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International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 05, May 2019, pp. 69-74, Article ID: IJMET_10_05_008
Available online at
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
SURFACE ROUGHNESS OPTIMIZATION IN
BALL NOSE MILLING PROCESS OF C45 STEEL
USING TAGUCHI METHOD
Vu Nhu Nguyet
1
Faculty of Mechanical Engineering, Thai Nguyen University of Technology,
3/2 street, Tich Luong ward, Thai Nguyen Ci
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ity, Vietnam.
Do Duc Trung
Faculty of Mechanical Engineering, Hanoi University of Industry, No. 298,
Cau Dien Street, Bac Tu Liem district, Hanoi, Vietnam
Dinh Trong Hai
Faculty of Mechanical Engineering, Thai Nguyen University of Technology,
3/2 street, Tich Luong ward, Thai Nguyen City, Vietnam.
ABSTRACT
In this paper, Taguchi method with L16 was used to experimental research in
order to present the influence of cutting parameters and feed path on the surface
roughness of workpiece when machining C45 by ball nose mill. The experimental
result shows the influence of above parameters on surface roughness and the value of
cutting conditions and feed path to minimize the surface roughness.
Keywords: Milling, Ball Nose Mill, Surface roughness, C45 Steel, Taguchi Method.
Cite this Article: Vu Nhu Nguyet, Do Duc Trung and Dinh Trong Hai, Surface
Roughness Optimization In Ball Nose Milling Process of C45 Steel Using Taguchi
Method, International Journal of Mechanical Engineering and Technology 10(5),
2019, pp. 69-74.
1. INTRODUCTION
In mechanical engineering, milling is a common method that has high productivity. Milling
process by ball nose mill can be used in many cases of surface contour that has a high
accuracy and productivity. In those cases, surface roughness of workpiece has the significant
influence on productivity and accuracy of milling process. A research on the influence of
processing parameters on surface roughness proposed the determination of the reasonable
value (or optimum value) of them to get minimum surface roughness. Basim A. Khidhir and
Bashir Mohamed [1] proposed the influence of velocity speed, feed rate, depth of cut and nose
radius on surface roughness when machining nickel based hastelloy – 276 by response surface
Surface Roughness Optimization In Ball Nose Milling Process of C45 Steel Using Taguchi Method
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method, experimental matrix was Box-Behnken; Duong Xuan-Truong and Tran Minh-Duc
[2] presented the influence of velocity speed, feed rate and depth of cut on surface roughness
when machining Inconel 718 material by response surface method. In this work, experimental
matrix was central composite design. Study on the influence of velocity speed, feed rate,
depth of cut and machining time on surface roughness when milling Stainless Steel (316L)
was done by Ali Abbar Khleif and Mostafa Adel Abdullah [3]; Taguchi method was applied
to determine the influence of cutting speed, milling direction, insert number and cutting tool
material on surface roughness when machining Inconel 718 material [4]; Muataz Hazza Faizi
Al-Hazza et at. [5] proposed the influence of velocity speed, feed rate and depth of cut on
surface roughness when machining Hardened Steel D2 by Taguchi method; Taguchi method
was applied to determine the influence of type of coolant, cutting speed and feed rate on
surface roughness when dry machining in milling process of alloy special steel (20MNCR5)
EN1008:2008 [6]; Proposed the influence of velocity speed, feed rate and depth of cut on
surface roughness using ball nose mill by Taguchi method when machining titanium alloy Ti-
6Al-4V [7] and when machining EN 31 steel using solid carbide tool [8], and so on.
This paper presents the experimental research when machining C45 steel by ball nose
mill. Taguchi method with 16 points in experimental matrix (L16) was used to study the
influence of parameters on surface roughness of workpiece. The four input parameters include
velocity speed, feed rate, depth of cut and feed path. Finally, this work shows the optimized
value of above parameters for minimum value of surface roughness.
2. EXPERIMENTAL MILLING
2.1. Experimental system
The C45 hardened steel (40÷42HRC) was used, and its chemical composition is shown in
Table 1. The dimensions of the experimental sample are shown in Fig.1.
Table 1 Chemical composition of C45 steel
C(%) Si(%) Mn(%) P(%) S(%) Cr (%)
0.42 15 0.5 0.025 0.025 0.2
The P3202-XWM25 ball nose mill was used with the diameter 8, flutes 02, helix angle
15
0
, and rake angle -14
0
as shown in Fig. 2.
Milling experiments were conducted on Mazak VCS530C, as shown in Fig. 3. Mitutoyo -
S3000 surface roughness tester was used, as shown in Fig 4. Each component was measured
in three times.
a) Dimensions of experimental sample b) Experimental sample
Figure 1 Experimental Component
Vu Nhu Nguyet, Do Duc Trung and Dinh Trong Hai
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Figure 2 Ball nose mill
Figure 3 Experimental machine-tool
Figure 4 Mitutoyo - S3000 surface roughness tester
2.2. Design of experiment
The Taguchi method with 16 runs was used. Four parameters are velocity speed vc, feed rate
ft, depth of cut t and feed method Fm. Experimental matrix is shown in Table 2.
Surface Roughness Optimization In Ball Nose Milling Process of C45 Steel Using Taguchi Method
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Table 2 Experimental design matrix
Run v (m/min) f (mm/tooth) t (mm) Fm Ra (m)
1 80 0.1 0.16 zigzag 0.36
2 80 0.2 0.25 zig 0.33
3 80 0.3 0.35 zig with contour 0.36
4 80 0.4 0.45 Follow periphery 0.79
5 100 0.1 0.25 zig with contour 0.34
6 100 0.2 0.16 Follow periphery 0.47
7 100 0.3 0.45 zigzag 0.97
8 100 0.4 0.35 zig 0.67
9 150 0.1 0.35 Follow periphery 0.45
10 150 0.2 0.45 zig with contour 0.45
11 150 0.3 0.16 zig 0.24
12 150 0.4 0.25 zigzag 0.54
13 180 0.1 0.45 zig 0.27
14 180 0.2 0.35 zigzag 0.78
15 180 0.3 0.25 Follow periphery 0.77
16 180 0.4 0.16 zig with contour 0.16
3. RESULTS
The surface roughness is taken by the average values of three consecutive measurements, as
shown in Table 2. From results in Table 2, experimental data was analysed by Minitab 16.
The results are shown in Table 3, Fig.5 and Fig.6.
Table 3 Experimental data analysis
Run v (m/min) f (mm/tooth) t (mm) Fm Ra (m) MEAN
1 80 0.1 0.16 zigzag 0.36 0.3607
2 80 0.2 0.25 zig 0.33 0.3300
3 80 0.3 0.35 zig with contour 0.36 0.3643
4 80 0.4 0.45 Follow periphery 0.79 0.7870
5 100 0.1 0.25 zig with contour 0.34 0.3377
6 100 0.2 0.16 Follow periphery 0.47 0.4733
7 100 0.3 0.45 zigzag 0.97 0.9730
8 100 0.4 0.35 zig 0.67 0.6697
9 150 0.1 0.35 Follow periphery 0.45 0.4493
10 150 0.2 0.45 zig with contour 0.45 0.4457
11 150 0.3 0.16 zig 0.24 0.2400
12 150 0.4 0.25 zigzag 0.54 0.5433
13 180 0.1 0.45 zig 0.27 0.2667
14 180 0.2 0.35 zigzag 0.78 0.7773
15 180 0.3 0.25 Follow periphery 0.77 0.7653
16 180 0.4 0.16 zig with contour 0.16 0.1600
Vu Nhu Nguyet, Do Duc Trung and Dinh Trong Hai
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Figure 5 Influence of parameters on surface roughness
Figure 6 Main effect plot for S/N ratio
Fig.5. shows that the feed path has the biggest influence on surface roughness, the
following parameters are depth of cut, feed rate and velocity speed respectively.
Fig.6. shows that the optimized value of velocity speed, feed rate, depth of cut are
150(m/min), 0.3(mm/tooth), 0.45(mm) respectively, and feed path is zigzag. This is
optimization condition for minimum value of surface roughness.
4. CONCLUSION
This paper presents the study on the influence of cutting parameters and feed path on the
surface roughness when machining C45 steel by ball nose mill using Taguchi method. The
influence of investigated parameters on the surface roughness was proposed. Also, this work
is shown the value of above cutting parameter and feed path for minimizing the value of
surface roughness.
ACKNOWLEDGEMENTS
The work described in this paper was supported by Ha Noi university of Industry
(https://www.haui.edu.vn/vn) and Thai Nguyen university of Technology
(
Surface Roughness Optimization In Ball Nose Milling Process of C45 Steel Using Taguchi Method
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REFEREENCES
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parameters on surface roughness and tool wear when machining nickel based hastelloy -
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[2] Duong Xuan-Truong and Tran Minh-Duc, (2013), Effect of cutting condition on tool wear
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[3] Ali Abbar Khleif and Mostafa Adel Abdullah, (2016), Effect of Cutting Parameters on
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