International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
Application of Taguchi Technique for Identifying Optimum
Surface Roughness in CNC end Milling Process
BalaRaju.J1, Anup Kumar.J2, Dayal Saran.P3, Dr.C.S.Krishna Prasad Rao4
12
Assistant Professor, 3Associate Professor, 4Dean & Professor,
Department of Mechanical Engineering,
Bharat Institute of Engineering &Technology, Hyderabad, Telangana, India
ABSTRACT: In order to build up a bridge between
quality and productivity, the present study highlights
optimization of CNC End milling process parameters to
provide good surface finish. The Surface Finish has
been identified as one of the quality attribute and
directly related to productivity. An attempt will be made
to optimize aforesaid quality attribute in a manner that
could be fulfilled simultaneously up to expected level.
The aim of this work is to apply Taguchi optimization
method for low surface roughness values in terms of
CNC End milling of Aluminium and Mild Steel. The
milling parameters evaluated is cutting speed, feed rate
and depth of cut. A series of milling experiments are
performed to measure the surface roughness data. The
settings of end milling parameters are determined by
using
Taguchi
experimental
design method.
Orthogonal arrays of Taguchi, the signal-to noise ratio
(S/N) ratio, the analysis of variance (ANOVA) are
employed to find the optimal levels and to analyze the
milling parameters on surface roughness. Finally
confirmation tests with the optimal levels of cutting
parameters are carried out in order to illustrate the
effectiveness of Taguchi optimization method.
Key words: CNC End Milling, Surface Finish, Taguchi
Method.
1. INTRODUCTION
Surface finish produced on machined surface
plays an important role in production. The surface
roughness has a vital influence on most important
functional properties such as wear resistance,
fatigue strength, corrosion resistance and power
losses due to friction. Poor surface roughness will
lead to the rupture of oil films on the peaks of
micro irregularities, which lead to a state
approaching dry friction and results in decisive
wear of rubbing surface. Therefore finishing
processes are employed in machining in order to
obtain a very high surface finish. Surface roughness
in End Milling depends on spindle rpm, feed, depth
of cut, helix angle, lubricating oil etc… Among
them mainly surface finish depends on spindle rpm,
feed, depth of cut.
In order to infer the science behind the
observed phenomenon, one has to plan and conduct
the experiments to obtain enough and relevant data.
This can be done by any one of the method as
mentioned below:
ISSN: 2231-5381
1.1 Trial and Error method:
In this method we will perform series of
experiments, each of which gives some
understanding. This requires making measurements
after completion of every experiment to analyze the
observed data. Many a times such series does not
progress much as negative results may discourage
or will not allow a selection of parameters which
ought to be changed in the next experiment.
Therefore, such experimentation usually ends well
before the number of experiments reaches a double
digit. The data is insufficient to draw any
significant conclusions will still remain unsolved.
1.2 Design of Experiments :
A well planned set of experiments, in
which all parameters of interest are varied over a
specified range, is a much better approach to obtain
systematic data. However, it does not easily lend
itself to understanding of science behind the
phenomenon. The analysis is not very easy and
thus effects of various parameters on the observed
data are not readily apparent. In many cases,
particularly those in which some optimization is
required, the method does not point to the BEST
settings of parameters. A classic example
illustrating the drawback of design of experiments
is found in the planning of a world cup event, say
football. While all matches are well arranged with
respect to the different teams and different venues
on different dates and yet the planning does not
care about the result of any match (win or lose).
Obviously, such a strategy is not desirable for
conducting scientific experiments.
1.3 Taguchi Method:
Dr. Taguchi of Nippon Telephones and
Telegraph Company, Japan has developed a
method based on" Orthogonal Array” experiments
which gives much reduced " variance " for the
experiment with " optimum settings " of control
parameters. Thus the combination of Design of
Experiments with optimization of control
parameters to obtain best results is achieved in the
Taguchi Method. "Orthogonal Arrays" (OA)
http://www.ijettjournal.org
Page 103
International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
provide a set of well balanced (minimum)
experiments and Dr. Taguchi's Signal-to-Noise
ratios (S/N), which are log functions of desired
output, serve as objective functions for
optimization, help in data analysis and prediction of
optimum results.
By changing one parameter and other
keeping constant, extreme and mean limits
are found.
The upper, middle, lower limits are coded
as 3, 2, 1 respectively.
2.3 Design of Experiment:
John L. Yang et al., [1] conducted the
experiments for Identifying Optimum Surface
Roughness
Performance
in
End-Milling
Operations,in order to identify the optimum surface
roughness performance with a particular
combination of cutting parameters in an end
milling operation.Eyup Bagci, Seref Aykut et al.,
[2] conducted the experiments for Identifying
optimum Surface Roughness in CNC Face Milling
of cobalt-based alloy.KrishanKant, JatinTaneja,
MohitBector, Rajesh Kumar et al.,[3] carried out
experiments on optimizing Turning Process by the
effects of Machining Parameters.AdemCicek TurgayKıvak- GurcanSamtaş et al., [4] conducted
experiments for Identifying Surface Roughness and
Roundness Error in Drilling of AISI 316 Stainless
Steel.Two cutting tools, cutting speeds and feed
rates were considered as control factors, and L8(23)
orthogonal array was determined for experimental
trials.Rama Rao. S, Padmanabhan. G et al., [5]
conducted experiments for optimization of process
parameters by using Taguchi’s experimental design
method. Orthogonal arrays of Taguchi, the signalto-noise (S/N) ratio, the analysis of variance
(ANOVA), and regression analyses are employed
to find the optimal process parameter levels and to
analyze the effect of these parameters on metal
removalratevalues.Mehmet,Pinarbasi&CagriSel&H
aci Mehmet Alagas& Mustafa Yuzukirmizi et al.,
[6] conducted experiments on Integrated definition
modeling and Taguchi analysis of flexible
manufacturing systems.
2. MATHEMATICAL
MODELLING
2.1 Identification
variables
of
process
control
Identification of control factors is very
important to get a good and accurate model. The
parameters that influence the surface finish are
spindle speed, depth of cut, feed, helix angle,
lubricating oils etc., among them the following are
major influencing parameters:
Speed(A)
Feed(B)
Depth of cut(C)
2.2 Finding the limits of the process
variables:
ISSN: 2231-5381
Design of experiment is an effective tool to
design and conduct the experiments with minimum
resources. Orthogonal Array is a statistical method
of defining parameters that converts test areas into
factors and levels. Test design using orthogonal
array creates an efficient and concise test suite with
fewer test cases without compromising test
coverage.
If there is an experiment having three
factors which have three values, then total
number of experiments is 27. Then results of all
experiments will give 100% accurate results. In
comparison to above method the Taguchi
orthogonal array make list of all 9 experiments in
a particular order which cover all factors. Those
9 experiments will give 99.96% accurate result.
By using this method number of experiments
reduced to 9 instead of 27 with almost same
accuracy. Hence L9 Orthogonal Array design
matrix is used to set the control parameters to
evaluate the process performance.
2.4 Conducting the Experiments as per the
Design Matrix:
The orthogonal array L9 has 9 rows
corresponding to the number of tests with three
columns at three levels. The factors and the
interactions are assigned to the columns. The
outputs studied are Surface Roughness (Ra). For
the purpose of observing the effect influence
degree of cutting conditions (feed rate, depth of cut
and cutting speed) in end milling, three factors,
each at three levels, are taken into account.
2.4 Taguchi Experimental Design
approach:
The Taguchi method uses a loss function to
determine the quality characteristics. Loss function
values are also converted to a signal-to-noise (S/N)
ratio (η). In general, there are three different quality
characteristics (Eqs. (1) to (3)) in S/N ratio analysis,
namely “Nominal is the best”, “Larger is the better”
and “Smaller is the better”. For each level of
process parameters, signal-to-noise ratio is
calculated based on S/N analysis.
Nominal is best;
http://www.ijettjournal.org
Page 104
International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
interactions by comparing the mean square against
an estimate of the experimental errors at specific
confidence levels. This is to be accomplished by
separating the total variability of S/N ratios, which
is measured by the sum of the squared deviations
from the total mean S/N ratio, into contributions by
each of the design parameters and the error. F
(1)
Larger is better;
(2)
) can be calculated as:
Smaller is better;
(3)
is the mean of observed data, s²is
the variance of y, n is the number of observations
and yi is the observed data.
Where n is the number of experiments in the
orthogonal array and yi is the mean S/N ratio for
the experiment. The percentage contribution P can
be calculated as below:
P= (SSA / SST)
2.5 Signal-to-noise ratio(S/N) Ratio:
The Taguchi method uses S/N ratio to measure
the variations of the experimental design. The
equation of “Smaller is better” was selected for the
calculation of S/N ratio since the lowest values of
surface roughness were the desired results in terms
of good product quality. The effects of the level of
each factor on the quality characteristics can be
analyzed using S/N ratios. These effects are defined
and evaluated according to total mean values of
experimental trial results or S/N ratios. The
optimum surface roughness values can be
calculated by means of total mean values of
experimental trial results. Another requirement in
the calculation of optimum values is to determine
the optimum levels. The optimum levels can be
determined by evaluating three different levels of
the control factors according to the results from the
Statistically, there is a tool called an F test
named after Fisher to see which design parameters
have a significant effect on quality characteristic. In
the analysis, F ratio is a ratio of mean square error
to residual, and is traditionally used to determine
the significance of a factor.
Where,
)²
Mean Square;
(MS) = Sum of Square(SS) / Degrees of Freedom
(DOF)
F-ratio;
F= (MS/MSE)
3. EXPERIMENTAL DESIGN
AND PROCEDURE
3.1 Taguchi Parameter Design:
Limits
Speed
rpm
Feed
mm/min
Depth of cut
(mm)
Upper(3)
1400
100
1
Middle(2)
1200
75
0.75
Lower(1)
1000
50
0.5
combinations generated by the orthogonal array.
There are 18 basic types of standard
Orthogonal Arrays in the Taguchi parameter
design. Since three factors were studied in this
research,three levels of each factor were
considered. Therefore, an L9 Orthogonal Array was
selected. The taguchi design parameters and levels
are listed in table 3.1.The layout of this L9
orthogonal array is shown in table 3.2.
Table 3.1 Working limits of milling parameters
2.6 Analysis of variance (ANOVA)
This method was first developed by Sir
Ronald Fisher in the 1930s as a way to interpret the
results from agricultural experiments. ANOVA is a
statistically based, objective decision-making tool
for detecting any differences in average
performance of groups of items tested.
ANOVA helps in formally testing the
significance of all main factors and their
ISSN: 2231-5381
Exp.
No
1
2
3
4
5
6
Speed(A)
1
1
1
2
2
2
http://www.ijettjournal.org
Parameters
Feed(B) Depth of cut (C)
1
3
2
2
3
1
1
2
2
1
3
3
Page 105
International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
7
8
9
3
3
3
1
2
3
1
3
2
8
9
1400
1400
75
100
1
0.75
0.46
0.48
6.74
6.37
Table 4.1 Results of the L9 orthogonal array for Aluminium
Table 3.2 L9 Orthogonal Array
3.2 Experimental Procedure:
Experimental work is carried on CNC
Vertical Milling Machine shown in Figure 3.1.
Aluminium and Mild Steel are chosen as work
piece materials and High Speed Steel Single Point
Cutting Tool is chosen as cutting tool material.
Machining has been done as per the Design Matrix.
In this current paper Speed, feed and Depth of Cut
are chosen as the influencing parameters of
Surface Roughness and their mean and extreme
values are decided after conducting trail
experiments.
Job
Cutting Parameter level
Surface
Rough
ness
Speed
Feed
(mm/min)
Ra
[µm]
[dB]
(rpm)
1000
1000
1000
1200
1200
1200
1400
1400
1400
50
75
100
50
75
100
50
75
100
1.49
1.59
1.86
1.48
1.53
2.2
1.26
1.47
1.56
-3.46
-4.02
-5.39
-3.40
-3.69
-6.84
-2.00
-3.34
-3.8
Depth
of cut
S/N
ratio
(mm)
1
2
3
4
5
6
7
8
9
1
0.75
0.5
0.75
0.5
1
0.5
1
0.75
Table 4.2 Results of the L9 orthogonal array for Mild Steel
The signal-to-noise ratio of each experimental run
is calculated based on the following equation
.
Where n = number of measurements in a trial/row,
in this case, n = 3 and yi is the ith measured value
in a run/row.
For example:
S/N Ratio for Job 1
= -10 log (y1²)
= -10 log (0.49²)
= 6.196 db
Figure 3.1 CNC Vertical milling machine
4. RESULTS AND DISCUSSION
4.1 Calculation of S/N Ratios:
After conducting the experiments based on
the design matrix, the surface roughness data for
each experiment is collected using Talysurf. The
recorded values of surface roughness and signal-tonoise ratio of each experiment for aluminium and
mild steels are shown in table 4.1 and table 4.2.
Cutting Parameter level
Job
Speed
Feed
(rpm)
(mm/min)
1000
1000
1000
1200
1200
1200
1400
50
75
100
50
75
100
50
Depth
of cut
Surface
Rough
ness
S/N
ratio
Ra
[µm]
[dB]
0.49
0.53
0.69
0.37
0.53
0.65
0.33
6.19
5.51
3.22
8.63
5.51
3.74
9.62
(mm)
1
2
3
4
5
6
7
ISSN: 2231-5381
1
0.75
0.5
0.75
0.5
1
0.5
Ra response table for the speed parameter
(A) at levels 1, 2, and 3 for aluminium and mild
steel are created by using the Ra values between 1–
3, 4–6 and 7–9 in Table 6.1 and Table 6.2
respectively. Ra response table for each level of the
process parameters are created in the integrated
manner and Ra response results are given in Table
4.3 and Table 4.4 for aluminium and mild steel
respectively. On the other hand, the same
procedure for S/N response table including process
parameters is applied and the S/N response results
are listed in Table 4.5 and Table 4.6 for aluminium
and mild steel respectively.
Levels
A(rpm)
B(mm/min)
C(mm)
1
2
3
∆max-min
Rank
0.57
0.51
0.42
0.14
2
0.39
0.50
0.60
0.21
1
0.51
0.46
0.53
0.07
3
Table 4.3 Average effect response table for surface roughness
(Ra) of Aluminium
http://www.ijettjournal.org
Page 106
International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
Levels
A(rpm)
B(mm/min)
C(mm)
1
2
3
∆max-min
Rank
1.64
1.73
1.43
0.09
3
1.41
1.53
1.87
0.34
1
1.55
1.54
1.72
0.17
2
Table 4.4 Average effect response table for surface roughness
(Ra) of Mild Steel
Levels
A(rpm)
B(mm/min)
C(mm)
1
2
3
∆max-min
Rank
4.88
5.74
7.47
2.59
2
8.04
5.91
4.35
3.69
1
5.73
6.74
5.46
1.27
3
Table 4.5 Average effect response table for S/N ratio of
Aluminium
Levels
A(rpm)
B(mm/min)
C(mm)
1
2
3
∆max-min
Rank
-4.29
-4.64
-3.07
1.22
1
-2.95
-3.68
-5.36
0.73
2
-3.80
-3.76
-4.55
0.06
3
Table 4.6 Average effect response table for S/N ratio of Mild
Steel
Similarly calculate SSB and SSC
SS error = SS total- (SSA+SSB+SSC)
SS error = 0.004;
MSA = MS error / DOF
MSA = (0.033/2)
MSA =0.0165;
Similarly calculate MSB, MSC, and MS error
F-ratio for A;
FA =MSA / MS error
= (0.0165/0.002)
FA = 8.25;
Contribution (P %) = (SSA/SStotal) x100
= (0.033/0.109) x100
= 30.2
FA = 30.2
Similarly calculate for Sum of Squares, Mean
Squares, F-ratio, and Contribution for Mild Steel.
The results of ANOVA tables for Aluminium and
Mild Steel are given in table 4.7 and table 4.8
respectively.
Source of
variation
A
B
C
Error
Total
DOF
SS
MS
FRatio
2
2
2
20
26
0.033
0.066
0.008
0.004
0.109
0.016
0.033
0.004
0.002
--
8.25
16.5
2.2
--
Contrib
ution
(P%)
30.2
60.5
80.7
--
Table 4.7 ANOVA results for surface roughness for Aluminium
Source of
variation
DOF
A
B
C
Error
Total
2
2
2
20
26
SS
MS
FRatio
4.2 Calculation of ANOVA Table:
ANOVA helps in formally testing the
significance of all main factors and their
interactions by comparing the mean square against
an estimate of the experimental errors at specific
confidence levels. First,
) for
Aluminium can be calculated as:
0.148
0.346
0.060
0.038
0.593
0.074
0.174
0.030
0.019
--
3.90
9.10
1.58
---
Contrib
ution
(P%)
25.02
58.41
10.16
---
Table 4.8 ANOVA results for surface roughness for Mild Steel
4.3 Analysis of S/N Ratio:
Where n is the number of experiments in the
orthogonal array and yi is the output parameter of
ith
is the total mean of the output
parameter of all experiments.
= 0.503;
SS (total) = 0.1099;
)²
Here, n=3; i = 0 to 3;
x1 = 0.57; x2 = 0.516; x3 = 0.423;
SS (A) = 0.033;
ISSN: 2231-5381
The objective of using the S/N ratio as a
performance measurement is to develop products
and process insensitive to noise factors. The S/N
ratio indicates the degree of the predictable
performance of a product or process in the presence
of noise factors. Regardless of the category of the
performance characteristics, a greater S/N value
corresponds to a better performance. Therefore, the
optimal level of the machining parameters is the
level with the greatest S/N value. According to the
table 4.7, the optimal machining performance for
surface roughness of Aluminium is obtained at
cutting speed 1400rpm (level3), feed rate
50mm/min (level1), and depth of cut 0.75mm
http://www.ijettjournal.org
Page 107
Ra Surface Roughness
(level2). Fig 4.1 shows the effect of the process
parameters on the surface roughness.
0.8
0.6
A(rpm)
0.4
B(mm/min
)
0.2
0
1
2
Ra Surface Roughness
International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
2
1.5
1
0.5
0
2
3
Levels
Figure 4.3 Effect of process parameters on surface roughness for
Mild Steel
0
10
8
6
4
2
0
A(rpm)
3
S/N Ratio
-1
S/N Ratio
C(mm)
1
Figure 4.1 Effect of process parameters on surface roughness for
Aluminium
2
B(mm/min)
C(mm)
3
Levels
1
A(rpm)
1
2
3
A(rpm)
-2
-3
-4
B(mm/min
)
B(mm/min
)
-5
C(mm)
C(mm)
-6
Levels
Levels
Figure 4.4 S/N response table for surface roughness of Mild
Steel
Figure 4.2 S/N response table for surface roughness of
Aluminium
Figure 4.2 shows the graph which
contains three curves representing the S/N response
table for surface roughness of Aluminium. Greater
S/N response values indicate better performance.
From the figure 4.2, we can conclude that with
increase of cutting speed surface finish will
increases. Similarly with increase in feed surface
finish decreases and with increase in depth of cut
surface finish increases and then decreases.
Similarly, according to the table 4.8, the
optimal machining performance for surface
roughness of Mild Steel is obtained at cutting speed
1400rpm (level3), feed rate 50mm/min (level1),
depth of cut 0.75mm (level2). Fig 4.3 shows the
effect of the process parameters on the surface
roughness of Mild Steel.
Figure 4.4 shows the graph which contains
three curves representing the S/N response table for
surface roughness of Mild Steel. Greater S/N
response values indicate better performance From
the figure 4.4, we can conclude that with increase
of cutting speed surface finish will decrease and
then increases. Similarly with increase in feed
surface finish decreases and with increase in depth
of cut surface finish increases and then decreases.
ISSN: 2231-5381
4.4 Analysis of Variance:
ANOVA is a statistical method used for
determining individual interactions of all control
factors. In the analysis, the percentage distributions
of each control factor were used to measure the
corresponding effects on the quality characteristics.
The performed experimental plan was evaluated at
a confidence level of 95%. ANOVA values
belonging to experimental results for the surface
roughness of Aluminium and Mild Steel are shown
in Table 4.9 and Table 4.10 respectively. The
significance of control factors in ANOVA is
determined by comparing F value of each control
factor and F0.05.
Table 4.9 shows the results of
ANOVA analysis of raw data for surface roughness
of Aluminium. It is apparent that the F values of
factor A (cutting speed), factor B (Feed rate) are
greater than F0.05, 2, 20=3.2. Factor C (depth of
cut) was not a significant cutting factor affecting
surface roughness. Its F value =2.121 is less than F
0.05, 2, 20=3.2.
Table 4.10 shows the results of ANOVA
analysis of raw data for surface roughness of Mild
Steel. It is apparent that the F values of factor A
http://www.ijettjournal.org
Page 108
International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
(cutting speed), factor B (Feed rate) are greater than
F0.05, 2, 20=3.2. Factor C (depth of cut) was not a
significant cutting factor affecting surface
roughness. Its F value =1.58 is less than F 0.05, 2,
20=3.2.
4.5 Determination of optimum Factor level
combination:
The S/N ratio indicates the degree of the
predictable performance of a product or process in
the presence of noise factors. Process parameter
settings with the highest S/N ratio always yield the
optimum quality with minimum variance.
Consequently, the level that has a higher value
determines the optimum level of each factor. For
example, in Figure 4.3, level two for depth of cut
(C2= 0.75mm) has the highest S/N ratio value,
which indicated that the machining performance at
such level produces minimum variation of the
surface roughness. In addition, the lower surface
roughness value had a better machining
performance. Furthermore, level two of depth of
cut C2= 0.75mm has indicated the optimum
situation in terms of mean value. Similarly, the
level three of cutting speed (A3=1400rpm) and the
level three of feed rate (B1=50mm/min) have also
indicated the optimum situation in terms of S/N
ratio and mean value.
Using the before mentioned data, one can predict
the optimum surface roughness performance using
the cutting parameters as:
Predicted Mean (Minimum roughness) for
Aluminiu;
= A3+ B1+C2 −
)
= 0.423+0.396+0.46 −2× (0.503)
= 0.273 μm.
Predicted Mean (Minimum roughness) for Mild
Steel;
= A3+ B1+C2 −
)
= 1.43+1.41+1.543 −2× (1.604)
= 1.175 μm.
Similarly, the maximum S/N ratio is calculated to
determine whether or not the minimum surface
roughness is acceptable. Also, the maximum S/N
ratio varies from the min =−11 dB to max=+8dB.
The S/N ratio could be predicted as:
Predicted S/N Ratio (Maximum) for
Aluminium;
= ηA3 +ηB1+ηC2−2× (η)
=7.47+8.04+6.74−2×(5.96)
= 11.27 dB
Predicted S/N Ratio (Maximum) for Mild Steel;
= ηA3 +ηB1+ηC2−2× (η)
=-3.071+-2.95+-3.76−2×(-4.10)
= -1.40 dB
ISSN: 2231-5381
where η is the average value of surface roughness
or S/N ratio.
With this prediction, one could
conclude that the machine creates the best surface
roughness (Ra = 0.273 μm) for Aluminium within
the range of specified cutting conditions (Table4.9).
The Ra value of 0.273 μm is the smallest value
involving in experimental measurements
Similarly, one could conclude that the
machine creates the best surface roughness (Ra =
1.175 μm) for Mild Steel within the range of
specified cutting conditions (Table4.10). The Ra
value of 1.175 μm is the smallest value involving in
experimental measurements
4.6 Confirmation test:
The confirmation experiment is very
important in parameter design, particularly when
screening or small fractional factorial experiments
are utilized. In this study, a confirmation
experiment was conducted by utilizing the level of
optimal process parameters (A3B1C2) in case of
Aluminium and (A3B1C2) in case of Mild Steel.
The purpose of the confirmation experiment in this
study was to validate the optimum cutting
conditions that were suggested by the experiment
that corresponded with the predicted value.
In this research, the confirmation runs
with the optimum cutting conditions of Aluminium
A3B1C2 resulted in response values of 0.29, 0.295
and 0.285 μm. Each Ra measurement was repeated
at least three times. Therefore, the optimum surface
roughness (Ra = 0.29 μm) can be obtained under
the above-mentioned cutting condition for
Aluminium in the CNC vertical milling machine.
Similarly, the confirmation runs with the
optimum cutting conditions of Mild Steel A3B1C2
resulted in response values of 1.183, 1.191, 1.196
μm. Each Ra measurement was repeated at least
three times. Therefore, the optimum surface
roughness (Ra = 1.19 μm) can be obtained under
the above-mentioned cutting condition for Mild
Steel in the CNC vertical milling machine.
Level
Optimal combination
(Experiment)
Optimal combination
(Prediction)
S/N
[dB]
A3B1C2
Surface
Roughness
Ra [μm]
0.29
A3B1C2
0.273
11.27
10.75
Table 4.9 Comparison of surface roughness of Aluminum
http://www.ijettjournal.org
Page 109
International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
Level
Optimal combination
(Experiment)
Optimal combination
(Prediction)
S/N
[dB]
A3B1C2
Surface
Roughness
Ra [μm]
1.19
A3B1C2
1.175
-1.4
-1.5
6. SCOPE OF FUTURE
Table 4.10 Comparison of surface roughness of Mild Steel
5. CONCLUSIONS
This study has discussed an application of the
Taguchi method for investigating the effects of
cutting parameters on the surface roughness value
in the End milling of Aluminium and Mild Steel
material.
In this study, the analysis of confirmation
experiments has shown that Taguchi
parameter design can successfully verify
the optimum cutting parameters
of
Aluminum (A3B1C2), which are cutting
speed = 1400 rpm (A3), feed rate = 50
mm/min (B1) and depth of cut = 0.75 mm
(C2). And for Mild Steel the optimum
cutting parameters are cutting speed =
1400 rpm (A3), feed rate = 50 mm/min
(B1) and depth of cut = 0.75 mm (C2).
The optimum surface roughness (Ra =
0.29 μm) for Aluminum and (Ra =1.19
μm) for Mild Steel can be obtained under
the above-mentioned cutting condition in
the CNC vertical milling machine.
Taguchi parameter design can provide a
systematic procedure that can effectively
and efficiently identify the optimum
surface roughness in the process control of
individual end milling machines. It also
allows industry to reduce process or
product variability and minimize product
defects by using a relatively small number
of experimental runs and costs to achieve
superior-quality products.
This research not only demonstrates how
to use Taguchi parameter design for
ISSN: 2231-5381
optimizing machining performance with
minimum cost and time to industrial
readers but also shows the Industrial
Technology educator a project exercise in
any Taguchi-related curricula.
In the present investigation the effect of
various process parameters like spindle speed, feed,
depth of cut on surface finish were studied with the
predicted values. Further study could consider
more factors (different insert geometry, materials,
lubricant, cooling strategy etc.) in the research to
see how the factors would affect surface roughness.
7. REFERENCES
[1]. Mr. John L. Yang & Dr. Joseph C. Chen
(2001) A systematic approach for Identifying
Optimum Surface Roughness Performance in EndMilling Operations.
[2]. Eyup Bagci, Seref Aykut (2005) A study of
Taguchi Optimization for Identifying optimum
Surface Roughness in CNC Face Milling of cobaltbased alloy (stellite 6).
[3]. KrishanKant, Jatin Taneja, MohitBector,
Rajesh Kumar (2012) Application of taguchi
method for optimizing Turning Process by the
effects of Machining Parameters.
[4]. Adem Cicek –Turgay Kivak-Gurcan Samtas
Application of Taguchi method for Identifying
Surface Roughness and Roundness Error in
Drilling of AISI 316 Stainless Steel.
[5]. Rama Rao. S, Padmanabhan (2012)
Application of Tagughi method and ANOVA in
optimization of process parameters for metal
removal rate in electrochemical machining of
Al/5%SiC composites.
[6]. Mehmet Pinarbasi & Cagri Sel&Haci Mehmet
Alagas & Mustafa Yuzukirmizi (2013) on
Integrated definition modeling and Taguchi
analysis of flexible manufacturing systems:
Aircraft Industry Application.
http://www.ijettjournal.org
Page 110