, and Tugrul Ozel, 2011, “Machining induced surface
integrity in titanium and nickel alloys: A review,” International Journal of
Machine Tools and Manufacture, 51(3), pp. 250–280.
[5] Arunachalam, R. M., Mannan, M. A., and Spowage, A. C., 2004, “Surface
integrity when machining age hardened Inconel 718 with coated cutting
tools,” International Journal of Machine tools and Manufacture, 44(4), pp.
1481–1491.
[6] Dhar, N. R., Paul, S., and Chattopadhyay, A. B., 2001, “The influence of
cryogenic cooling on tool wear, dimensional accuracy and surface finish in
turning AISI 1040 and E4340C steels,” Wear, 249(10-11), pp. 932–942.
[7] Devillez, A., Le Coz, G. Dominiak, S., and Dudzinski, D., 2011, “Dry
machining of Inconel 718, workpiece surface integrity,” Journal of
Materials Processing Technology, 211(10), pp.1590– 1598.
[8] Wang, Z., Meng, H., and Fu, J., 2010, “Novel method for evaluating
surface roughness by grey dynamic filtering,” Measurement, 43(1), pp. 78-
82.
[9] Dillip jerold, B., and Pradeep Kumar, M., 2011, "Experimental
investigation of Turning AISI 1045 steel using Cryogenic carbon di oxide
as the cutting fluid", Journal of Manufacturing Process, 13(2), pp. 113-
119.
[10] Lin, Z. C., and Chang, D. Y, 2003, “Tool wear investigation on the
precision progressive die for the IC dambar cutting process,” Int J Adv
Manuf Technol., 22, pp. 344–356.
[11] Hamdi Aouici, Mohamed Athmane Yallese, Kamel Chaoui, Tarek
Mabrouki, and Jean-François Rigal, 2012 “Analysis of surface roughness
and cutting force components in hard turning with CBN tool: Prediction
model and cutting conditions optimization,” Measurement, 45(3), pp. 344–
353.
[12] Ravi, S., and Pradeep Kumar, M., 2011, "Effect of liquid Nitrogen on
cryogenic machining of AISI D2 Hardened Steel", Advanved Materials
Research, 335-336, pp. 400-405.
[13] Taguchi, G., 1990, A primer on Taguchi method, New York: Van
Noshtrand Reinhold Int. Co. Ltd.
[14] Taguchi, G. 1986, Introduction to Quality Engineering. New York: Asian
Productivity Organization.
[15] Montgomery, D. C., 1997, Design and analysis of experiments (4th ed.).
New York: Wi1ey.
[16] Nataraj, M., Arunachalam, V. P., and Ranganathan, G., 2006, “Using risk
analysis and Taguchi‟s method to find optimal conditions of design
parameters: a case study,” Int J Adv Manuf Technol., 27, pp. 445–454.
[17] Dhananchezian, M., and Pradeep Kumar, M., 2011, “Influence of
cryogenic cooling with modified cutting tool insert in the turning of
Aluminium 6061-T6 Alloy," Journal of Mechanical Engineering, 1(3).
4200 Dr.M.Nataraj
[18] Dhananchezian, M., and Pradeep Kumar, .M., 2011, "Influence of
cryogenic cooling in turning of AISI 1045 steel with modified Cutting
Tool Inserts," Int. J. of Applied Engg. Research, 6(14), pp. 1721-1731.
[19] Nataraj, M, Arunachalam, V. P., and Suresh, K. G. 2006, “Optimizing
planer cam mechanism in printing machine for quality improvement using
Taguchi method: risk analysis with concurrent engineering approach,”. Int.
J. Computer Applications in Technology, 26(3), pp.164-173.
[20] Palanikumar, K., 2008 “Application of Taguchi and response surface
methodologies for surface roughness in machining glass fiber reinforced
plastics by PCD tooling,” Int J Adv Manuf Technol., 36, pp. 19–27.