Vol. 43, No. 1 (2021) 57-65, DOI: 10.24874/ti.1006.11.20.

01

Tribology in Industry

RESEARCH
www.tribology.rs

Multi-objective Optimization in Turning Operation

of AISI 1055 Steel Using DEAR Method

S. Nguyen Honga, U. Vo Thi Nhub,*

a Center for Mechanical Engineering, Hanoi University of Industry, Hanoi City, Vietnam.
b Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi City, Vietnam.

Keywords: ABSTRACT
Turning AISI 1055 steel
Multi-objective optimization This paper presents a study on multi-objective optimization of turning process
Surface roughness AISI 1055 steel. It designs 9 experiments (L9) for a Taguchi test series matrix.
Cutting force The four parameters of input include spindle speed, feed rate, depth of cut, tool
MRR nose radius. The AISI 1055 steel machining operation experiments are carried
Taguchi out based on the matrix created. They are performed on a conventional lathe.
DEAR The factors considered for evaluating the machining quality include surface
roughness, cutting force in X, Y, Z directions and material removal rate (MRR).
First, the research is carried out to identify the impact of the input parameters
* Corresponding author: on the output parameters. Analysis of experimental results show that spindle
speed significantly affects all three components of cutting force, but slightly
Vo Thi Nhu Uyen influences the surface roughness. Regarding feed rate, this is the parameter that
E-mail: vothinhuuyen@haui.edu.vn has a strong effect on surface roughness and cutting force Fx but not on the
cutting force Fy and Fz. Meanwhile, the depth of cut has a considerable
Received: 17 November 2020 influence on the force in the x and y directions but a limited impact on the
Revised: 15 December 2020 surface roughness and the force in the z direction. Similar to the cutting speed,
Accepted: 3 January 2021 the tool nose radius is noticeable to all three components of the cutting force
and negligible to surface roughness. The second aim of this study is to determine
the value of the cutting parameters to achieve the minimum of surface
roughness and cutting force and the maximum of MRR. The Data Envelopment
Analysis-based Ranking (Dear) method is applied to solve multi-objective
problems. The paper identified the optimum values of spindle speed, feed rate,
depth of cut and tool nose radius are 910 rev/min, 0.194 mm/rev, 0.2 mm and
0.2 mm, respectively.
© 2021 Published by Faculty of Engineering

1. INTRODUCTION Many studies are conducted for optimizing the

turning operation in order to improve the
Turning is the most common method of cutting efficiency of the machining process by different
machining. Turning workload accounts for 40% methods such as: using the hybrid algorithm of
of the total machining and the number of lathes artificial Bee Colony combined with response
makes up 25 - 35% of total machines in a cutting surface methodology (RSM) to define optimal
workshop [1]. values of cutting parameters to achieve minimum

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value of surface roughness [2]; applying the mm of depth of cut. Meanwhile, to earn the
hybrid whale optimization algorithm combined minimum of surface roughness of Copper, the
with the central composite design (CCD) matrix spindle speed, feed rate and depth of cut are
to determine the optimal value of the cutting respectively 80 rev/min, 0.1 mm/rev and 1.5 mm
parameters with the aim of increasing MRR and [12]. Surface roughness of S45C steel in turning
reducing surface roughness, cutting heat and operation will obtain the minimum value if the
cutting force [3]. cutting speed, feed rate and depth of cut are 60
m/min, 0.1 mm/rev and 0.4 mm, respectively
Besides, some research on turning process [13]. To have the minimum value of surface
optimization has been carried out using the roughness when turning Ti-6Al-4V Titanium
Taguchi method. When turning aluminum Alloy, the depth of cut, feed rate and cutting speed
workpieces, to reach the minimum of surface are respectively 0.6 mm, 125 m/min and 0.12
roughness, the optimal value of cutting speed, mm/rev [14]. To achieve the minimum of surface
feed rate and depth of cut are 35 m/min, 0.15 roughness when turning AISI 1020 steel, the
mm/rev and 1.25 mm respectively [4]. For spindle speed, feed rate and depth of cut are
Polyethylene materials, surface roughness will be respectively 630 rev/min, 0.05 mm/rev and 1.25
minimized when turning if the cutting speed, feed mm [15]. Regarding Aluminium-2014 Alloy,
rate, depth of cut and tool nose radius are 213.88 surface roughness will be able to reach the
m/min, 0.049 mm/rev, 2 mm and 0.8 mm smallest value when turning if the spindle speed,
respectively [5]. When dry turning 42CrMo4 depth of cut and feed rate are respectively 1700
material, surface roughness has minimum value rev/min, 0.4 mm and 35 mm/min [16]. For AISI
when the cutting speed, depth of cut and feed 409 steel turning, to have the minimum of surface
rate are 110 m/min, 2 mm and 0.214 mm/rev, roughness, the cutting speed, depth of cut and
respectively [6]. When turning EN8 steel, if the feed rate are respectively 400m/min, 2.0 mm and
spindle speed, feed rate and depth of cut are 303 0.2 mm/rev [17]. To earn the maximum of MRR
rev/min, 0.067 mm/rev and 0.2 mm, when turning thermoplastic polymer-delrin
respectively, surface roughness has the minimum 500AL materials in turning operation, the spindle
value [7]. For EN 354 steel, in order to obtain the speed, feed rate and depth of cut are respectively
minimum value of surface roughness, the feed 300 rev/min, 0.25 mm/rev and 0.14 mm [18].
rate, cutting speed and depth of cut during
turning operation are respectively 0.015 The Taguchi method is also combined with several
mm/rev, 222 m/min and 1.2 mm [8]. For AM other methods to optimize the turning process. A
alloys, to reach the minimum value of surface combination of Taguchi and Grey relational
roughness, the feed rate, cutting speed and depth analysis (GRA) method found that surface
of cut during turning process are respectively 0.1 roughness and MRR respectively obtain the
mm/rev, 115 m/min and 0.5 mm [9]. The minimum and maximum in turning process when
optimum values of cut depth, cutting speed and the parameters is optimized at 400 m/min of
feed rate when turning AISI 1045 steel are 0.5 cutting speed, 0.1 mm/rev of feed rate and 1.2 mm
mm, 200 m/min and 0.1 mm/rev, respectively. of cut depth [19]. Similarly, under this integrated
Upon machining in this condition, the surface approach, surface roughness is able to reach the
roughness has the smallest value [10]. For S45C minimum value in turning operation of EN-36 steel
steel turning, surface roughness has the when the spindle speed, feed rate and depth of cut
minimum value when the cutting speed, feed rate are respectively 598 rev/min, 0.15 mm/rev and 1.5
and depth of cut are 135 m/min, 0.08 mm/rev mm [20]. This combination was also used in
and 1.1 mm, respectively [11]. When turning another study to determine optimization level of
aluminum materials, surface roughness will be tool nose radius, tool rake angle, feed rate, cutting
able to achieve the minimum value if the spindle speed, cutting environment and depth of cut while
speed, feed rate and depth of cut are 160 rev/min, turning unidirectional glass fiber reinforced plastic
0.05 mm/rev and 1.5 mm, respectively. (UD-GFRP) composite. This study reveals that
Regarding Brass materials, the cutting when machining in the cooled cutting environment,
parameters for the minimum of surface surface roughness is minimum and MRR is
roughness are found to be 660 rev/min of the maximum if the tool nose radius, tool rake angle,
spindle speed, 0.1 mm/rev of feed rate and 1.0 feed rate, cutting speed and depth of cut are
respectively 0.4 mm, -60, 0.2 mm/rev, 159.66

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m/min, 1.4 mm [21]. Two other optimization to deal with multi-objective optimization
methods, TOPSIS and SAW, are used with the problems, it is necessary to combine the Taguchi
Taguchi to identify the optimization level of the with another method, such as GRA, TOPSIS, SAW,
cutting speed, feed rate and depth of cut while PSO as described in the introduction. The mixture
turning Ti-6Al-4V alloy under minimum quantity of the DEAR and Taguchi also overcome this
lubrication (MQL). The aim of this paper is to limitation of the Taguchi [24].
minimize surface roughness, flank wear, cutting
force and cutting temperature. The results The purpose of the experiment presented in this
indicated that the two methods TOPSIS and SAW study is to have the minimum values of surface
respond similarly, the optimal values of the cutting roughness (Ra) and the cutting force (Fx, Fy, Fz)
speed, feed rate and depth of cut are 80 m/min, 0.05 and the maximum of MRR. Thus, it is necessary to
mm/rev and 0.1 mm, respectively [22]. solve the problem of multi-objective
optimization. DEAR method is used in the
These studies show that the Taguchi method has research and performed in the following steps
been successfully applied to optimize the turning [25-27]:
process in many specified conditions. However,
the optimum values of the machining parameters - Determine the weights (w) for each response for
for each type of material are found different. The all experiments. Weight of response is the ratio of
combination of Taguchi method and others (GRA, a response at any trial to the summation of all
TOPSIS, SAW, PSO) has also been efficiently used responses.
for optimizing turning operations. Nevertheless,
there are likely no studies using the Taguchi - Transform the data of response into weighted
method blended with the DEAR method to data by multiplying the observed data with its
optimize the turning process. own weight.

The papers mentioned often choose surface - Divide the data as smaller the better with
roughness as the output. Some of them choose smaller the better.
MRR and/or cutting force. AISI 1055 steel is
common for the production of machined details - Treat this value as a multi response
in manufacturing engineering industry because performance index (MRPI).
of its good machinability and low cost. However,
there have been no studies on multi-objective MRPI in the research is defined by the following
optimization of turning this steel considering all formula:
five outputs, which include surface roughness,
three components of cutting force and MRR. 𝑀𝑅𝑃𝐼 = 𝑊𝑅𝑎 ∙ 𝑅𝑎 + 𝑊𝐹𝑥 ∙ 𝐹𝑥 + 𝑊𝐹𝑦 ∙ 𝐹𝑦 +
𝑊𝐹𝑧 ∙ 𝐹𝑧 + 𝑊𝑀𝑅𝑅 ∙ 𝑀𝑅𝑅 (1)
Hence, this study focuses on the Taguchi method
combined with the Dear method in order to Where the weights are defined as:
determine the optimal value of spindle speed,
𝑅𝑎
feed rate, depth of cut and tool nose radius while 𝑊𝑅𝑎= (2)
turning AISI 1055 steel to achieve the minimum ∑ 𝑅𝑎
surface roughness, minimum cutting force in 3 𝐹𝑥
directions and maximum MRR. Influence of input 𝑊𝐹𝑥 = (3)
parameters on output parameters is also ∑ 𝐹𝑥
analyzed in this study. 𝐹𝑦
𝑊𝐹𝑦 = (4)
∑ 𝐹𝑦
2. DEAR METHOD 𝐹𝑧
𝑊𝐹𝑧= (5)
∑ 𝐹𝑧
DEAR is the method used for multi-objective
optimization [23]. It is often combined with the 1
Taguchi method. The Taguchi method is only able 𝑀𝑅𝑅
𝑊𝑀𝑅𝑅= (6)
to solve the single-objective optimization 1
∑
problems based on the ratio S/N. Hence, in order 𝑀𝑅𝑅

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3. EXPERIMENT OF TURNING OPERATION 3.2 Lathe and cutting tools

3.1 Material The cutting tools used in the experiment are

titanium-coated from Lungaloy (Japan). Their tool
The material used in the experiment is AISI 1055 nose radii are 0.2mm, 0.4mm and 0.6mm. A FEL-
steel. This is a common material to manufacture 1440GMW conventional lathe from MAGNUM-CUT
machine details in the manufacturing (Taiwan) is used for experimental purposes (Fig. 2).
engineering industry such as shafts, gears, and
levers. The steel has high workability and its
price is reasonable. Table 1 shows its designation
according to several standards. Some properties
of the steel are shown in Table 2. Table 3
introduces chemical composition of the steel.
Test specimens have a length of 280 mm and
their diameter of 28 mm (Fig. 1).

Table 1. Designation of AISI 1055 steel according to

several standards
USA EU Germany Japan France England Italy
AISI EN DIN JIS AFNOR BS UNI
1055 1.0535 C55 S55C C54 En9 1C55
Spain China Sweden Poland Czechia Russia Inter Fig. 2. The conventional lathe
UNE GB SS PN CSN GOST ISO
C55k 55 1655 55 12060 50 C55 3.3 Experiment design

Table 2. Some properties of AISI 1055 steel The four parameters chosen are the input of the
experiment, including spindle speed, feed rate,
Youngs Poisson´s Shear Density
module ratio module (kg/m3)
depth of cut, tool nose radius. Three levels of each
(GPa) (GPa) parameter were considered as shown in Table 4.
210 0.3 80 7800 The 9 experiments (L9) were designed based on
Average Specific heat Thermal Electrical the Taguchi orthogonal array, shown in Table 5.
CTE 20- capacity 50/ conductivity resistivityA
300°C 100°C Ambient mbient Table 4. Levels of parameters
(µm/m°K) (J/kg°K) temperature temperature Levels
(W/m°K) (µΩm) Parameter Symbols Unit
1 2 3
12 460 - 480 40 - 45 0.20 - 0.25 Spindle speed n rev/min 460 650 910
Feed rate f mm/rev 0.08 0.194 0.302
Table 3. Chemical composition of AISI 1055 steel Depth of cut t mm 0.20 0.35 0.50
Element C Si Mn Cr Al Cu Tool nose radius r mm 0.2 0.4 0.6
% 0.55 0.25 0.7 0.1 0.02 0.1
Table 5. L9 orthogonal array with input parameters
Code value Actual value
Trial
No. n f t r n f t r
(rev/min) (mm/rev) (mm) (mm)
1 1 1 1 1 460 0.08 0.20 0.2
2 1 2 2 2 460 0,194 0.35 0.4
3 1 3 3 3 460 0.302 0.50 0.6
4 2 1 2 3 650 0.08 0.35 0.6
5 2 2 3 1 650 0.194 0.50 0.2
6 2 3 1 2 650 0.302 0.20 0.4
7 3 1 3 2 910 0.08 0.50 0.4
8 3 2 1 3 910 0.194 0.20 0.6
9 3 3 2 1 910 0,302 0.35 0.2
Fig. 1. Test specimens

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3.4 Measuring instrument The cutting force measuring device used in the study
is a three-component dynamometer from KISTLER
3.4.1 Surface roughness measuring instrument (Switzerland). Its model number is 9129AA. The
device is capable of measuring up to 5kN for
Surface roughness measuring machine SJ-301
component forces in the X and Y directions and 30kN
from Mitutoyo (Japan) was used to measure the
for component force in the Z direction. The
roughness of the test components (Fig. 3). Its
dynamometer is fixed on carriage (Fig. 4). The data-
order number, detector number and stylus tip
processing devices were connected to the computer
radius are 178-953-2, 178-390 and 5 µm,
and they processed the results of the measurement
respectively. Each sample had a standard length
of the component forces by the dynamometer. The
of 0.8 mm and was measured at least 3 times. The
value of the forces at each experiment is the average
roughness value of each experiment is the
during the machining operation.
average of the successive measurements.
3.4.3 Determination of MRR

Material removal rate is defined as:

1
𝑀𝑅𝑅 = ∙𝑛∙𝜋∙𝑑∙𝑓∙𝑡 (mm3/s) (7)
60
Where:
n is spindle speed (rev/min).
d is diameter of workpiece (mm).
f is feed rate (mm/rev).
t is depth of cut (mm).
Fig. 3. Surface roughness tester
3.5 Result and discussion
3.4.2 Force measuring instrument
Experiment results are presented in Table 6.
Pareto chart of the influence of input parameters
on output parameters is shown in Figures 5 to 8.
Table 6. Result of the experiments
Trial Fx Fy Fz MRR
Ra (µm)
No. (N) (N) (N) (mm3/s)
1 1,344 94,749 218,941 94,964 10,790
2 0,968 184,704 307,095 85,576 45,791
3 1,030 626,367 1286.969 275,913 101,834
4 1,795 243,559 447,649 115,240 26,683
5 1,070 169,184 255,388 69,449 92,436
6 1,029 194,803 282,244 79,465 57,558
7 0,994 212,316 400,216 83,109 53,365
8 1,166 236,584 410,505 106,411 51,764
a) Dynamometer
9 1,080 138,854 143,253 72.981 141,018

b) The data-processing devices and computer

Fig. 5. Pareto chart of the influence of the input
Fig. 4. Cutting force measuring instrument parameters on Ra

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- All four input parameters have a significant

impact on the X-direction component force and
the degree of influence decreases in order of tool
nose radius, depth of cut, feed rate and spindle
speed.

- Regarding component force Fy, the tool nose

radius is the most influential, followed by depth
of cut, spindle speed and feed rate is negligible
to it.
Fig. 6. Pareto chart of the influence of the input
parameters on Fx - The tool nose radius and spindle speed both
considerably affect the component force in the z
direction, on which the impact of the tool tip
radius is greater than the spindle speed. The
other two parameters are not noticeable to Fz.
Since MRR is calculated based on (7), it is clear
that all parameters of the spindle speed, feed rate
and depth of cut influence on the MRR.
Increasing the value of these parameters
increases the MRR.

Analyses above show that the effects of the input

parameters on the output parameters are not the
Fig. 7. Pareto chart of the influence of the input same. For example, the tool nose radius is
parameters on Fy significant to all three component cutting forces
but unimportant to surface roughness, while the
feed rate has the greatest impact on surface
roughness. The spindle speed is a parameter that
negligibly affects surface roughness but greatly
influences all three of component cutting forces,
etc. The data in Table 6 reveals that the surface
roughness are minimum at trial No.2; the
component forces Fx, Fy, Fz have the smallest
value in trial No.1, No.9 and No.5 respectively; the
MRR reaches the highest value in trial No.9.
Therefore, in order to identify the desired cutting
parameters for achieving the “minimum” of
Fig. 8. Pareto chart of the influence of the input surface roughness and cutting forces and the
parameters on Fz “maximum” of MRR, it is necessary to solve the
multi-objective optimization problem, in which
Figures 5 to 8 indicate that: the spindle speed, the feed rate, the depth of the
cut and the tool nose radius are parameters to be
- The feed rate has a significant impact on surface defined.
roughness, while the other parameters are
negligible to it (because the line showing their 3.6 Multi-objective optimization of turning
influence on surface roughness does not exceed operation using Dear method
across the red limit line in the Pareto chart).
However, when considering in detail, effect of the Based on the results in Table 6, the weights of the
tool nose radius on surface roughness is greater responses and the MRPI values in each
than depth of cut, while spindle speed is the experiment were defined by formula from (1) to
parameter with the least influence. (6), as presented in Table 7.

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Table 7. Weights of the responses and the MRPI values in each experiment
Weight
Trial No. MRPI
Ra Fx Fy Fz MRR
1 0.12829 0.04509 0.05835 0.09660 0.39412 30.64574
2 0.09240 0.08791 0.08184 0.08705 0.09287 53.16138
3 0.09832 0.29811 0.34299 0.28065 0.04176 709.9278
4 0.17134 0.11592 0.11930 0.11722 0.15937 99.70661
5 0.10214 0.08052 0.06806 0.07064 0.04601 40.27302
6 0.09822 0.09271 0.07522 0.08083 0.07388 50.06805
7 0.09488 0.10105 0.10666 0.08454 0.07969 75.51397
8 0.11130 0.11260 0.10940 0.10824 0.08215 87.44938
9 0.10309 0.06609 0.03818 0.07423 0.03016 24.42696

MRPIs of the initial cutting parameters were Surface roughness and component cutting forces
determined. They are the summation of MRPI of are measured in each test, MRR is also defined in
each parameter at the respective levels, shown in each case. The result is presented in table 9.
Table 8.
Table 9. Output parameters with optimum values of
Table 8. Summation of MRPI at each level of cutting input parameters
parameters Trial Ra Fx Fy Fz MRR
Levels No. (µm) (N) (N) (N) (mm3/s)
Par. Max-Min
1 2 3 1 0.985 114.522 162.852 80.772 51.764
n 793.7349 190.0477 187.3903 606.3446 2 1.022 113.560 160.288 82.288 51.764
f 205.8663 180.8838 784.4228 603.5390 3 0.981 109.922 159.386 78.776 51.764
t 168.1632 177.2949 825.7148 657.5517 Mean 0.996 112.668 160.842 80.612 51.764
r 95.34572 178.7434 897.0838 801.7381
The comparison of data between table 9 and table
The data in Table 8 show that the MRPI of spindle 6 shows that the value of the output parameters
speed, feed rate, depth of cut and tool nose radius is significantly improved when the optimal of the
are minimum at level 3, 2, 1, 1 respectively. In the input parameters is applied.
DEAR method, the value of input parameter
corresponding to the minimum MRPI is the most
optimal [25, 28]. Thus, the optimal input 4. CONCLUSION
parameters include n (910 rev/min), f (0.194
In this study, experiments of turning AISI 1055
mm/rev), t (0.2 mm) and r (0.2 mm). The greatest
steel with a titanium coated cutting tool are
“Max – Min” value of MRPI is 801.7381, which is
performed. In each test, surface roughness and
also the tool nose radius. Furthermore, the input
three component cutting forces are measured
parameters corresponding to the maximum of
and MRR is identified. The combination of the
“Max-Min” of MRPI have the greatest influence on
Taguchi and DEAR method determines the value
the efficiency of the entire turning process [25, 28].
of the input parameters for reaching the
Thus, when assessing the turning process by
“minimum” of surface roughness and component
considering surface roughness, cutting force in
cutting forces and the “maximum” of MRR. Some
three directions (X, Y, Z) and MRR, the tool nose
conclusions are drawn as follows:
radius is found to be the most influential parameter,
followed by depth of cut and spindle speed. The - The feed rate is the parameter that has a
feed rate affects the turning operation least. significant impact on the surface roughness,
while the spindle speed, depth of cut and tool
3.7 Experiments using the optimal cutting nose radius have negligible effects on it.
parameters
- All four of the input parameters are considerable
The above optimal values of the input parameters to Fx, in which the tool nose radius is the most
are used for the turning process (910 rev/min of n, influential, followed by the depth of cut and the feed
0.194 mm/rev of f , 0.2 mm of t and 0.2 mm of r ). rate. The least is the spindle speed.

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- The tool nose radius greatly impacts on Fy, [4] P.K. Sahu, N.K. Sahu, A. Dubey, Optimization of
followed by depth of cut and the spindle speed. cutting parameters by turning operation in lathe
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[10] K. Chomsamutr, S. Jongprasithporn, The Cutting
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Optimization and Validation with Taguchi
gratitude.
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