International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 04, April 2019, pp. 379–388, Article ID: IJMET_10_04_038
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=4
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
Scopus Indexed
INFLUENCE OF DIFFERENT CUTTER HELIX
ANGLE AND CUTTING CONDITION ON
SURFACE ROUGHNESS DURING ENDMILLING OF C45 STEEL
Dung Hoang Tien
Hanoi University of Industry, Vietnam
Nhu -Tung Nguyen*
Hanoi University of Industry, Vietnam
Trung Do Duc
Hanoi University of Industry, Vietnam
*Corresponding Author
ABSTRACT
This experimental study investigated the effects of milling conditions on the
finished surface roughness. With four controllable factors-three levels (cutting
velocity, feedrate, radial depth of cut, and cutter helix angle), the nineteen
experiments were performed with performance measurements of surface roughness.
By ANOVA analysis, the effect of cutting conditions on the surface roughness were
analyzed and modeled. The most suitable regression of surface roughness was a
quadratic regression with the confidence level is more than 97%, and this model was
successfully verified by experimental results with very promising results. Besides, by
using ANOVA method, the optimization process of surface roughness was performed.
The optimum value of surface roughness is 0.2259 μm that was obtained at cutting
velocity of 143.4904 m/min, a feedrate of 0.01 mm/flute, at a radial depth of cut of 0.1
mm, and a cutter helix angle of 45o. The approach method of the present study can be
applied in industrial machining to improve the surface quality in finished face milling
the C45 Steel.
Key words: Surface roughness, ANOVA method, C45 Steel.
Cite this Article: Dung Hoang Tien, Nhu -Tung Nguyen and Trung Do Duc,
Influence of Different Cutter Helix Angle and Cutting Condition on Surface
Roughness During End-Milling of C45 Steel, International Journal of Mechanical
Engineering and Technology 10(4), 2019, pp. 379–388.
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379
editor@iaeme.com
Influence of Different Cutter Helix Angle and Cutting Condition on Surface Roughness During
End-Milling of C45 Steel
1. INTRODUCTION
In Industrial manufacturing, milling is one of the most important processes in manufacturing.
In milling processes, optimizing the cutting conditions is very important to predict the surface
quality, geometrical accuracy, etc. Following this research directions, the Taguchi method and
ANOVA analysis have been widely used in industrial engineering analysis. Moreover, the
Taguchi method employs a special design of orthogonal array through reducing the number of
experiments to investigate the effect of the entire machining parameters.
Recently, this method has been widely employed in several industrial fields, and research
work. Lin, Chen, Wang, Lee [1] and Lajis, Mohd Radzi, ANOVA analysis was used to
research the effect of main machining parameters such as machining polarity, peak current,
pulse duration, and so on, on the wire-cut electrical discharge machining (WEDM)
characteristics such as material removal rate, surface roughness [2].
The surface roughness and cutting force are important machining characteristics to
evaluating the productivity of machining processes. In milling processes, by using Taguchi
method and ANOVA analysis, the cutting forces and surface roughness could be investigated
based on a number of factors such as depth of cut, feedrate, cutting speed, cutting time,
workpiece hardness, etc. Several research works had been conducted in different conditions
and had also been applied for different workpieces and tool materials such as Kıvak [3],
Ozcelik, Bayramoglu [4], Turgut, Çinici, and Findik [5], Karakas, Acır, Übeyli, and Ögel [6],
and Jayakumar [7].
However, although there were already many studies on surface roughness, it seems that
the cutter helix angle had not been mentioned. In this study, the influence of cutting
conditions and cutter helix angle on the machining surface roughness was investigated. The
minimum value of surface roughness was determined with the optimization values of cutting
conditions and cutter helix angle.
2. EXPERIMENTAL METHOD
2.1. The experiment setup
2.1.1. Workpiece and tool
The workpiece material was C45 steel. The compositions of C45 are listed in Table 1 and the
The properties of the C45 were the following: hardness 160-220 HB, Young’s modulus =
190-210 GPa, Poisson’s ratio = 0.27-0.30, tensile strength = 569 MPa. The workpiece
dimensions are 70 mm × 70 mm × 40 mm.
Table 1 Chemical compositions of 45C
Min
Max
C
0.42
0.48
Composite (%)
Mn
Si
P
S
0.6 0.15
0.9 0.35 0.030 0.035
Fe
Balance
The three tools were chosen as follows. Cutter: Flat-end mill tool with cutter material is
Hard alloy KF440, number of flute Nf = 4, rake angle αr = 50, and the diameter was 8 mm.
The cutter helix angle of three tools: β1 = 150, β1 = 300, β1 = 450. The geometry of three tools
are described in Figure. 1.
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Figure 1. The three tools with different cutter helix angle
2.1.2. Machine Set-Up and Cutting force measurements
The experiments were performed at a five-axis vertical machining center (DMU 50 - 5 Axis
Milling) as described in Figure. 2. All experiments were peformed under dry machining
condition. The surface roughness (Ra) of the product was measured by MITUTOYO-Surftest
SJ-210 Portable Surface Roughness Tester (Japan) as shown in Figure. 3. The surface
roughness was measured parallel to the machined surface from three different points and
repeated three times following three repeated times of each cutting test. The average values of
the measurements were evaluated.
Figure 3. Setting of surface roughness
measurement
Figure 2. DMU 50 - 5 Axis Milling
machine
2.2. Experiment design
In this research, the cutting velocity (Vc), feed rate (ft), radial depth of cut (ar), and cutter helix
angle (β) were selected as control factors and their levels were expressed in the Table 2. The
experimental plan was performed with 19 experiments and detailed as in Table 3. Besides, the
response surface methodology (RSM) technique has been used to design of experiments and
analysis of experimental results. RSM is used to model and analysis the response variables
that are influence by several controllable input variables [8].
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Influence of Different Cutter Helix Angle and Cutting Condition on Surface Roughness During
End-Milling of C45 Steel
Table 2. Milling parameters and their levels
No.
1
2
3
4
Machining parameters
Cutting velocity [m/min]
Feed per flute [mm/flute]
Radial depth of cut [mm]
Cutter helix angle [Degree]
Level 1
60
0.01
0.1
15
Level 2
130
0.08
0.3
30
Level 3
200
0.15
0.5
45
Table 3. The experimental design and results
Run
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Machining parameters
Vc [m/min]
ft
ar
[mm/flute]
[mm]
60
0.01
0.1
200
0.01
0.1
60
0.15
0.1
200
0.15
0.1
60
0.01
0.5
200
0.01
0.5
60
0.15
0.5
200
0.15
0.5
60
0.01
0.1
200
0.01
0.1
60
0.15
0.1
200
0.15
0.1
60
0.01
0.5
200
0.01
0.5
60
0.15
0.5
200
0.15
0.5
130
0.08
0.3
130
0.08
0.3
130
0.08
0.3
β
[o]
15
15
15
15
15
15
15
15
45
45
45
45
45
45
45
45
30
30
30
Ra
[µm]
0.623
0.553
0.878
0.795
0.566
0.559
1.220
1.045
0.322
0.21
0.48
0.461
0.623
0.571
0.895
0.781
0.624
0.647
0.618
3. ANALYSIS AND EVALUATION OF EXPERIMENTAL RESULTS
3.1. Analysis of Variance (ANOVA) for surface roughness
The experimental results were investigated and listed in Table 3. In this study, the influence of
the cutting velocity, feed rate, radial depth of cut, and cutter helix angle on the surface roughness
was analyzed by ANOVA. This analysis was performed with 95% confidence level and 5%
significance level. This indicates that the obtained models are considered to be statistically
significant. The coefficient of determination (R2), that coefficient is defined as the ratio of the
explained variation to the total variation and is a measure of the fit degree. When R2
approaches to unity, it indicates a good correlation between the experimental and the
predicted values.
According to Table 4, the contributions of each factor on surface roughness were listed in
the last column. It is clear from the results of ANOVA that the most important factor affecting
on the surface roughness was feedrate (38.766%). The other factors affect differently on the
surface roughness. The second and third factors influencing the surface roughness were radial
depth of cut (22.78%) and cutter helix angle (21.809%). The fourth factor influencing on the
surface roughness was cutting velocity (2.669%).
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Table 4. Results of ANOVA for surface roughness
Number of obs: 27
Root MSE:
0.0209
R-squared:
Adj R-squared:
Source
Sum of
squares
Degree of
freedom
Mean
square
Model
Vc
ft
ar
β
Vc*ft
Vc*ar
V c* β
ft*ar
ft* β
ar* β
Error
Total
1.0007
0.0275
0.3994
0.2347
0.2247
0.0014
0.0003
0.0001
0.0320
0.0348
0.0458
0.0296
1.0303
11
2
1
1
1
1
1
1
1
1
1
7
18
0.0910
0.0138
0.3994
0.2347
0.2247
0.0014
0.0003
0.0001
0.0320
0.0348
0.0458
0.0042
0.0572
0.9881
0.9613
F-value
Prob > F
21.52
3.25
94.48
55.53
53.15
0.33
0.06
0.02
7.58
8.23
10.83
0.0000
0.0100
0.0025
0.0000
0.0001
0.0002
0.5822
0.8127
0.8880
0.0284
0.0240
Percent
contribution
[%]
2.669
38.766
22.780
21.809
0.136
0.029
0.009
3.106
3.378
4.445
2.873
100
3.2. Regression and Verification of surface roughness model
In this study, one dependent variable is surface roughness, whereas the independent variables
are the cutting velocity (Vc), feed rate (ft), radial depth of cut (ar), and cutter helix angle (β). The
surface roughness model was modeled by quadratic regression and exponent regression as
expressed in Eq. 1 and Eq. 2.
The exponential regression of surface roughness:
𝑅𝑎 = 9.317 ∗ 𝑉𝑐−0.119 ∗ 𝑓𝑡0.117 ∗ 𝑎𝑟0.252 ∗ 𝛽 −0.378
{
1
𝑅 2 = 82.49
𝑅2
= 77.49
The quadratic regression of surface roughness:
R 𝑎 = 0.77490 − 0.00406 ∗ V𝑐 + 2.87908 ∗ 𝑓𝑡 − 0.14795 ∗ 𝑎𝑟 − 0.00191𝑉𝑐 ∗ f𝑡
−0.00027 ∗ 𝑉𝑐 ∗ a𝑟 + 0.000002 ∗ V𝑐 ∗ β + 3.19643 ∗ 𝑓𝑡 ∗ 𝑎𝑟
−0.04440 ∗ f𝑡 ∗ β + 0.000014 ∗ V𝑐2 − 0.000167 ∗ β2
R2 = 97.13
R2
= 92.61
{
2
There is a very good relation between predicted values and test values. The R 2 values of
the equations obtained by quadratic regression model for surface roughness was found to be
97.13%. The comparison and verification results of surface roughness model were described
in Figure. 4. As seen from this figure, the predicted results of two models were very close to
the experimental results. However, the predicted results of quadratic model are closer to
experimental results than other one. So, the most suitable regression of surface roughness was
a Quadratic regression as given in Eq. 2. These results showed that the Quadratic regression
model was shown to be successfully investigated of surface roughness in milling processes of
C45 steel.
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Influence of Different Cutter Helix Angle and Cutting Condition on Surface Roughness During
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Verification of surface roughness
1.6
Ra (Measured)
Surface Roughness [µm]
1.4
Ra (Quadratic regression -Predicted)
1.2
Ra (Exponential regression-Predicted)
1
0.8
0.6
0.4
0.2
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Run. N
Figure 4. Measured and predicted results of surface roughness
3.3. Parametric Influence on Surface Roughness
The consequences of axial depth of cut and federate on surface roughness for three cutting
velocity as 60 m/min, 130 m/min, and 200 m/min, respectively. It is very clear that surface
roughness increase with increasing of feedrate. This trend can be explained that when feedrate
increases, that results increase in undeformed chip thickness, and undeformed chip thickness
is directly proportional to cutting forces. And then, when the cutting forces increase, the
stability and damping characteristics of machine-tool system will be affected, that make more
vibrations and ultimately affects the surface roughness. This result is similar the result of the
change in the surface roughness that are noted in the several works such as [9, 10]. The
surface roughness values exhibited increasing tendency with increasing of radial depth of cut
from 0.1 mm to about 0.5 mm.
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Figure 5. Effect of cutting conditions on surface roughness
The surface roughness decreases with increasing of cutting velocity from 60 m/min to
about 130 m/min, and with the cutting velocity increases from about 130m/min to 200m/min,
the tendency of surface roughness is also increasing as shown in Figure 5. So, in order to
improve the surface roughness in face milling process of C45 steel, the tendency of machining
conditions was proposed that the feedrate decreases, the radial depth decreases, and the
cutting velocity is about 130m/min.
3.4. Comparison of maximum surface roughness for different of cutter helix angle
Figure 6 and 7 illustrate that cutter (with helix angle 45o) generates the smallest values of
minimum and maximum surface roughness for different cutter in comparison with other two
tool. The maximum and minimum of surface roughness decreases with decreasing of cutter
helix angle. So, in order to improve the quality of machining surface, the bigger helix angle of
cutter is a good choice.
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Influence of Different Cutter Helix Angle and Cutting Condition on Surface Roughness During
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Maximum values of Surface roughness
Surface Roughness[µm]
1.4
1.2
Vc=80 [m/min]
Vc=130 [m/min]
Vc=200 [m/min]
1
0.8
0.6
0.4
0.2
0
45
30
15
Cutter helix angle [Degree]
Figure 6. Maximum surface roughness for different of cutter helix angle
Minimum values of Surface roughness
Surface Roughness[µm]
0.70
0.60
Vc=80 [m/min]
Vc=130 [m/min]
Vc=200 [m/min]
0.50
0.40
0.30
0.20
0.10
0.00
15
30
45
Cutter helix angle [Degree]
Figure 7. Minimum surface roughness for different of cutter helix angle
3.5. Estimation of optimum surface roughness by ANOVA method
The lowest value of surface roughness is very important for quality improvement of the
machining product and lowering production costs. In this study, the quadratic regression
model of surface roughness as presented by Eq. 2 that was used to find the optimized values
of surface roughness and machining parameters. Using ANOVA method MATLABTM
software, the optimized results of machining parameters were obtained as below.
x=
143.4904
0.0100
0.1000
45.0000
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fval =
0.2259
So by ANOVA method, the optimal parameters of milling process were determined as
below.
Cutting volecity:
Vc = 143.4904 m/min.
Feedrate:
ft = 0.0100 mm/flute.
Radial depth of cut: a = 0.1000 mm.
Cutter helix angle: β = 45o
And, the optimization value of surface roughness was: Ra = 0.2259 μm.
4. CONCLUSIONS
Depending on the analysis of experimental results, the conclusions of this study can be drawn
as follows.
1. The most important factor affecting on the surface roughness was feedrate (38.766%). The
second and third factors influencing the surface roughness were radial depth of cut (22.78%)
and cutter helix angle (21.809%). The fourth factor influencing on the surface roughness was
cutting velocity (2.669%).
2. The surface roughness decreases with decreasing of feedrate, with decreasing of radial
depth, and with the increasing of cutting velocity from 60 m/min to about 130 m/min. And,
the surface roughness increases with increasing of cutting velocity from about 130m/min to
200m/min.
3. The maximum and minimum of surface roughness decreases with decreasing of cutter helix
angle. The cutter (with helix angle 45o) generates the smallest values of minimum and
maximum surface roughness
4. ANOVA method can be used to find the optimal value of surface roughness. In this study,
the optimum value of surface roughness is 0.2259 μm that was obtained at cutting velocity of
143.4904 m/min, a feedrate of 0.01 mm/flute, at a radial depth of cut of 0.1 mm, and a cutter
helix angle of 45o
ACKNOWLEDGMENTS
The authors appreciate the generous assistance from the CNC Lab for the dynamometer in the
cutting force measurement experiments. Thanks also extend to the support from the Faculty of
Mechanical Engineering, Hanoi University of Industry (HaUI), Vietnam.
REFERENCES
[1]
Y.C. Lin, Y.F. Chen, D.A. Wang, H.S. Lee, “Optimization of machining parameters in
magnetic force assisted EDM based on Taguchi method,” Journal of Materials Processing
Technology, Vol. 209, No. 7, p.3374-3383, 2009.
[2]
Ponnuvel, S., and N. Senthilkumar. "A study on machinability evaluation of Al-Gr-B 4 C
MMC using response surface methodology-based desirability analysis and artificial neural
network technique." International Journal of Rapid Manufacturing 8.1-2 (2019): 95-122.
[3]
Y. Turgut, H. Çinici, I. Sahin, and T. Findik, “Study of cutting force and surface
roughness in milling of Al/Sic metal matrix composites,” Scientific Research and Essays
Vol. 6(10), pp. 2056-2062, 2011.
http://www.iaeme.com/IJMET/index.asp
387
editor@iaeme.com
Influence of Different Cutter Helix Angle and Cutting Condition on Surface Roughness During
End-Milling of C45 Steel
[4]
T. Kıvak, “Optimization of surface roughness and flank wear using the Taguchi method in
milling of Hadfield steel with PVD and CVD coated inserts,” Measurement 50: 19–28,
2014.
[5]
B. Ozcelik, M. Bayramoglu, “The statistical modeling of surface roughness in high-speed
flat end milling,” International Journal of Machine Tools & Manufacture 46: 1395–1402,
2006.
[6]
M.S. Karakas, A. Acır, M. Übeyli, B. Ögel, “Effect of cutting speed on tool performance
in milling of B4Cp reinforced aluminum metal matrix composites,” Journal of Materials
Processing Technology 178: 241–246, 2006.
[7]
K. Jayakumar, J. Mathew, M.A. Joseph, “An investigation of cutting force and tool–work
interface temperature in milling of Al–SiCp metal matrix composite,” Journal of
Engineering Manufacture 227: 362, 2013.
[8]
D.C. Montgomery, “Design and analysis of experiments,”. 5th ed. New York:
Wiley,2001.
[9]
Abbas, A. T., Pimenov, D. Y., Erdakov, I. N., Mikolajczyk, T., El Danaf, E. A., & Taha,
M. A. (2017). Minimization of turning time for high-strength steel with a given surface
roughness using the Edgeworth–Pareto optimization method. The International Journal of
Advanced Manufacturing Technology, 93(5-8), 2375-2392.
[10]
Abbas, A., Pimenov, D., Erdakov, I., Taha, M., El Rayes, M., & Soliman, M. (2018).
Artificial Intelligence Monitoring of Hardening Methods and Cutting Conditions and
Their Effects on Surface Roughness, Performance, and Finish Turning Costs of SolidState Recycled Aluminum Alloy 6061 Сhips. Metals, 8(6), 394.
http://www.iaeme.com/IJMET/index.asp
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