20120140503002

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. 09-20, © IAEME
9
OPTIMIZATION OF END MILLING PARAMETERS OF AISI 1055 BY
TAGUCHI METHOD
Kareem Idan Fadheel
Ministry of Higher Education & Scientific Research, Foundation of Technical Education,
KUT Technical Institutes, Republic of Iraq
Dr. Mohammad Tariq
Department of Mechanical Engineering, Shepherd School of Engineering and Technology,
SHIATS-DU, Naini, Allahabad-211007
ABSTRACT
CNC End milling is a unique adaption of the conventional milling process which uses an end
mill tool for the machining process. During the End milling process, the material is removed by the
end mill cutter. Surface finish is an important indicator of the milling operation in manufacturing
process. This paper, aims to optimize milling parameters depend on the Taguchi method for
minimizing surface roughness (Ra). The experiments were conducted using the L18 orthogonal array
in a CNC milling machine with respect to three different cutting parameters. These parameters were
cutting depth, cutting speed and feed rate. Dry milling tests were performed on hardened AISI 1055
(48HRC) with APTK 1705 LT 30 carbide inserts as a cutting condition in order to reduce
optimization parameters such as the coolant. Each experiment was repeated five times and a new
insert was used for each test to ensure accurate readings of surface roughness. In addition, the
statistical methods of signal to noise ratio (S/N) and analysis of variance (ANOVA) were carried out
to investigate the effects of cutting parameters on surface roughness. As a result of the experiment,
the feed rate has the most dominant effect on average surface roughness. The effects of interactions
of factors appear to be important, especially cutting speed-feed rate pair. These statistical methods
can be very useful in manufacturing units to optimize the manufacturing processes.
Keywords: Taguchi Method, End Milling, S/N Ratio, Surface Roughness, ANOVA.
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. 09-20
© IAEME: www.iaeme.com/ijaret.asp
Journal Impact Factor (2014): 7.8273 (Calculated by GISI)
www.jifactor.com
IJARET
© I A E M E

10
1. INTRODUCTION
Milling operates on the principle of rotary motion. A milling cutter is spun about an axis
while a workpiece is advanced through it in such a way that the blades of the cutter are able to shave
chips of material with each pass. Milling processes are designed such that the cutter makes many
individual cuts on the material in a single run; this may be accomplished by using a cutter with many
teeth, spinning the cutter at high speed, or advancing the material through the cutter slowly. Most
often it is some combination of the three [2]. The speed at which the piece advances through the
cutter is called feed rate, or just feed; it is most often measured in length of material per full
revolution of the cutter.
As material passes through the cutting area of a milling machine, the blades of the cutter take
swarfs of material at regular intervals. This non-continuous cutting operation means that no surface
cut by a milling machine will ever be completely smooth; at a very close level (microscopic for very
fine feed rates), it will always contain regular ridges. These ridges are known as revolution marks,
because rather than being caused by the individual teeth of the cutter, they are caused by
irregularities present in the cutter and milling machine; these irregularities amount to the cutter being
at effectively different heights above the workpiece at each point in its rotation. The height and
occurrence of these ridges can be calculated from the diameter of the cutter and the feed [4]. These
revolution ridges create the roughness associated with surface finish. Robust design is an engineering
methodology for obtaining product and process conditions, which are minimally sensitive to the
various causes of variation to produce high-quality products with low development and
manufacturing costs [12]. Taguchi’s parameter design is an important tool for robust design. It offers
a simple and systematic approach to optimize design for performance, quality and cost. Two major
tools used in robust design are [12–14]:
signal to noise ratio, which measures quality with emphasis on variation
Orthogonal arrays, which accommodate many design factors simultaneously.
The successful applications of Taguchi methods by both engineers and statisticians within
British industry have lead to the formation of UK Taguchi Club [15]. Taguchi’s approach is totally
based on statistical design of experiments [12], and this can economically satisfy the needs of
problem solving and product/process design optimization [16]. By applying this technique one can
significantly reduce the time required for experimental investigation, as it is effective in investigating
the effects of multiple factors on performance as well as to study the influence of individual factors
to determine which factor has more influence, which less [12,16]. Some of the previous works that
used the Taguchi method as tool for design of experiment in various areas including metal cutting are
listed in [17–23] references.
1.1 Taguchi methods
Taguchi methods are statistical methods developed by Genichi Taguchi to improve the
quality of manufactured goods, and more recently also applied to engineering [1], biotechnology
[2, 3], marketing and advertising [4]. Professional statisticians have welcomed the goals and
improvements brought about by Taguchi methods, particularly by Taguchi's development of designs
for studying variation, but have criticized the inefficiency of some of Taguchi's proposals [5].
Taguchi’s work includes three principal contributions to statistics:
A specific loss function
The philosophy of off-line quality control
Innovations in the design of experiments

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue
1.2 Design of Experiments
Taguchi developed his experimental theories independently. Taguchi read works following R.
A. Fisher only in 1954. Taguchi's framework for design of experiments
flawed, but contains much that is of enormous value.
sequential designs of response surface methodology
a sequence of Taguchi's designs [8].
2. METHODOLOGY
The design of parameters in the Taguchi method aims to determine the parameters generating
the best levels of optimum cutting conditions. To find the optimum cutting conditions, the Taguchi
method uses the signal to noise ratio (S/N) via an orthogonal arra
value. The signal to noise ratio characteristics can be calculated in three different ways;
• Smaller the better
• The larger the better
• The nominal-the best
The equations of the three levels are given
Smaller the better
Larger-the better
Nominal the best
where n is repeat number of experiment and is measured variable value i
average of observed data, the variance of
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.
The optimum cutting parameters required for the best surface roughness were obtained by
using eq.1 the smaller the better. By using the smaller
levels were estimated. S/N ratios according to the mentioned e
paragraphs.
Fig 1: Shear length and shear angle in chip formation process
6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME
11
only in 1954. Taguchi's framework for design of experiments is idiosyncrat
flawed, but contains much that is of enormous value. He made a number of innovations.
sequential designs of response surface methodology require far fewer experimental runs than would
method uses the signal to noise ratio (S/N) via an orthogonal array. S/N ratio is used as a measurable
The equations of the three levels are given by eq. (1-3).
Smaller the better
the better
where n is repeat number of experiment and is measured variable value in equ
the variance of y, n the number of observations, and
using eq.1 the smaller the better. By using the smaller-the better (eq.1), S/N ratios of parameters and
levels were estimated. S/N ratios according to the mentioned equations are given in following
Shear length and shear angle in chip formation process
, © IAEME
is idiosyncratic and often
He made a number of innovations. The
require far fewer experimental runs than would
y. S/N ratio is used as a measurable
(1)
(2)
(3)
n equation is the
the number of observations, and y the observed
the better (eq.1), S/N ratios of parameters and
quations are given in following

6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue
The chip formation process is influenced by the shear length
(ls) is given as:
where t is undeformed chip thickness and
shear angle ∅ is large at high cutting speeds, therefore the shear length
figure 1.
End milling is aim to removing material
work pieces. Basically the tool has rotating motion (spindle speed) and the work pieces is linear ones
(feed rate) as shown in fig. 2.
Fig.
2.1 Experimental design
Response surface methodology (RSM) is a procedure which is able to determine a
relationship between independent input process parameters (e.g. cutting parameters) and output data
(process response, e.g. Ra). In the current study, the relationship between the input pa
cutting conditions (cutting speed (vc
parameters, defined as the machinability aspect (
Ra= (vc, f, t)
where is the response function.
The approximation of Ra is proposed by using the following equation which consists of linear
and quadratic effects of the input parameters and their interactions as well:
6499(Online) Volume 5, Issue 3, March (2014), pp. 09-20, © IAEME
12
The chip formation process is influenced by the shear length (ls) in the shear zone. The shear length
is undeformed chip thickness and is the shear angle [25]. Philip [
is large at high cutting speeds, therefore the shear length (ls) is small, as shown in
End milling is aim to removing material by two continuous motions. Those are the tool and
Fig. 2: End milling operation
rface methodology (RSM) is a procedure which is able to determine a
). In the current study, the relationship between the input pa
c, m/min), feed rate (f, mm), depth of cut (t, mm), and the output
parameters, defined as the machinability aspect (Ra) which is given as:
is proposed by using the following equation which consists of linear
and quadratic effects of the input parameters and their interactions as well:
, © IAEME
in the shear zone. The shear length
(4)
Philip [24] found the
is small, as shown in
by two continuous motions. Those are the tool and
rface methodology (RSM) is a procedure which is able to determine a
). In the current study, the relationship between the input parameters, as the
, mm), and the output
(5)
is proposed by using the following equation which consists of linear

13
Rୟ ൌ b଴ ൅ bଵvୡ ൅ bଶf ൅ bଷt ൅ bଵଵvୡ
ଶ
൅ bଶଶfଶ
൅ bଷଷtଶ
൅ bଵଶvୡf ൅ bଵଷvୡt ൅ bଶଷftɛ (6)
where bis are the calculated coefficients, vc, f and t are input parameters, and ε is the
experimental error.
Chemical composition of AISI 1055
Table 1: Chemical Composition of the Alloy Steel AISI 1055
S. No. Constituents Chemical Composition Limits, (%)
1 C 0.5 – 0.6
2 Mn 0.6 – 0.9
3 P 0.04 (Max.)
4 S 0.05 (Max.)
Table 2: Estimated Physical Properties of Hot Rolled Carbon Steel Bars
S. No. Properties Estimated Minimum Values
1 Tensile Strength (psi) 94,000
2 Yield Strength (psi) 51,500
3 Elongation In 2in., (%) 12
4 Reduction in Area, (%) 30
5 Brinell Hardness 192
2.2 Experimental Procedure
In order to achieve the correlation between cutting parameters and surface roughness,
different parameters were used in the experiments. As work material, hardened AISI 1055
(200x50x50 mm) steel was used. The hardness of AISI 1055 steel was increased by applying heat
treatment and measured hardness of AISI 1055 steel was 48 HRC. Hardened AISI 1055 steel parts
were machined with KAFU CNC milling machine. The samples were prepared by cutting all
surfaces at 2 mm depth of cut with the milling process to obtain clear surface for measurement and
their sizes were fixed to 50x50x40 mm dimension because of the dimensional differences of work
part size at the end of heat treatment. The experiment was performed under dry conditions. The tool
holder and insert were used as 5 slots RILT730-M-W-D2500/5 and APTK 1705 LT30 respectively.
The measurement of the average surface roughness (Ra) on surface of hardened AISI 1055 was taken
by Mitutoyo SJ – 301P portable device within the sampling length of 250 mm and the measurements.
The levels of cutting parameters; depth of cut (a), feed rate (f) and cutting speed (v) were chosen
from the insert manufacturer’s catalogue in accordance with recommended test values. The cutting
parameters were given in Table 3. The Taguchi and variance of analysis were carried out to reduce
the number of the experiments and optimize cutting parameters.
3. RESULTS AND DISCUSSION
The experiment has done to optimize the milling parameters to get better (i.e. low value)
surface roughness; the smaller the better characteristics are used. Table 4 shows the actual data for
surface roughness and corresponding computed S/N ratio for these parameters. Whereas table 5, 6, 7
and 8 show the mean S/N ratio for each levels of surface roughness for various parameters (A, B and
C) and their one interaction namely BXC. Fig 3 shows the mean S/N ratio for various parameters of
surface roughness.

14
Table 3: Input parameters taken for calculations
Factor Level
0 1 2
A- Cutting Speed (vc, m/min) 250 300 350
B - Feed (f, mm/tooth) 0.10 0.20 0.30
C - Radial Depth of Cut (t, mm) 0.25 0.50 0.75
Table 4: Experimental results for Surface roughness and their 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଴ 0.186 14.609
2 0 1 1 A଴BଵCଵ 0.175 15.139
3 0 2 2 A଴BଶCଶ 0.432 7.290
4 1 0 0 AଵB଴C଴ 0.296 10.574
5 1 1 1 AଵBଵCଵ 0.301 10.420
6 1 2 2 AଵBଶCଶ 0.317 9.972
7 2 0 0 AଶB଴C଴ 0.571 4.861
8 2 1 1 AଶBଵCଵ 0.586 4.642
9 2 2 2 AଶBଶCଶ 0.606 4.350
10 0 0 1 A଴B଴Cଵ 0.282 10.995
11 0 1 2 A଴BଵCଶ 0.506 5.916
12 0 2 0 A଴BଶC଴ 0.464 6.669
13 1 0 1 AଵB଴Cଵ 0.243 12.280
14 1 1 2 AଵBଵCଶ 0.726 2.786
15 1 2 0 AଵBଶC଴ 0.281 11.025
16 2 0 1 AଶB଴Cଵ 0.311 10.144
17 2 1 2 AଶBଵCଶ 0.652 3.715
18 2 2 0 AଶBଶC଴ 0.693 3.185
19 0 0 2 A଴B଴Cଶ 1.121 -0.992
20 0 1 0 A଴BଵC଴ 0.812 1.808
21 0 2 1 A଴BଶCଵ 0.923 0.695
22 1 0 2 AଵB଴Cଶ 1.552 -3.817
23 1 1 0 AଵBଵC଴ 0.912 0.800
24 1 2 1 AଵBଶCଵ 0.920 0.724
25 2 0 2 AଶB଴Cଶ 1.461 -3.290
26 2 1 0 AଶB଴Cଶ 1.217 -1.705
27 2 2 1 AଶBଶCଵ 1.338 -2.529
It is clearly indicated from the table 4.2 that the S/N ratio has very limited variation
irrespective to the actual values of surface roughness.

15
Table 5: Experimental results for Mean S/N ratio for cutting speed at different levels for Surface
roughness
S. No. Calculated S/N Ratio Calculated S/N Ratio Calculated S/N Ratio
Cutting Speed (A)
Level 0
Cutting Speed (A)
Level 1
Cutting Speed (A)
Level 2
1 14.609 10.574 4.861
2 15.139 10.420 4.642
3 7.290 9.972 4.350
4 10.574 12.280 10.144
5 5.916 2.786 3.715
6 6.669 11.025 3.185
7 -0.992 -3.817 -3.290
8 1.808 0.800 -1.705
9 0.695 0.724 -2.529
Mean S/N Ratio 6.856 6.76 2.59
Table 6: Experimental results for Mean S/N ratio for feed rate at different levels for Surface
Roughness
Feed Rate (B) Level 0 Feed Rate (B) Level 1 Feed Rate (B) Level 2
1 14.609 15.139 7.290
2 10.574 10.420 9.972
3 4.861 4.642 4.350
4 10.995 5.916 6.669
5 12.280 2.786 11.025
6 10.144 3.715 3.185
7 -0.992 1.808 0.695
8 -3.817 0.800 0.724
9 -3.290 -1.705 -2.529
Mean S/N Ratio 6.151 4.835 4.597
Table 7: Experimental results for Mean S/N ratio for Depth of Cut at different levels for Surface
Roughness
Depth of Cut (C)
Level 0
Depth of Cut (C)
Level 1
Depth of Cut (C)
Level 2
1 14.609 15.139 7.290
2 10.574 10.420 9.972
3 4.861 4.642 4.350
4 6.669 10.995 5.916
5 11.025 12.280 2.786
6 3.185 10.144 3.715
7 1.808 0.695 -0.992
8 0.800 0.724 -3.817
9 -1.705 -2.529 -3.290
Mean S/N Ratio 5.758 6.945 2.881

16
Table 8: Experimental results for Mean S/N ratio for interaction (BXC) at different levels for
Surface Roughness
Interaction (B⨉⨉⨉⨉C)
Level 0
Level 1
Level 2
1 14.609 10.995 -0.992
2 15.139 5.916 1.808
3 7.290 6.669 0.695
4 10.574 12.280 -3.817
5 10.420 2.786 0.800
6 9.972 11.025 0.724
7 4.861 10.144 -3.290
8 4.642 3.715 -1.705
9 4.350 3.185 -2.529
Mean S/N Ratio 9.095 7.412 -0.922
Fig 3: Mean S/N ratio for various parameters of Surface roughness
Table 9: Response table for average S/N ratio for surface roughness factors and significant
interaction for Surface Roughness
Cutting Parameters Max-Min Net Value
Cutting Speed (A) 6.856-2.59 4.266
Feed Rate (B) 6.151-4.597 1.554
Depth of Cut (C) 6.945-2.881 4.064
Interaction (B⨉C) 9.095– (-0.922) 10.017

17
4. 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 [12]. 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. 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.
4.1 Pareto ANOVA Analyses
One of the methods to analyze data for process optimization is the use of Pareto ANOVA.
Pareto ANOVA is a simplified ANOVA method which uses Pareto principles. It is a quick and easy
method to analyze results of parameter design. It does not require an ANOVA table and therefore
does not use F-tests. Following are the Pareto ANOVA table for surface roughness analysis. The
Pareto ANOVA technique of analysis has been performed, which requires least knowledge about
ANOVA method and suitable for engineers and industrial practitioners. The use of S/N ratio for
selecting the best levels combination for surface roughness suggests that cutting speed (factor A) and
interaction B×C have strong effect on the surface roughness. From the result obtained, the best
combination to get low value of surface roughness is at level ‘2’ of cutting speed, level ‘0’ of feed
rate, and level ‘1’ of depth of cut. Since the role of depth of cut is least in obtaining good surface
finish, it is indicated that in order to achieve good surface finish, always use high cutting speed and
low feed rate. By increasing the cutting speed, surface roughness values are kept at minimum.
Table 10: Pareto ANOVA analysis for surface roughness
Sum of Factor Level Factor and interaction
BXC A B C BXC AXB AXC AXB AXC
0 81.857 61.434 55.364 51.826 41.351 52.985 44.284 43.624 51.285
1 66.715 54.764 43.521 62.51 42.981 46.906 54.003 56.299 42.111
2 -8.306 25.902 41.381 25.93 55.934 40.375 41.979 40.343 46.87
Sum of Squares of
differences (S)
13986.796 2140.027 340.36 2122.846 383.1 238.62 244.348 450.567 126.302
Contribution ratio (%) 69.818 10.682 1.698 10.596 1.912 1.191 1.219 2.249 0.630
1 2 3 4 5 6 7 8 9
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
AXCAXBAXCBBXCAXB
CA
BXC
Contributionratio(%)
Factors and Interactions
Values in (%)
Fig 4: Pareto diagram for surface roughness
From the table 10, the optimum combination of significant factor level is A0B0C1 (maximum)
for surface roughness. Figure 4 shows the maximum value to (BXC) for surface roughness.

18
Table 11: Calculated BXC two way tables for surface roughness
Factor level B0 B1 B2 TOTAL
C0 30.044 0.903 20.879 51.826
C1 33.419 30.201 -1.11 62.51
C2 -8.099 12.417 21.612 25.93
TOTAL 55.364 43.521 41.381 140.266
From the BC two way table 11, B0C1 (max) is found to be an optimal condition for surface
roughness.
4.2 Discussion
In the above experimental results, two techniques of data analysis have been used. Both
techniques draw similar conclusions. The cutting speed has found to be the most significant effect to
produce low value of average surface roughness (Ra). The explanation for the influence of cutting
speed on surface finish is still not available. This could be explained in terms of the velocity of chips
that is faster at high cutting speed than at low cutting speed. This leads to a shorter time for the chips
to be in contact with the newly formed surface of workpiece and the tendency for the chips to wrap
back to the new face form is little as compared to low speed. The condition of seizure and sub layer
plastic flow occurred at high speed and the term flow-zone is used to describe secondary deformation
in this range [27]. The time taken for the chips at this flow-zone for high speed cutting is short as
compared to lower speed, as the velocity of chip is faster. 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
[28]. 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.
4.3 Conclusion
This paper illustrates the application of the parameter design (Taguchi method) in the
optimization of end milling operation. The following conclusions can be drawn based on the above
experimental results of this study:
Taguchi’s Method of parameter design can be performed with lesser number of
experimentations as compared to that of full factorial analysis and yields similar results.
Taguchi’s method can be applied for analyzing any other kind of problems as described in
this paper.
It is found that the parameter design of the Taguchi method provides a simple, systematic,
and efficient methodology for optimizing the process parameters
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