Turning parameters optimization for surface roughness by taguchi method

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME
203
TURNING PARAMETERS OPTIMIZATION FOR SURFACE
ROUGHNESS BY TAGUCHI METHOD
Ajeet Kumar rai*, Richa Dubey, Shalini yadav and Vivek Sachan
Mechanical Engineering Department
Sam Higginbottom Institute of Agriculture, Technology and Sciences, Allahabad-211004, India
ABSTRACT
In the present study an attempt has been made to investigate the effect of cutting
parameters (cutting speed, feed rate, and depth of cut) on surface roughness in a turning
operation of cast iron. Experiments have been conducted using Taguchi’s experimental
design technique. An orthogonal array, the signal to noise ratio, and the analysis of variance
are employed to investigate the cutting characteristics of cast iron using carbide tool.
Optimum cutting parameters for minimizing surface roughness were determined.
Experimental results reveal that among the cutting parameters, the cutting speed is most
significant machining parameter for surface roughness followed by feed rate and depth of cut
in the specified test range.
Keywords:, Optimization, Taguchi method, S/N ratio. Turning operation
INTRODUCTION
In recent years the challenge before the manufacturers is to increase the production
rate, decreasing operation cost, and enhancing the quality of production. Among the several
factors machining parameters will affect them most [1]. Among these machining parameters,
cutting speed, feed rate and depth of cut will play a significant role in machining quality that
are controlled by the user. Therefore, suitable selection of these parameters is necessary to
reach optimal machining conditions to enhance production efficiency. Several researchers
have performed experimental investigations about the machining operations and evaluated
the effect of machining parameters on the output of the process. [2, 3]. But implementing
numerous experimental tests for finding optimal conditions of the process is time consuming
and costly. In order to find a solution to this problem many researchers have attempted to
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 4, Issue 3, May - June (2013), pp. 203-211
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204
model the machining processes by various methods such as statistic, intelligent and analytical
methods.[4,5]. Yang et al. [6] have used Taguchi method to optimize the turning operation of
S45C steel bars using tungsten carbide cutting tools and reported that cutting speed, feed rate,
and depth of cut are the significant cutting parameters which affect surface roughness.
However, the contribution order of the cutting parameters for surface roughness is feed rate,
then depth of cut, and then cutting speed. Zhang et al. [7] have used Taguchi method for
surface finish optimization in end milling of Aluminum blocks. The experimental results
indicate that the effects of spindle speed and feed rate on surface finish were larger than depth
of cut for milling operation. Nalbant et al. [8] used Taguchi method to find optimum cutting
parameters for surface roughness in turning of AISI 1030 carbon steel bars using TiN coated
tools. Three cutting parameters namely, insert radius, feed rate, and depth of cut are
optimized with considerations of surface roughness. In turning, use of greater insert radius,
low feed rate and low depth of cut are recommended to obtain better surface roughness for
the specific test range. Ghani et al. [9] applied Taguchi method to find optimum cutting
parameters for surface roughness and cutting force in end milling when machining hardened
steel AISI H13 with TiN coated P10 carbide insert tool under semi-finishing and finishing
conditions of high speed cutting. The milling parameters evaluated is cutting speed, feed rate,
and depth of cut. In end milling, use of high cutting speed, low feed rate and low depth of cut
are recommended to obtain better surface roughness and low cutting force.
From the above stated literature review, it becomes clear that the Taguchi Design method has
been widely applied with great success for optimizing industrial/production processes.
TAGUCHI METHOD
The Taguchi approach is a form of DOE with special application principles. For most
experiments carried out in the industry, the difference between the DOE and Taguchi
approach is in the method of application [10].
Fig 1: Scheme of the major steps of Taguchi method [11]
Identify the
factors/
interactions
Identify the
levels of each
factor
Select an
appropriate
orthogonal array
(OA)
Assign the
factors/interactio
ns to the column
of the OA
Conduct the
experiments
Analyse the
data, Determine
the optimum
levels
Conduct the
confirmation
experiment

205
Taguchi method is a technique for designing and performing experiments to
investigate processes where the output depends on many factors (variables, inputs)
without having tediously and uneconomically run of the process using all possible
combinations of values. Thanks to systematically chosen certain combinations of
variables it is possible to separate their individual effects [12]. The tool used in the
Taguchi method is the orthogonal array (OA). OA is the matrix of numbers arranged in
columns and rows [13]. The Taguchi method employs a generic signal- to –noise (S/N)
ratio to quantify the present variation. These S/N ratios are meant to be used as measures
of the effect of noise factors on performance characteristics. S/N ratios take into account
both amount of variability in the response data and closeness of the average response to
target. There are several S/N ratios available depending on type of characteristics: smaller
is better, nominal is better and larger is better.[6,12]
EXPERIMENTAL DESIGN
Taguchi method based design of experiment has been used to study the effect of
three machining process parameters on the output parameter of surface roughness. Table
1 shows three factors and three levels used in the experiment. For selecting appropriate
arrays, degree of freedom of array is calculated. There are six degrees of freedom owing
to three machining parameters, so Taguchi based L27 orthogonal array is selected (Table
2). Accordingly 27 experiments were carried out to study the effect of machining input
parameters. Each experiment was repeated three times in order to reduce experimental
errors.
Table 1: Level of process parameters
Symbol Factors Level 1 Level 2 Level 3
A Cutting Speed (rpm) 780 1560 2340
B Feed (mm/rev) 0.4 0.8 0.16
C Depth of cut (mm) 0.4 0.5 0.6

206
Table 2:Taguchi’s L27 orthogonal array
Factor
Standard order A B C
1 780 0.4 0.4
2 780 0.4 0.5
3 780 0.4 0.6
4 780 0.8 0.4
5 780 0.8 0.5
6 780 0.8 0.6
7 780 0.16 0.4
8 780 0.16 0.5
9 780 0.16 0.6
10 1560 0.4 0.4
11 1560 0.4 0.5
12 1560 0.4 0.6
13 1560 0.8 0.4
14 1560 0.8 0.5
15 1560 0.8 0.6
16 1560 0.16 0.4
17 1560 0.16 0.5
18 1560 0.16 0.6
19 2340 0.4 0.4
20 2340 0.4 0.5
21 2340 0.4 0.6
22 2340 0.8 0.4
23 2340 0.8 0.5
24 2340 0.8 0.6
25 2340 0.16 0.4
26 2340 0.16 0.5
27 2340 0.16 0.6

207
RESULTS AND DISCUSSION
Twenty-seven experiments were performed using the design parameter combinations
in the specified orthogonal array table. Nine specimens were fabricated for each of the
parameter combinations. The complete response table for these data appears in Table 3. In
order to estimate the effect of factor A (Cutting Speed) on average value of response
variables, were summed together nine observed response at level 1 of factor A. Then the sum
was divided by nine to obtain the average response. Average responses at level 2 and level 3
were obtained in the similar manner. The estimated effects are presented graphically in fig. 2.
The range of average responses over the three levels of each experimental factor is:
For Cutting speed = 26.314
For Feed rate = 10.63889
For Depth of cut = 5.8888
In particular, factor A, B and C should be set at level 1, level 3 respectively with negligible
effect of depth of cut parameter.
Figure 2: Estimated factor effects
The sample standard deviation is generally accepted measure of variability in
statistical data analysis and experimental design. This statistics is somewhat more difficult to
calculate than the sample range, but it has desirable properties which make its use worth the
added effort [6, 12].
The standard deviation was calculated for each tube in five steps. First, y was
subtracted from each measurement in the sample (sample mean), then the square differences
obtained prior were calculated. Next, the squared obtained differences were and was divided
the sum by the sample size minus one (s2
). Finally obtain the square root of s2
. The sample
variance is written as
s2
= ∑(y-y)2
/(n-1) (1)
s = √s2
(2)
0
5
10
15
20
25
30
35
40
45
A1 A2 A3 B1 B2 B3 C1 C2 C3
Average
Level of Factor

208
Table 3: Experimental data sample statistics
Experiment
number
Observed response values for
Surface roughness (µm)
Mean Standard
Deviation
Logarithm
Of S.D.
S/N
Ratio
1. 6.25 13.75 28.75 36.25 21.25 13.6930 1.1364 -26.547
2. 17.5 7.5 7.5 32.5 16.25 11.8145 1.0724 -24.217
3. 30 15 10 35 22.5 11.9023 1.07563 -27.043
4. 15 0 0 15 7.5 8.660 0.9375 -17.501
5. 30 0 5 20 13.75 13.768 1.1389 -22.766
6. 22.5 2.5 7.5 12.5 11.25 8.5391 0.93141 -21.023
7. 30 5 10 0 11.25 13.1497 1.1189 -21.023
8. 10 10 0 5 6.25 4.7871 0.68007 -15.917
9. 30 0 15 5 12.5 13.2287 1.1215 -21.938
10. 20 15 15 5 13.75 6.2915 0.7987 -22.766
11. 58.75 6.25 26.25 26.25 29.375 21.7346 1.3371 -29.359
12. 7.5 5 7.5 17.5 9.375 5.5433 0.7437 -19.439
13. 3.75 3.75 3.75 11.25 5.625 3.75 0.5740 -15.002
14. 36.25 8.75 23.75 3.75 18.125 14.7725 1.1694 -25.165
15. 38.75 13.75 3.75 56.25 28.125 23.8375 1.3772 -28.981
16. 2.5 27.5 2.5 29.5 15.5 15.0332 1.1770 -23.806
17. 20 25 15 20 20 4.0824 0.6109 -26.020
18. 35 15 15 0 16.25 14.3614 1.1571 -24.217
19. 110 35 110 180 108.75 59.2135 1.7724 -40.728
20. 5 20 5 30 15 12.2474 1.0880 -23.521
21. 38.75 28.75 11.25 56.25 33.75 18.8193 1.2746 -30.565
22. 148.75 51.25 21.25 76.25 49.375 61.6230 1.7897 -33.870
23. 47.5 22.5 17.5 52.5 35 17.5544 1.2445 -30.881
24. 20 30 5 45 25 16.8325 1.2261 -27.958
25. 18.75 3.75 1.25 21.25 11.25 10.2062 1.0088 -21.023
26. 61.25 36.25 8.75 88.75 48.75 34.2174 1.5342 -33.759
27. 62.5 2.5 37.5 27.5 32.5 24.83277 1.3950 -30.237
The estimated log s effects from Table 3 are plotted in Fig.3.In order to minimize the
variability the following optimum results were obtained.
Factor A, Cutting Speed at level 2
Factor B, feed rate at level 3
Factor C, Depth of cut at level 2

209
Figure 3: Estimated factor effects on log(s)
In this work, the minimum surface roughness is the indication of better performance.
Therefore, the smaller-is-better for the surface roughness was selected for obtaining optimum
result. The following S/N ratios for the lower-is-better case could be calculated:
S/NLB = −10 Log (
ଵ
௥
∑ y௥
௜ୀଵ i
2
)
Figure 4: Plot of factor effects on S/N Ratio
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
A1 A2 A3 B1 B2 B3 C1 C2 C3
Log(s)
Level of Factor
-35
-30
-25
-20
-15
-10
-5
0
A1 A2 A3 B1 B2 B3 C1 C2 C3
S/NRatio
Level of Factor

210
Table 4:Overall mean S/N Ratio
Level Average S/N Ratio by factor level Overall mean
S/N RatioA B C
1 -21.9974 -27.1320 -24.696 -25.3807
2 -23.8620 -24.7944 -25.7342
3 -30.2828 -24.21587 -25.7116
In order to maximize the S/N ratio the following assignments were done: factor A
(Cutting speed) – level 1, factor B (Feed rate) – level 3, factor C (Depth of cut) – level 1.
Figure 4 shows that factor A have a strong effect on S/N ratio response. Factor B is the next
most significant. The above analyses of table 3 and table 4 are summarized in table 5. In that
table the levels of key factors which are optimizing the response are listed. Some significant
levels are shown in fig. 2, 3 and 4. Keep in mind that the objective is to minimize the
response average, minimize log s, and maximize the S/N Ratio.
Table 5. Summary of analyses of factor effects
Level which was optimized
Factor y Log s S/N Ratio
A 1 2 1
B 3 3 3
C Not very significant 2 1
In this study factor A and B were dominant. For parameter C, reducing log s will have little
effect on the performance than the S/N ratio. So level 1 is optimized.The final optimized
values are:-
1) Cutting speed: - Level 1 – 780 rpm.
2) Feed rate: - Level 3 - 0.16 mm/rev.
3) Depth of cut: - Level 1 - 0.4 mm.
CONCLUSIONS
In this study, the Taguchi optimization method was applied to find the optimal
process parameters, which minimizes the surface roughness during the dry turning of cast
iron. A Taguchi orthogonal array, the signal to noise(S/N) ratio and the analysis of variance
(ANOVA), were used for the optimization of cutting parameters. ANOVA results shows that
cutting speed, feed rate and depth of cut affects the surface roughness by 38.45%, 4.85% and
0.72% respectively. In this experiment depth of cut do not have a significant effect on the
surface roughness in the specified test range, however 0.4 mm will be the optimum value.
Confirmation experimentation was also conducted and verified the effectiveness of the
Taguchi optimization method.

211
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Issue 3, 2013, pp. 378 - 386, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

Turning parameters optimization for surface roughness by taguchi method

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