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Engineering, Technology & Applied Science Research Vol. 11, No. 5, 2021, 7551-7557 7551
www.etasr.com Khanh & Cuong: Parameter Selection to Ensure Multi-Criteria Optimization of the Taguchi Method …
Parameter Selection to Ensure Multi-Criteria
Optimization of the Taguchi Method Combined with
the Data Envelopment Analysis-based Ranking
Method when Milling SCM440 Steel
Nguyen Lam Khanh
Faculty of Mechanical Engineering
University of Transport and Communications
Hanoi, Vietnam
khanh_mxd@utc.edu.vn
Nguyen Van Cuong
Faculty of Mechanical Engineering
University of Transport and Communications
Hanoi, Vietnam
nguyencuong@utc.edu.vn
Abstract-SCM440 steel is a commonly used material for making
plastic injection molds and components such as gears,
transmission shafts, rolling pins, etc. Surface roughness has a
direct influence on the workability and durability of the parts
and/or components, while the Material Removal Rate (MRR) is a
parameter that is used to evaluate the productivity of the
machining process. Furnished products with small surface
roughness and large MRR is the desired result by all milling
processes. In this paper, the determination of the values of input
parameters is studied in order to ensure that during the process
of milling SCM440 steel, it will have the smallest surface
roughness and the largest MRR. There are five parameters that
are required to be determined, namely the cutting insert
material, the tool nose radius, the cutting speed, the feed rate,
and the cutting depth. The Taguchi method was applied to design
the experimental matrix with a total of 27 experiments. Result
analysis determined the influence of the input parameters on
surface roughness and MRR. The Data Envelopment Analysis-
based Ranking (DEAR) method was applied to determine the
optimal value of the input parameters, which were used to
conduct the milling experiments to re-evaluate their suitability.
Keywords-milling; SCM440 steel; surface roughness; MRR;
optimization; Taguchi; DEAR
I. INTRODUCTION
Milling is a very common machining method in the
mechanical engineering industry. It is considered to be the
cutting method with the highest productivity. With the
development of the cutting tool manufacturing technology as
well as the emergence of the modern CNC machines, this
method is capable of ensuring very high accuracy. Research of
solutions that improve the accuracy of milling machines and
milling cutters is continuously conducted [1]. Therefore, in
some cases, this method is used as the final machining method
for surfaces requiring high precision [2]. To make the most use
of the achievements in the mechanical engineering technology
and the cutting tool manufacturing technology, many
researchers carried out experimental studies to determine the
optimal value of parameters related to the machining process.
The purpose of these studies is to determine the value of the
machining process’s parameters to ensure minimum surface
roughness and maximum Material Removal Rate (MRR). This
problem is known as milling operations optimization. When
studying the milling operations optimization, many authors
used the Taguchi method to design the experimental matrix.
When comparing the Taguchi-based matrix design method with
some other matrix design methods, it was found that the
Taguchi method requires a smaller number of experiments. An
advantage that only the Taguchi method possesses is that it can
design a matrix with the input parameters being a qualitative
(not a quantitative) parameter [3, 4].
In [5], the Taguchi method was applied to design the
experimental matrix when milling the AA6082T6 material by a
tungsten carbide cutting tool. Signal-to-Noise (S/N) ratio was
analyzed to determine the optimal values of spindle speed, feed
rate, and depth of cut to ensure the minimum value of surface
roughness. The Taguchi method was also used to design the
experimental matrix when milling AA6082T6 with PVD-
coated and CVD-coated cutting tools. The determination of
cutting speed, feed rate, and tool material type to ensure tool
wear was similarly performed for surface roughness in [6]. In
[7], the authors applied the Taguchi method to design the
experimental matrix when milling D2 steel with the carbide
inserts cutting tool. Cutting parameters including spindle speed,
feed rate, and depth of cut were selected as input parameters for
each experiment. The S/N ratio analysis method was applied to
determine the optimal value of the cutting parameters to ensure
the minimum value of surface roughness. When milling AISI
P20 steel with the carbide inserts cutting tool, the authors in [8]
used the Taguchi method to design an experimental matrix with
spindle speed, feed rate, and depth of cut as input parameters
[8]. They also used S/N ratio analysis to determine the optimal
value of the input parameters to ensure the minimum value of
surface roughness. To determine the optimal value of
parameters including cutting speed, feed, radial depth, and
axial depth to ensure the minimum value of surface roughness
when milling 1.2738 steel with the WNHU 04T310 cutting tool
Corresponding author: Nguyen Van Cuong
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(manufactured by Palbit), the authors in [9] also applied the
Taguchi method to design the experimental matrix. The S/N
ratio analysis method was applied to determine the optimal
value of the cutting parameters. Authors in [10] also designed
the experimental matrix according to the Taguchi method when
milling 7075T6 aluminum alloy with an AlTiN PVD-coated
cutting tool. The optimal values of cutting speed, feed rate,
radial depth, and axial depth were also determined by the
analysis of the S/N ratio. This study also aimed to ensure the
minimum value of surface roughness. To determine the
optimum value of cutting speed, feed rate, depth of cut, and
coolant flow to ensure the minimum value of surface roughness
when milling AISI 1040 MS steel with the carbide inserts
cutting tool, the authors in [11] also designed the experimental
matrix according to the Taguchi method and the S/N ratio
analysis method was also used to determine the optimal value
of the input parameters. Authors in [12] also designed the
experimental matrix according to the Taguchi method when
milling 7075 T6 aluminum alloy with a High-Speed Steel
(HSS) cutting tool. They also applied the S/N ratio analysis
method to determine two sets of optimal values of cutting
speed, feed rate, and depth of cut, one set that ensures the
smallest surface roughness and another set that ensures the
largest MRR.
The experimental matrix design based on the Taguchi
method has been successfully applied in a number of studies to
ensure a certain criterion of the machining process. However, if
only the Taguchi method is applied for the experimental design
and the S/N ratio analysis to determine the optimal value of the
machining process parameters, only one criterion of the
machining process can be guaranteed. In order to resolve this
shortcoming of the Taguchi method, many studies combined
the Taguchi method with other methods to optimize the multi-
objectives of the milling process: in [13], the Taguchi method
has been combined with ANOVA to determine the values of
the spindle speed, the feed rate and the cutting depth to ensure
the minimum surface roughness and the maximum MRR when
milling AISI 1005 steel with the TiN coated cutting tool. In
[14], the Taguchi method and the Weighted Principal
Component Analysis (WPCA) were combined to determine the
milling type and the values of milling parameters to
simultaneously ensure minimum surface roughness and
maximum MRR when milling Al 6061 aluminum alloy with a
high speed steel cutting tool. The Taguchi method and the Gray
Relational Analysis (GRA) method were combined to
determine the values of cutting speed, feed rate, and cutting
depth to ensure simultaneously minimum surface roughness
and maximum MRR when milling Inconel 718 super alloy by
an uncoated tungsten carbide cutting tool in [15]. A
combination of the Taguchi method, TOPSIS method, and
ANOVA analysis was performed to determine the optimal
values of cutting speed, feed rate, and depth of cut to ensure
simultaneously the minimum value of surface roughness and
maximum value of MRR when milling Ti-6Al-4V titanium
alloy with TiN coated cutting tools in [16]. Through the above
studies, it is shown that the cutting tool parameters are
commonly selected as the input parameters of the milling
experiment process. These parameters can be easily adjusted by
the operators. However, to the best of our knowledge, there
have been no studies that consider all the 5 parameters of the
cutting tool material, i.e. the insert material, the tool nose
radius, the cutting speed, the feed rate, and the depth of cut.
SCM440 steel is a type of steel used quite commonly to
make plastic injection molds and components such as gears,
transmission shafts, and rolling pins [17]. Due to the high
content of Cr, Mo and Mn elements, this steel has a low
thermal conductivity. When machining this steel, the tool wears
out quickly, thus it is required to select the right cutting tool
[18-20]. A number of studies on milling steels equivalent to
this steel have been carried out [17-23]. However, to the best of
our knowledge, there are no published studies on milling
SCM440 (or equivalent) steel that consider all 5 parameters.
DEAR is a method used for multi-criteria decision making that
was introduced in 2002 [24]. This method has been used for the
multi-objective optimization of the AISI 1055 steel turning
process [25], the Ti-6Al-4V alloy turning process [26], the
SAE420 steel grinding with a segmented grinding wheel [27],
of the Electrical Discharge Machining (EDM) with the material
type AA 6082 [26], etc. However, to the best of our
knowledge, there have been no published studies on the
application of this method in multi-criteria decision making for
milling methods in general and for milling SCM440 steel in
particular.
In this study, milling parameters such as the cutting tool
material, the tool nose radius, the cutting speed, the feed rate,
and the cutting depth will be determined to simultaneously
ensure the optimization of two criteria, minimum surface
roughness and maximum MRR when milling this type of steel.
A combination of the Taguchi method and the DEAR method
was used to solve this problem.
II. THE DEAR METHOD
The purpose of the experimental process of this study is to
ensure that surface roughness (Ra) has the smallest value and
the MRR reaches the maximum value. Thus, it is required to
determine the values of the input parameters that ensure the set
out objectives. The DEAR method will be applied in this study
to carry out the above-stated work [24]. The DEAR method's
steps are [24]:
• Determine the weight of each response for all experiments.
This value is calculated as the ratio of the value of each
response to the sum of all responses.
• Transfer the response data to the weight data by multiplying
the observed data by their respective weight.
• Divide the inversed data by the sum of all inversed data.
• The Multi Response Performance Index (MRPI) is
calculated by (1):
���� = �� ∗ �� + �
�� ∗ ��� (1)
The weights of the responses are calculated as:
�� =
�
∑�
(2)
�
�� =
�
���
∑
�
���
(3)
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III. EXPERIMENTS ON MILLING SCM400 STEEL
A. Experimental System
A CNC milling machine with the HAA5 serial was used to
carry out the experiments (Figure 1). The experimental sample
is SCM400 steel with length, width, and height of 80mm,
40mm, and 30mm respectively. The steel’s chemical
composition conducted with a spectrometer was: 0.43% C,
0.28% Si, 0.72% Mn, 1.05% Cr, 0.23% Mo, 0.024% P, and
0.026% S.
Fig. 1. The milling machine.
TABLE I. PARAMETERS OF THE CUTTING INSERTS
Parameter
Cutting insert
R390-
11T303M
-PM1025
R390-
11T305M-
PM1025
R390-
11T305M
-PM1025
Tool nose radius (mm) 0.3 0.5 0.8
Back edge length (mm) 0.8 0.9 1.2
Weight (kg) 0.0022 0.0026 0.003
Coating material TiN; TiCN; TiAlN
Cutting thickness (mm) 3.59
Main cutting angle (degree) 90
Maximum cutting depth (mm) 10
Shape style of cutting piece L
Edge width (mm) 6.8
Effective length of edge (mm) 10
Three types of cutting pieces were used in the experiment
namely the TiN-, TiCN-, and TiAlN-coated pieces. These
cutting pieces have high thermal resistance, and have been
proven to be very suitable for machining SCM440 steel. Each
cutting piece was used with 3 tool nose radius values of
0.3mm, 0.5mm, and 0.8mm. On the tool shank with 12mm
diameter, 2 symmetrical cutting pieces were installed. Each
cutting insert was used only once for the purpose of eliminating
the influence of tool wear on the output parameters of the
milling process. In other words, the number of cutting inserts
used in the experiment is twice the number of experiments to
be carried out. The milling process has been carried out
according to the method of symmetric milling, which means
that the milling width was equal to the diameter of the milling
cutter. Table I shows some basic parameters of the cutting
inserts used.
The surface roughness was measured with a Mitutoyo -
Japan SJ301 surface roughness tester of 0.8mm standard
length. The surface roughness of each experimental sample
was determined by averaging at least three consecutive
measurements. The MRR was calculated according to:
��� = �� ∙ �� ∙ �� (mm
3/min) (4)
where �� is the feed rate (mm/min), �� is the cutting depth
(mm), and �� is the cutting width (mm). In this case the cutting
width is just equal to the diameter of the milling cutter.
B. Experimental Design
The Taguchi method was applied to design the
experimental process in this study. Five parameters were
selected as the input parameters of the experimental process,.
Each selected parameter has three levels of values
(corresponding to three encoding degrees of 1, 2, and 3). The
values of the experimental parameters, selected within their
range as recommended by the cutting tool manufacturer [29],
are shown in Table II.
TABLE II. INPUT PARAMETERS
Parameter Symbol Unit
Value at level
1 2 3
Insert material IM - TiN TiCN TiAlN
Tool nose radius r mm 0.3 0.5 0.8
Cutting speed Vc m/min 80 120 160
Feed rate Vf mm/min 250 320 390
Depth of cut ap mm 0.20 0.30 0.40
The Taguchi method was used to design the experimental
matrix. When comparing the matrix design method by the
Taguchi method with some other matrix design methods, it can
be found that it requires a smaller number of experiments. For
example, with 5 input parameters, in which each parameter has
3 levels of values, the Taguchi method only needs 27
experiments while the Box-Behnken design needs at least 46
experiments and the Central Composite Design (CCD) method
needs a minimum of 43 experiments. An advantage that only
the Taguchi method obtains is that it allows designing the
experimental matrix with input parameters that are not
quantitative parameters. In this case the qualitative parameter is
just the cutting insert material type. So, the experimental matrix
was designed according to the Taguchi method with a total of
27 experiments, as shown in Table III.
IV. RESULTS AND DISCUSSION
The experiments in Table III are given in accordance with
the results shown in Table IV. Figure 2 shows the influence of
the input parameters on surface roughness. The comparison of
the difference at the lowest and highest levels, i.e. between
level 1 and level 3 of the parameter line graph (red broken line)
shows that the tool nose radius is the parameter that has the
greatest influence on surface roughness, followed by the
influence of the cutting insert material and the feed rate. The
difference of the line graph of cutting speed and cutting depth
is very small, showing that these two parameters have
negligible influence on surface roughness.
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TABLE III. EXPERIMENTAL MATRIX
No.
Code value Actual value
IM r Vc Vf ap IM r (mm) Vc (m/min) Vf (mm/min) ap (mm)
1 1 1 1 1 1 TiN 0.3 80 250 0.2
2 1 1 1 1 2 TiN 0.3 80 250 0.4
3 1 1 1 1 3 TiN 0.3 80 250 0.6
4 1 2 2 2 1 TiN 0.5 120 320 0.2
5 1 2 2 2 2 TiN 0.5 120 320 0.4
6 1 2 2 2 3 TiN 0.5 120 320 0.6
7 1 3 3 3 1 TiN 0.8 160 390 0.2
8 1 3 3 3 2 TiN 0.8 160 390 0.4
9 1 3 3 3 3 TiN 0.8 160 390 0.6
10 2 1 2 3 1 TiCN 0.3 120 390 0.2
11 2 1 2 3 2 TiCN 0.3 120 390 0.4
12 2 1 2 3 3 TiCN 0.3 120 390 0.6
13 2 2 3 1 1 TiCN 0.5 160 250 0.2
14 2 2 3 1 2 TiCN 0.5 160 250 0.4
15 2 2 3 1 3 TiCN 0.5 160 250 0.6
16 2 3 1 2 1 TiCN 0.8 80 320 0.2
17 2 3 1 2 2 TiCN 0.8 80 320 0.4
18 2 3 1 2 3 TiCN 0.8 80 320 0.6
19 3 1 3 2 1 TiAlN 0.3 160 320 0.2
20 3 1 3 2 2 TiAlN 0.3 160 320 0.4
21 3 1 3 2 3 TiAlN 0.3 160 320 0.6
22 3 2 1 3 1 TiAlN 0.5 80 390 0.2
23 3 2 1 3 2 TiAlN 0.5 80 390 0.4
24 3 2 1 3 3 TiAlN 0.5 80 390 0.6
25 3 3 2 1 1 TiAlN 0.8 120 250 0.2
26 3 3 2 1 2 TiAlN 0.8 120 250 0.4
27 3 3 2 1 3 TiAlN 0.8 120 250 0.6
TABLE IV. EXPERIMENTAL RESULTS
No IM r (mm) Vc (m/min) Vf (mm/min) ap (mm) Ra (µm) MRR (mm
3
/min)
1 TiN 0.3 80 250 0.2 0.771 600
2 TiN 0.3 80 250 0.4 1.457 1200
3 TiN 0.3 80 250 0.6 1.697 1800
4 TiN 0.5 120 320 0.2 1.538 768
5 TiN 0.5 120 320 0.4 0.905 1536
6 TiN 0.5 120 320 0.6 0.986 2304
7 TiN 0.8 160 390 0.2 2.205 936
8 TiN 0.8 160 390 0.4 1.582 1872
9 TiN 0.8 160 390 0.6 0.863 2808
10 TiCN 0.3 120 390 0.2 1.024 936
11 TiCN 0.3 120 390 0.4 1.112 1872
12 TiCN 0.3 120 390 0.6 0.801 2808
13 TiCN 0.5 160 250 0.2 1.076 600
14 TiCN 0.5 160 250 0.4 2.908 1200
15 TiCN 0.5 160 250 0.6 1.014 1800
16 TiCN 0.8 80 320 0.2 0.985 768
17 TiCN 0.8 80 320 0.4 3.019 1536
18 TiCN 0.8 80 320 0.6 1.549 2304
19 TiAlN 0.3 160 320 0.2 0.927 768
20 TiAlN 0.3 160 320 0.4 0.877 1536
21 TiAlN 0.3 160 320 0.6 0.892 2304
22 TiAlN 0.5 80 390 0.2 1.234 936
23 TiAlN 0.5 80 390 0.4 1.929 1872
24 TiAlN 0.5 80 390 0.6 0.824 2808
25 TiAlN 0.8 120 250 0.2 0.545 600
26 TiAlN 0.8 120 250 0.4 1.549 1200
27 TiAlN 0.8 120 250 0.6 1.601 1800
Because the cutting insert material, the tool nose radius and
the cutting speed do not exist in the MRR calculation formula
(4), they have no influence on MRR. Figure 3 shows the
influence of the feed rate and the cutting depth on MRR. The
difference between level 1 and level 3 of cutting depth line
graph is greater than the one of the feed rate line graph. This
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shows that the cutting depth has a greater influence on MRR
than the feed rate. Thus, we see that the influence of the input
parameters on surface roughness and MRR is different, even
adverse, e.g. the tool nose radius has a great influence on
surface roughness without any influence on MRR, the feed rate
and the cutting depth are two parameters needed for calculating
the MRR, but they do not significantly affect surface
roughness, etc. Thus, it can be said that being based only on the
two graphs of Figures 2 and 3, limits the determination of the
values of the input parameters to ensure minimum surface
roughness and maximum MRR. Table IV shows that surface
roughness has the smallest value in experiment #25, while
MRR has the maximum value in experiments #9, #12 and #24.
Thus, if we only observe Table IV, it is not possible to
determine the value of the input parameters to ensure both
minimum surface roughness and maximum MRR. The DEAR
method will be used to solve this problem in the next section.
Fig. 2. Main effect plot for surface roughness.
Fig. 3. Main effect plot for MRR.
V. SELECTION OF THE VALUE OF THE INPUT PARAMETERS
From the experimental data in Table IV, the weights of the
responses and the MRPI value at each experiment are
calculated according to (1) - (3), as shown in Table V. From the
data in Table V, the MRPI values of all input parameters at all
degrees were calculated. This value is calculated as the sum of
the MRPI value of each parameter at the respective degree, as
shown in Table VI. From the data in Table VI, it can be seen
that the cutting insert material (IM) has the smallest value of
MRPI corresponding to level 3, tool nose radius (r) has the
smallest value of MRPI corresponding to level 1, cutting speed
(��) corresponding to level 2, and feed rate (��) and cutting
depth (��) corresponding to level 3. Thus, the optimal value of
the parameters of the cutting insert material, the tool nose
radius, the cutting speed, the feed rate, and the cutting depth are
TiAlN, 0.3mm, 120m/min, 390mm/min, and 0.6mm
respectively [24]. The MRPI value with the maximum Max-
Min of 0.52972 is the cutting depth. Thus, the cutting depth is
the parameter that has the greatest influence, followed by the
tool nose radius, the cutting insert material, the cutting speed,
and the feed rate [24].
TABLE V. EXPERIMENTAL RESPONSE WEIGHT AND MRPI
No. ��� ���� MRPI
1 0.02168 0.07506 45.05364
2 0.04096 0.03753 45.09661
3 0.04771 0.02502 45.11789
4 0.04324 0.05864 45.10343
5 0.02544 0.02932 45.05996
6 0.02772 0.01955 45.06426
7 0.06199 0.04812 45.17362
8 0.04448 0.02406 45.10729
9 0.02426 0.01604 45.05787
10 0.02879 0.04812 45.06641
11 0.03126 0.02406 45.07169
12 0.02252 0.01604 45.05497
13 0.03025 0.07506 45.06948
14 0.08175 0.03753 45.27467
15 0.02851 0.02502 45.06584
16 0.02769 0.05864 45.06421
17 0.08487 0.02932 45.29317
18 0.04355 0.01955 45.10439
19 0.02606 0.05864 45.06109
20 0.02466 0.02932 45.05855
21 0.02508 0.01955 45.05930
22 0.03469 0.04812 45.07974
23 0.05423 0.02406 45.14154
24 0.01473 0.01604 45.04465
25 0.01532 0.07506 45.04528
26 0.04355 0.03753 45.10439
27 0.04501 0.02502 45.10899
TABLE VI. TOTAL MRPI
Parameter
Level Max - Min
1 2 3
IM 405.83457 406.06482 405.70353 0.36129
r 405.64016 405.90357 406.05920 0.41904
Vc 405.99584 405.67938 405.92771 0.31646
Vf 405.93679 405.86835 405.79778 0.13901
ap 405.71690 406.20787 405.67815 0.52972
VI. EXPERIMENTS WITH THE OPTIMAL VALUES OF THE
PARAMETERS
The optimal set of the 5 input parameters defined above
was used to experiment on the milling process with 3 steel
samples. The surface roughness of each experimental sample is
shown in Table VII. The MRR value at each experiment has
also been calculated and is included in this Table. The average
value of surface roughness in these cases is 0.724µm. If
compared with the surface roughness values in Table IV, it can
be seen that although 0.724µm is still larger than the value of
surface roughness at experiment #25, this value is very small
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when compared to the total of 27 experiments that were carried
out. For MRR, when calculated according to (4), in the three
test samples, the MRR is equal to 2808mm3/min, which is also
larger than the data in Table IV. From that, it can be seen that
when machining with the optimal values of the input
parameters, MRR reaches its maximum value and surface
roughness is also significantly improved. This result ensures
the reliability when using the optimal value of the input
parameters and proves the success in using the DEAR method
in this study.
TABLE VII. OUTPUT PARAMETERS WHEN EXPERIMENTING WITH THE OPTIMAL VALUES OF THE INPUT PARAMETERS
No.
Optimization value
Ra (µµµµm) MRR (mm
3
/min)
IM r (mm) Vc (m/min) Vf (mm/min) ap (mm)
1
TiAlN 0.3 120 390 0.6
0.726 2808
2 0.721 2808
3 0.725 2808
Mean 0.724 2808
VII. CONCLUSION
An experimental process of milling SCM 440 steel was
carried out in this study. Three types of cutting inserts were
used, coated with TiN, TiCN, and TiAlN. The tool nose radius,
the cutting speed, the feed rate, and the cutting depth were also
determined as input parameters of the experimental process.
The DEAR method was applied to determine the optimal value
of the input parameters. Some of the conclusions drawn from
this study are:
• The tool nose radius is the parameter that has the greatest
influence on surface roughness, followed by the influence
of the cutting insert material and the feed rate. The cutting
speed and the cutting depth have no significant influence on
surface roughness.
• Only the feed rate and the cutting depth have an influence
on MRR, and the influence of the cutting depth on MRR is
greater than the one of the feed rate.
• The parameter set that ensures simultaneously the two
objectives is: TiAlN cutting insert material, 0.3mm tool
nose radius, 120mm/min cutting speed, 390mm/min feed
rate, and 0.6mm cutting depth.
• DEAR method was not only successful in determining the
optimal values of the input parameters in this study as well
as in [25-28] but it is also quite promising to being
successful in the future when applied to determine the value
of input parameters to simultaneously ensure multi-criteria
optimization of the machining process.
ACKNOWLEDGMENT
This research is funded by University of Transport and
Communications (UTC) under the grant number T2021-CK-
003.
REFERENCES
J. P. Hung, K. D. Wu, W. Z. Lin, and W. C. Shih, "Analyzing the [1]
Dynamic Characteristics of Milling Tool Using Finite Element Method
and Receptance Coupling Method," Engineering, Technology & Applied
Science Research, vol. 9, no. 2, pp. 3918-3923, Apr. 2019,
https://doi.org/10.48084/etasr.2463.
D. D. Trung, "Influence of Cutting Parameters on Surface Roughness [2]
during Milling AISI 1045 Steel", Tribology in Industry, vol. 42, no. 4,
pp. 658-665, Dec. 2020, https://doi.org/10.24874/ti.969.09.20.11.
M. S. Phadke, Quality Engineering Using Robust Design, Printice Hall, [3]
1989.
S. K. Karna, R. V. Singh, and R. Sahai, "Application of Taguchi Method [4]
in Indian Industry", International Journal of Emerging Technology and
Advanced Engineering, vol. 2, no. 11, pp. 387–391, 2012.
M. Tamiloli and J. Venkatesan, "Investigation on Effect of Machining [5]
Parameters of End Milling on Surface Finish and Temperature Using
Taguchi Technique", Journal for Manufacturing Science and
Production, vol. 16, no. 4, pp. 255-261, Nov. 2016, https://doi.org/
10.1515/jmsp-2016-0030.
T. Kıvak, "Optimization of surface roughness and flank wear using the [6]
Taguchi method in milling of Hadfield steel with PVD and CVD coated
inserts", Measurement, vol. 20, pp. 19-28, Apr. 2014, https://doi.org/
10.1016/j.measurement.2013.12.017.
M. H. F. Al-Hazza, N. A. bt Ibrahim, E. T. Y. Adesta, A. A. Khan, and [7]
A. B. A. Sidek, "Surface Roughness Optimization Using Taguchi
Method of High Speed End Milling For Hardened Steel D2," IOP
Conference Series: Materials Science and Engineering, vol. 184, Mar.
Art. no. 012047, 2017, https://doi.org/10.1088/1757-899X/184/1/
012047.
I. Tlhabadira, I. A. Daniyan, R. Machaka, C. Machio, L. Masu, and L. R. [8]
VanStaden, "Modelling and optimization of surface roughness during
AISI P20 milling process using Taguchi method," The International
Journal of Advanced Manufacturing Technology, vol. 102, no. 9, pp.
3707–3718, Jun. 2019, https://doi.org/10.1007/s00170-019-03452-4.
J. E. Ribeiro, M. B. César, and H. Lopes, "Optimization of machining [9]
parameters to improve the surface quanlity," Procedia Structural
Integrity, vol. 5, pp. 355-362, Sep. 2017, https://doi.org/10.1016/
j.prostr.2017.07.182.
H. Öktem, T. Erzurumlu, and M. Çöl, "A study of the Taguchi [10]
optimization method for surface roughness in finish milling of mold
surfaces", The International Journal of Advanced Manufacturing
Technology, vol. 28, pp. 694–700, Oct. 2006, https://doi.org/10.1007/
s00170-004-2435-6.
A. A. Thakre, "Optimization of Milling Parameters for Minimizing [11]
Surface Roughness Using Taguchi‘s Approach", International Journal
of Emerging Technology and Advanced Engineering, vol. 3, no. 6, pp.
226-230, Jun. 2013.
S. Sakthivelu, T. Anandaraj, and M. Selwin, "Multi-Objective [12]
Optimization of Machining Conditions on Surface Roughness and MRR
during CNC End Milling of Aluminium Alloy 7075 Using Taguchi
Design of Experiments," Mechanics and Mechanical Engineering, vol.
21, no. 1, pp. 95–103, Jan. 2017.
S. Kumar, I. Saravanan, and L. Patnaik, "Optimization of surface [13]
roughness and material removal rate in milling of AISI 1005 carbon
steel using Taguchi approach," Materials Today: Proceedings, vol. 22,
no. 3, pp. 654-658, Feb. 2020, https://doi.org/10.1016/j.matpr.2019.
09.039.
L. Ulfiyah, B. Pramujati, and B. O. P. Soepangkat, "Optimization of [14]
Tool Wear, Surface Roughness and Material Removal Rate in the
Milling Process of Al 6061 using Taguchi and Weighted Principal
Component Analysis (WPCA)," Applied Mechanics and Materials, vol.
493, pp. 535-540, Jan. 2014, https://doi.org/10.4028/www.scientific.net/
AMM.493.535.
L. M. Maiyar, R. Ramanujam, K. Venkatesan, and J. Jerald, [15]
"Optimization of Machining Parameters for End Milling of Inconel 718
Engineering, Technology & Applied Science Research Vol. 11, No. 5, 2021, 7551-7557 7557
www.etasr.com Khanh & Cuong: Parameter Selection to Ensure Multi-Criteria Optimization of the Taguchi Method …
Super Alloy Using Taguchi Based Grey Relational Analysis," Procedia
Engineering, vol. 64, pp. 1276–1282, May. 2013, https://doi.org/
10.1016/j.proeng.2013.09.208.
V. C. Nguyen, T. D. Nguyen, and D. H. Tien, "Cutting Parameter [16]
Optimization in Finishing Milling of Ti-6Al-4V Titanium Alloy under
MQL Condition using TOPSIS and ANOVA Analysis," Engineering,
Technology & Applied Science Research, vol. 11, no. 11, pp. 6775-6780,
Feb. 2021, https://doi.org/10.48084/etasr.4015.
Q. Xu, J. Zhao, and X. Ai, "Cutting performance of tools made of [17]
different materials in the machining of 42CrMo4 high-strength steel: a
comparative study," The International Journal of Advanced
Manufacturing Technology, vol. 93, no. 5, pp. 2061–2069, Nov. 2017,
https://doi.org/10.1007/s00170-017-0666-6.
M. Elbah, M. A. Yallese, H. Aouici, T. Mabrouki, and J. Rigal, [18]
"Comparative assessment of wiper and conventional ceramic tools on
surface roughness in hard turning AISI 4140 steel," Measurement, vol.
46, no. 9, pp. 3041–3056, Nov. 2013, https://doi.org/10.1016/
j.measurement.2013.06.018.
L. Chen, Y. Du, P. H. Mayrhofer, S. Q. Wang, and J. Li, "The influence [19]
of age-hardening on turning and milling performance of Ti–Al–N coated
inserts," Surface and Coatings Technology, vol. 202, no. 21, pp. 5158–
5161, Jul. 2008, https://doi.org/10.1016/j.surfcoat.2008.05.036.
S. K. Khrais and Y. J. Lin, "Wear mechanisms and tool performance of [20]
TiAlN PVD coated inserts during machining of AISI 4140 steel," Wear,
vol. 262, no. 1–2, pp. 64–69, Jan. 2007, https://doi.org/10.1016/
j.wear.2006.03.052.
Q. Xu, J. Zhao, X. Ai, W. Huang, and G. Wang, "Optimum selection of [21]
tool materials for machining of high-strength steels based on fuzzy
comprehensive evaluation method," Proceedings of the Institution of
Mechanical Engineers, Part B: Journal of Engineering Manufacture,
vol. 233, no. 1, pp. 145–153, Jan. 2019, https://doi.org/10.1177/
0954405417713041.
M. A. Stipkovic, E. C. Bordinassi, A. D. Farias, and S. Delijaicov, [22]
"Surface Integrity Analysis in Machining of Hardened AISI 4140 Steel,"
Materials Research, vol. 20, no. 2, pp. 387-394, Apr. 2017,
https://doi.org/10.1590/1980-5373-MR-2016-0420.
W. Sales, M. Becker, S. B. Clovis, and J. Landre, "Tribological [23]
behaviour when face milling AISI 4140 steel with minimum quantity
fluid application," Industrial Lubrication and Tribology, vol. 61, no. 2,
pp. 84–90, Mar. 2009, https://doi.org/ 10.1108/00368790910940400.
H. Liao and Y. Chen, "Optimizing multi‐response problem in the [24]
Taguchi method by DEA based ranking method," International Journal
of Quality & Reliability Management, vol. 19, no. 7, pp. 825–837, Jan.
2002, https://doi.org/10.1108/02656710210434766.
S. Nguyen Hong and U. Vo Thi Nhu, "Multi-objective Optimization in [25]
Turning Operation of AISI 1055 Steel Using DEAR Method," Tribology
in Industry, vol. 43, no. 1, pp. 57–65, Mar. 2021, https://doi.org/
10.24874/ti.1006.11.20.01.
V. V. K. Lakshmi, K. V. Subbaiah, A. V. Kothapalli, and K. Suresh, [26]
"Parametric optimization while turning Ti-6Al-4V alloy in Mist-MQCL
(Green environment) using the DEAR method," Manufacturing Review,
vol. 7, 2020, Art. no. 38, https://doi.org/10.1051/mfreview/2020034.
D. D. Trung, N.-T. Nguyen, D. H. Tien, and H. L. Dang, "A research on [27]
multi-objective optimization of the grinding process using segmented
grinding wheel by Taguchi-Dear method," Eureka: Physics and
Engineering, vol. 1, pp. 67-77, Jan. 2021, https://doi.org/10.21303/2461-
4262.2021.001612.
V. V. Reddy and C. S. Reddy, "Multi Response Optimization of EDM of [28]
AA6082 Material using Taguchi-DEAR Method," International Journal
of Scientific & Engineering Research, vol. 7, no. 6, pp. 215-219, Jun.
2016.
"Sandvik Coromant - manufacturing tools & machining solutions," [29]
Sandvik Coromant. https://www.sandvik.coromant.com/en-gb/pages/
default.aspx (accessed Aug. 18, 2021).