1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME
89
ANALYSIS OF TURNING PARAMETERS DURING TURNING OF AISI 1040
STEEL USING TAGUCHI METHOD
Hassan Karim Mohammed1
, Dr. Mohammad Tariq2
, Dr. Fouad Alwan Saleh3
1
Ministry of Transport, General Maritime Transport Company, Republic of Iraq
2
Asst. Prof., Department of Mech. Engg., SSET, SHIATS-DU, Naini, Allahabad, U.P., India
3
Asst. Professor of Mechanical Engineering Department of Almustansiriya University, Iraq
ABSTRACT
The purpose of this research work is focused on the analysis of optimum cutting conditions to
get lowest surface roughness in turning AISI 1040 alloy steel by Taguchi Method. Experiment was
designed using Taguchi method and 27 experiments were designed by this process and experiments
conducted. The results are analyzed using the average S/N ratios for smaller the better for surface
roughness factors and significant interaction method. Taguchi method has shown that the depth of
cut has significant role to play in producing lower surface roughness followed by feed. The Cutting
speed has lesser role on surface roughness from the tests. The vibrations of the machine tool, tool
chattering are the other factors which may contribute poor surface roughness to the results and such
factors ignored for analyses. The results obtained by this method will be useful to other researches
for similar type of study and may be better opportunity for further study on tool vibrations, cutting
forces etc.
Keywords: Taguchi method, Turning, AISI 1040, Surface Roughness.
1. INTRODUCTION
Surface roughness influences the performance of mechanical parts and their production costs
because it affects factors, such as friction, ease of holding lubricant, electrical and thermal
conductivity, geometric tolerances and more. The ability of a manufacturing operation to produce a
desired surface roughness depends on various parameters. The factors that influence surface
roughness are machining parameters, tool and work piece material properties and cutting conditions.
For example, in turning operation the surface roughness depends on cutting speed, feed rate, depth of
cut, tool nose radius, lubrication of the cutting tool, machine vibrations, tool wear and on the
mechanical and other properties of the material being machined. Even small changes in any of the
mentioned factors may have a significant effect on the produced surface [1]. In machinability studies
investigations, statistical design of experiments is used quite extensively. Statistical design of
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experiments refers to the process of planning the experiments so that the appropriate data can be
analysed by statistical methods, resulting in valid and objective conclusions [2]. Design methods
such as factorial designs, response surface methodology (RSM) and Taguchi methods are now
widely use in place of one factor at a time experimental approach which is time consuming and
exorbitant in cost.
1.1 Three Important Elements
In order to get an efficient process and beautiful surface at the lathe machining, it is important
to adjust a rotating speed, a cutting depth and a sending speed. Please note that the important
elements cannot decide easily, because these suitable values are quiet different by materials, size and
shapes of the part. They are Rotating Speed, Cutting Depth and Sending Speed (Feed).
1.2 Signal to Noise (S/N) Ratios
The product/process/system design phase involves deciding the best values/levels for the
control factors. The signal to noise (S/N) ratio is an ideal metric for that purpose. The equation for
average quality loss, Q, says that the customer’s average quality loss depends on the deviation of the
mean from the target and also on the variance. An important class of design optimization problem
requires minimization of the variance while keeping the mean on target.
1.3 Taguchi method, design of experiment, and experimental details
Taguchi defines the quality of a product, in terms of the loss imparted by the product to the
society from the time the product is shipped to the customer [13]. Some of these losses are due to
deviation of the product’s functional characteristic from its desired target value, and these are called
losses due to functional variation. The uncontrollable factors which cause the functional
characteristics of a product to deviate from their target values are called noise factors, which can be
classified as external factors (e.g. temperatures and human errors), manufacturing imperfections (e.g.
unit to unit variation in product parameters) and product deterioration. The overall aim of quality
engineering is to make products that are robust with respect to all noise factors. Taguchi used the
signal-to-noise (S/N) ratio as the quality characteristic of choice [1 and 13]. S/N ratio is used as a
measurable value instead of standard deviation due to the fact that as the mean decreases, the
standard deviation also decreases and vice versa. In other words, the standard deviation cannot be
minimized first and the mean brought to the target. Taguchi has empirically found that the two stage
optimization procedure involving S/N ratios indeed gives the parameter level combination, where the
standard deviation is minimum while keeping the mean on target [13]. This implies that engineering
systems behave in such a way that the manipulated production factors can be divided into three
categories:
1. Control factors, which affect process variability as measured by the S/N ratio.
2. Signal factors, which do not influence the S/N ratio or process mean.
3. Factors, which do not affect the S/N ratio or process mean.
In practice, the target mean value may change during the process development. Two of the
applications in which the concept of S/N ratio is useful are the improvement of quality through
variability reduction and the improvement of measurement. The S/N ratio characteristics can be
divided into three categories when the characteristic is continuous:

3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 3, March (2014), pp. 89-99, © IAEME
91
Nominal is the best characteristic:
ୗ
୒
ൌ 10 log
୷ഥ
ୱ౯
మ (1)
Smaller the better characteristics:
ୗ
୒
ൌ െ10 log
ଵ
୬
ሺ∑ yଶሻ (2)
And Larger the better characteristics:
ୗ
୒
ൌ െ10 log
ଵ
୬
ቀ∑
ଵ
୷మቁ (3)
where ‫ݕ‬ത is the average of observed data, s
y the variance of y, n the number of observations, and y the observed data.
For each type of the characteristics, with the above S/N ratio transformation, the higher the
S/N ratio the better is the result.
2. DESIGN OF EXPERIMENT
In this experiment with three factors at three levels each, the fractional factorial design used
is a standard L27 (313) orthogonal array [1]. This orthogonal array is chosen due to its capability to
check the interactions among factors. Each row of the matrix represents one trial. However, the
sequence in which these trials are carried out is randomized. The three levels of each factor are
represented by a ‘0’ or a ‘1’ or a ‘2’ in the matrix. The factors and levels are assigned as in Table 1
according to semi-finishing and finishing conditions for the said material when machining at high
cutting speed. Factors A, B, and C are arranged in columns 2, 5 and 6, respectively, in the standard
L27 (313) orthogonal array as shown in Appendix A.
2.1 Experimental Design and Setup
The study was carried out using a 8-inch chuck class CNC lathe UNIVERSAL TYPE CNC
TURNING MACHINE TU15, 12-corner tool rest with 12 pieces of tool attached with multiple tool
change capabilities (max number of tools = 21) and with 15 HP spindle horsepower as shown in
figure 2. The machine is capable of a three-axis movement (along the x, y, and z planes) Fast-
forwarding speed: X-axis 20 m/min, Z-axis 24 m/min. CNC programs can be developed in the VMC
CPU or downloaded from an external memory disc or data link. In this study, the CNC program was
downloaded from an external memory disc. The machining trials were carried out on a chine in dry
condition, as recommended. The machining trials were carried out in dry condition, as recommended
by the tool supplier for the specific work material. Chip outlet can be selected based on a layout of
machine. The surface roughness was measured using surface roughness tester model Mpi Mahr
Perthometer. Table 1 shows the chemical composition of work material in percentage by weight.

4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
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Figure 1: Surface roughness profile
Figure 2: Universal Type CNC Turning Machine Tu15
Table 1: Chemical composition of AISI 1040 in percentage by weight
Elements Iron (Fe) Manganese (Mn) Carbon (C) Sulfur (S) Phosphorous (P)
Contents (%) 98.6-99 0.60-0.90 0.37-0.44 < 0.05 < 0.04
Table 2: Estimated Minimum Values of Physical Properties of AISI1040
Tensile Strength,
(psi)
Yield Strength,
(Psi)
Elongation In
2in.,%
Reduction In
Area,%
Brinell
Hardness
76000 42000 18 40 149
2.2 Machining Operations and Machine Tools
1. Turning
2. Machining
Turning –a machining process in which a single-point tool remove material from the surface of a
rotating work piece (Lathe).

5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
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Rotational Speed ൌ
୚
πۭୈబ
(4)
D଴ െ D୤ ൌ 2d (5)
Feed rate (f୰ ൌ N ൈ fሻ (6)
Time of machining T୫ ൌ
୐
୤౨
(7)
Material removal rate MRR = vfd (8)
Figure 3: Turning operations with parameters
Cutting conditions
Spindle Speed is given by N ൌ
୚
πۭୈ
(9)
Feed rate f୰ ൌ N ൈ f (10)
Metal Removal Rate MRR ൌ
πୈమ୤౨
ସ
(11)
Machining time T୫ ൌ t ൅
୅
୤౨
(For a through hole) (12)
T୫ ൌ
ୢ
୤౨
(For a blind hole) (13)
Table 3: Factors and levels used in the experiment (axial depth of cut = 3 mm)
Factor Level 0 1 2
A—speed (m/min) 200 300 400
B—feed rate (mm per tooth) 0.15 0.20 0.25
C—radial depth of cut (mm) 0.3 0.6 0.9
3. RESULTS AND DISCUSSION
3.1 Experimental results and data analysis
The objective of experiment is to optimize the milling parameters to get better (i.e. low value)
surface roughness and resultant force values, the smaller the better characteristics are used.

6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
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Table 4.2 shows the actual data for surface roughness along with their computed S/N ratio.
Whereas Tables 5, 6 and 7 shows the mean S/N ratio for each levels of surface roughness for cutting
speed at three different levels 0, 1 and 2 respectively. These data were then plotted as shown in fig.
4.
4.2 Conceptual S/N ratio approach
Taguchi recommends analyzing the means and S/N ratio using conceptual approach that
involves graphing the effects and visually identifying the factors that appear to be significant,
without using ANOVA, thus making the analysis simple [3]. The average S/N ratios for smaller the
better for surface roughness factors and their significant interactions are given in figs. 4 to 7. From
the figures 4, 5, 6 and 7 it is observed that cutting speed (factor A) and interaction between feed rate
and depth of cut (interaction B × C) are more significant compared to other two parameters. Feed
rate (factor B) and depth of cut (factor C) are insignificant. The highest cutting speed (A0) appears to
be the best choice to get low value of surface finish, and thus making the process robust to the
cutting speed in particular. The feed rate (factor B) and depth of cut (factor C) are insignificant on
the average S/N response.
Table 4: Experimental results for surface roughness and S/N ratio
Exp.
Run
Factor
Designation
Measured
Parameters
S/N Ratio
A B C Surface
roughness Ra
(µm)
1 0 0 0 A଴B଴C଴ 0.213 13.432
2 0 1 1 A଴BଵCଵ 0.153 16.306
3 0 2 2 A଴BଶCଶ 0.643 3.835
4 1 0 0 AଵB଴C଴ 0.233 12.652
5 1 1 1 AଵBଵCଵ 0.215 13.351
6 1 2 2 AଵBଶCଶ 0.276 11.181
7 2 0 0 AଶB଴C଴ 0.590 4.582
8 2 1 1 AଶBଵCଵ 0.601 4.422
9 2 2 2 AଶBଶCଶ 0.689 3.235
10 0 0 1 A଴B଴Cଵ 0.248 12.110
11 0 1 2 A଴BଵCଶ 0.412 7.702
12 0 2 0 A଴BଶC଴ 0.493 6.143
13 1 0 1 AଵB଴Cଵ 0.269 11.404
14 1 1 2 AଵBଵCଶ 0.251 12.006
15 1 2 0 AଵBଶC଴ 0.633 3.971
16 2 0 1 AଶB଴Cଵ 0.703 3.060
17 2 1 2 AଶBଵCଶ 1.104 -0.859
18 2 2 0 AଶBଶC଴ 0.683 3.311
19 0 0 2 A଴B଴Cଶ 1.104 -1.385
20 0 1 0 A଴BଵC଴ 0.828 1.639
21 0 2 1 A଴BଶCଵ 0.931 0.621
22 1 0 2 AଵB଴Cଶ 1.687 -4.542
23 1 1 0 AଵBଵC଴ 1.021 -0.180
24 1 2 1 AଵBଶCଵ 0.919 0.733
25 2 0 2 AଶB଴Cଶ 1.522 -3.648
26 2 1 0 AଶB଴Cଶ 1.103 -0.851
27 2 2 1 AଶBଶCଵ 1.412 -2.996

7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
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Table 5: Experimental results for Mean S/N ratio for cutting speed at level 0
S. No. Mean S/N Ratio Mean S/N Ratio Calculated S/N Ratio
Cutting Speed (A) Level 0 Cutting Speed (A) Level 1 Cutting Speed (A) Level 2
1 13.432 12.652 4.582
2 16.306 13.351 4.422
3 3.835 11.181 3.235
4 12.110 11.404 3.060
5 7.702 12.006 -0.859
6 6.143 3.971 3.311
7 -1.385 -4.542 -3.648
8 1.639 -0.180 -0.851
9 0.621 0.733 -2.996
Total 6.711 5.324 1.139
Figure 4: Smaller the better S/N graph for surface roughness at various cutting speed
Table 6: Experimental results for Mean S/N ratio for Feed rate at level 0
S. No. Mean S/N Ratio Calculated S/N Ratio Calculated S/N Ratio
Feed Rate (B) Level 0 Feed Rate (B) Level 1 Feed Rate (B) Level 2
1 13.432 16.306 3.835
2 12.652 13.351 11.181
3 4.582 4.422 3.235
4 12.110 7.702 6.143
5 11.404 12.006 3.971
6 3.060 -0.859 3.311
7 -1.385 1.639 0.621
8 -4.542 -0.180 0.733
9 -3.648 -0.851 -2.996
Total 5.296 5.948 3.337
Cutting
Speed
(m/min)
0
2
4
6
8
1 2 3
MeanS/NRatio
The Smaller the better S/N graph

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Figure 5: The smaller the better S/N graph for surface roughness at various feed rate
Table 7: Experimental results for Mean S/N ratio for Depth of cut at level 0
S. No. Mean S/N Ratio Mean S/N Ratio Mean S/N Ratio
Depth of Cut (C) Level 0 Depth of Cut (C) Level 1 Depth of Cut (C)
Level 2
1 13.432 16.306 3.835
2 12.652 13.351 11.181
3 4.582 4.422 3.235
4 6.143 12.110 7.702
5 3.971 11.404 12.006
6 3.311 3.060 -0.859
7 1.639 0.621 -1.385
8 -0.180 0.733 -4.542
9 -0.851 -2.996 -3.648
Total 4.966 6.556 3.058
Figure 6: The smaller the better S/N graph for surface roughness at various depth of cut
Radial
depth of cut
(mm)
0
2
4
6
8
1 2 3
MeanS/Nratio
The smaller the better S/N ratio for surface
roughness
Feed Rate
(mm/tooth)
0
1
2
3
4
5
6
7
1 2 3
MeanS/Nratio
Smaller the Better S/N graph for surface
roughness

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Table 8: Experimental results for Mean S/N ratio for interaction (BXC) at level 0
S. No. Mean S/N Ratio Mean S/N Ratio Mean S/N Ratio
Interaction (BXC) Level 0 Interaction (BXC)
Level 1
Interaction (BXC)
Level 2
1 13.432 12.110 -1.385
2 16.306 7.702 1.639
3 3.835 6.143 0.621
4 12.652 11.404 -4.542
5 13.351 12.006 -0.180
6 11.181 3.971 0.733
7 4.582 3.060 -3.648
8 4.422 -0.859 -0.851
9 3.235 3.311 -2.996
Total 9.221 6.538 -1.178
Figure 7: The smaller the better S/N graph for surface roughness interaction between feed rate and
depth of cut (BXC)
Table 9: Response table for average S/N ratio for surface roughness factors and significant
interaction
Cutting Parameters Max-Min Net Value
Cutting Speed (A) 6.711-1.139 5.572
Feed Rate (B) 5.948-3.337 2.611
Depth of Cut (C) 6.556-3.058 3.498
BXC 9.221 – (-1.178) 10.399
The use of S/N ratio for selecting the best levels of combination for surface roughness (Ra)
value suggests the use of low value of feed rate in order to obtain good finish. Smaller angle of tool
angular position is obtained at lower depth of cut [15]. Therefore, it is preferable to set the depth of
cut to a low value. Therefore, one can say that the set values for level ‘0’ and ‘1’ are both suitable to
obtain good quality of surface finish. From the result, the interaction of factor B and factor C is more
important than the effect of the individual factors. In other words, in order to get the best result it
requires experience to combine these two factors to achieve a suitable combination of feed rate and
depth of cut. The S/N ratio suggests that cutting force depends on feed rate and depth of cut. Both the
feed rate and depth of cut are found to be at level ‘0’ for the best combination to obtain low value of
(BXC)
-2
0
2
4
6
8
10
MeanS/NRatio
The Smaller the better S/N ratio for
surface roughness

10. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
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surface roughness. The combination of feed rate and depth of cut determines the undeformed chip
section and hence the amount of energy required to remove a specified volume of material.
Appendix A
L27 (313
) standard orthogonal array table with factors A, B and C arranged in column 2, 5 and 6
respectively. The interactions among factors are indicated as in columns 1, 7, 8, 9, 11 and 12.
Exp. Run 1 2 3 4 5 6 7 8 9 10 11 12 13
1 0 0 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 1 1 1 1 1 1 1 1 1
3 0 0 0 0 2 2 2 2 2 2 2 2 2
4 0 1 1 1 0 0 0 1 1 1 2 2 2
5 0 1 1 1 1 1 1 2 2 2 0 0 0
6 0 1 1 1 2 2 2 0 0 0 1 1 1
7 0 2 2 2 0 0 0 2 2 2 1 1 1
8 0 2 2 2 1 1 1 0 0 0 2 2 2
9 0 2 2 2 2 2 2 1 1 1 0 0 0
10 1 0 1 2 0 1 2 0 1 2 0 1 2
11 1 0 1 2 1 2 0 1 2 1 1 2 0
12 1 0 1 2 2 0 1 2 0 0 2 0 1
13 1 1 2 0 0 1 2 1 2 0 2 0 1
14 1 1 2 0 1 2 0 2 0 1 0 1 2
15 1 1 2 0 2 0 1 0 1 2 1 2 0
16 1 2 0 1 0 1 2 2 0 1 1 2 0
17 1 2 0 1 1 2 0 0 1 2 2 0 1
18 1 2 0 1 2 0 1 1 2 0 0 1 2
19 2 0 2 0 0 2 1 0 2 1 0 2 1
20 2 0 2 0 1 0 2 1 0 2 1 0 2
21 2 0 2 0 2 1 0 2 1 0 2 1 0
22 2 1 0 1 0 2 1 1 0 2 2 1 0
23 2 1 0 1 1 0 2 2 1 0 0 2 1
24 2 1 0 1 2 1 0 0 2 1 1 0 2
25 2 2 1 2 0 2 1 2 1 0 1 0 2
26 2 2 1 2 1 0 2 0 2 1 2 1 0
27 2 2 1 2 2 1 0 1 0 2 0 2 1
BXC A B C BXC AXB AXC AXB AXC
CONCLUSION
From the analysis of result in turning process using conceptual S/N ratio approach the
following can be concluded from the present study that the Taguchi’s robust design method is
suitable to analyze the metal cutting problem as described in this work. Conceptual S/N ratio
approaches for data analysis has been used and it gives the similar results published in other
literatures. In turning operation, use of high cutting speed (400 m/min), low feed rate (0.15mm per
tooth) and low depth of cut (0.3 mm) are recommended to obtain better surface finish for the specific
test range. Generally, the use of high cutting speed, low feed rate and low depth of cut leads to better
surface finish.

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