Experimental studies to verify the effect of chip shrinkage coefficient on cutting forces and surface roughness in high speed milling of A6061 aluminum alloy
Journal of Science & Technology 119 (2017) 001-005
Experimental Studies to Verify the Effect of Chip Shrinkage Coefficient on
Cutting Forces and Surface Roughness in High Speed Milling of A6061
Aluminum Alloy
Pham Thi Hoa1,2, Mac Thi Bich1,2, Banh Tien Long1, Nguyen Duc Toan1*
1 Hanoi University of Science and Technology, No. 1, Dai Co Viet, Hai Ba Trung, Hanoi, Viet Nam
2 Department of Mechanical Engineering, Hungyen University of Technology and Education, Hungyen, Vietnam
Received: June 14, 2016; accepted: June 9, 2017
Abstract
This paper studied the relationship between the cutting force, surface roughness and chip shrinkage
coefficient through the affect of cutting parameters, i.e., cutting speed, feed rate and uncut chip thickness.
Experimental results of the chip shrinkage coefficient, cutting force and surface roughness at various cutting
parameter values for high-speed milling of A6061 aluminum alloy were presented in this study. The results
show that the cutting force and surface roughness can be derived based on the relationships with chip
shrinkage coefficient.
Keywords: High-speed milling, A6061 aluminum alloy, Chip shrinkage coefficient, Surface roughness,
Cutting force.
1. Introduction1
High-speed milling (HSM) has become an
innovative technique, which keeps improving
progressively. It is particular important to model of
the HSM process in order to aid for prediction of the
cutting process variables. Consequently, modeling of
HSM process is essential for the design and
optimization of the cutting conditions.
ensure workpiece surface roughness at a desired
quality, properly cutting parameters should be
established. Several researchers studied the
relationships of surface roughness and cutting
parameters such as nose radius, clearance angle,
cutting speed, feed rate, depth of cut, rake angle [7].
Nowadays, research on surface roughness influenced
by cutting parameters is continuing for enhancing
product quality at low cost.
Cutting force in a milling process is one of the
most important issues for the selection of machining
parameters, such as feed rate and spindle speed [1].
Many researchers using both experiment and
simulation approaches for prediction of cutting
forces of HSM processes [2-4]. Compare to
experiment approach, simulation of a milling process
using Finite Element Method (FEM) show a
beneficial of providing more detail information of
cutting process variables, such as cutting forces, tool
stresses and temperatures [5]. Nowadays,
development of cutting force model for HSM is
increasing interests toward higher precise cutting
force estimation and applicable for different cutting
conditions.
Another concern in modeling the HSM process
is estimation of chip geometric parameters, such as
chip thickness, chip length, etc. Since accuracy of the
model for chip parameter estimation directly affects
the accuracy of the cutting force predictions, accurate
chip modeling is always desired in the estimation of
cutting forces, especially in micro milling [8]. Chip
shrinkage coefficient defined as the ratio of the uncut
chip length by the actual chip length, can be a
prominent parameter for modeling of a cutting
process. However, studies on the chip shrinkage
coefficient have been limited in literature.
This study investigate the effect of cutting
parameters, i.e., cutting speed, feed rate and uncut
chip thickness on the chip shrinkage coefficient,
cutting force, surface roughness. The relationship
between this factor are found the chip shrinkage
coefficient and cutting force, surface roughness and
cutting parameters are found through experimental
measurement for high-speed milling of A6061
aluminum alloy.
One of the major challenges of milling at high
speeds is that high-speed milling leads to the high
temperature and stress growing at the interfaces of
chip-tool or workpiece-tool resulting in unexpected
roughness of workpiece surface finish [6]. In order to
* Corresponding author: Tel: (+84) 988 693 047
Email: toan.nguyenduc@hust.edu.vn
1
Journal of Science & Technology 119 (2017) 001-005
2. Experimental setup
device.
2.1 Workpiece material
The workpiece used in this study is aluminum
alloy A6061, which has the hardness of 97HB. The
chemical composition of workpiece material is
represented in Table 1. Several workpieces used for
experiment are shown Figure 1. The workpiece
dimensions are 70x30x70 (mm).
Table 1. Chemical composition of the workpiece material
(%).
Si
0,4-0,8
Cr
Fe
0,3
Zn
Cu
0,05-0,3
Ti
Mn
0,10
Mg
0,8-1,2
Fig. 2. Surface roughness tester
Al
remaining
The Sartorius Volume Comparator (S224-1S)
scale is used to determine the weigh of the chip after
cutting. The scale parameters are as follows: capactiy
of 220 gr, readability of 0.1 mg.
0,05-0,30
0,25
0,15
The chip shrinkage coefficient (K) can be
calculated by following formula [11]:
1000.Q
K
(1)
.L.S.t
where Q is weight of the chip (gr), is material
density (g/cm3), l is chip length (mm), S and t are
feed rate (mm/rev) and uncut chip thickness (mm),
respectively.
3. Design of experiment
Fig .1. The workpieces used in experiments
2.2 Milling experiment
The effects of cutting speed, feed rate, uncut
chip thickness, on chip shrinkage coefficient, force
cutting, surface roughness are examined using a
three-factor/three-level full factorial design [10]. The
range of each factor is set at three different levels as
shown in Table 2. Figure 3 shows the experimental
set-up of the milling process.
All the experiments are performed on a HS
Super MC500
high-speed milling machine
maximum feed rate of 30 m/min, maximum spindle
speed of 30.000 rpm, travel distances of the
operating platform in the X, Y and Z directions of
500 mm, 400 mm and 300 mm, respectively. Dry
milling condition with carbide insert cutting tool
(APMTT1604PDTR TC300) and diameter of 40 mm
is used for milling.
2.3 Measurement equipment
The cutting forces are measured by force
measurement device Kisler, which is equipped with a
force sensor (Kisler 9257B). The maximum load
capacities of the device in X, Y and Z directions are
1500N, 1500N and 5000N, respectively. The
sensitivity of sensor in X, Y and Z directions are 7.39
pC/N 7.39 pC/N and 3.72 pC/N, respectively. The
measured data is collected by an acquisition system
using DASYlab 10.0 software.
The surface roughness of the machined
workpiece is measured by a surface roughness tester
(Mitutoyo SJ–400). The roughness values are in µm.
Figure 2 shows the surface roughness measuring
Fig. 3. Experimental set-up
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Journal of Science & Technology 119 (2017) 001-005
b3 b4
F bV b f t
(3)
(4)
2
Table 2. Cutting parameters for the experiment
1
Level
3
c3 c4
Ra c1V c f t
No. Parameter
Unit
m/min
mm/min
mm
Level 1 Level 2
2
V
where ai, bi, ci (i = 1...4) are the constants to be
determined. Using curve fitting tool in Minitab17,
those constants can be determined as shown in Table
4.
1
2
3
(cutting
speed)
f
1000
800
0,5
1130
1350
1,0
1256
1800
1,5
(feed rate)
t
Table 4. Fitted constants obtained by surface fitting
(uncut chip
thickness)
method
Table 3. Experimental results
i
1
2
3
4
-0,1402635
0,889866 0,0090714 0,0339467
a
Ra
(m)
V
f
F
(N)
0,092838
1264,012
-0,36893
0,047466
b
c
No
t (mm)
K
(m/ min)(m/min)
0,10138765
5820,781 -1,378843 0,04978739
1
2
1,294 135,71
1,304 126,98
1,322 120,88
1,370 111,73
1,144 146,36
1,102 128,42
1,137 139,71
1,133 117,38
1,236 125,46
1,236 124,82
1,236 126,72
0,64
0,39
0,60
0,31
0,53
0,48
0,55
0,35
0,44
0,44
0,44
1000
1256
1000
1256
1000
1256
1000
1256
800
800
0,5
0,5
Figures 4-6 show K, F, and Ra as a function of
the cutting parameters, i.e., V, f and t, respectively,
obtained using equations (2), (3) and (4) with the
constants in Table 4. Figure 4 shows that increasing
cutting speed leads to the increase of K. On the other
hand, K is decreased with increasing depth of cut.
This figure also shows that t has a great influence on
K, while the effect of f on K is minor. Besides,
cutting speed increases leading to the decrease in
contact area between the chip and the front of the
tool. Consequently, chip shrinkage coefficient is
increased [11]. In order to obtain the optimal cutting
parameter values for minimizing K, MAPLE
software is utilized based on NLPSolve command.
Optimal values for V, f and t are 1000 m/min, 800
mm/min, and 1.5 mm, respectively.
0,5
0,5
3
1800
1800
800
4
5
1,5
1,5
1,5
1,5
1
6
800
7
1800
1800
8
9
1130
1130
1350
1350
10
11
1
1130
1350
1
4. Results and discussions
4.1 Influences of V, f and t on the K, F and Ra
Table 3 shows the experiment results of K, F, Ra as a
function of V, f and t. Using curve-fitting tool, the
relationship between K, F, Ra dependence on V, f and
t is established. That relationship is described by the
following equation (2), (3),(4).
From Figure 5, increasing V, f or t all reduces F.
This is because at high-speed cutting, the generated
heat can soften the materials thus decreasing cutting
forces [11]. Using NLPSolve command, the optimal
parameters of V, f and t for the objective function of
minimizing F are also found equal to 1256 m/min,
1800 mm/min, and 0.5 mm, respectively.
a3 a4
K a1V a f t
(2)
2
Fig. 4. The relationship between K and cutting parameters V, t and f. a) Fixed V, b) Fixed f, c) Fixed t
3
Journal of Science & Technology 119 (2017) 001-005
Fig. 5. The relationship between F and cutting parameters V, t and f. a) Fixed V, b) Fixed f, c) Fixed
Fig. 6. The relationship between Ra and cutting parameters V, t and f. a) Fixed V, b) Fixed f, c) Fixed t
Fig. 7. The relationship between F and K
Fig. 8. The relationship between Ra and K
Figure 6 indicates that Ra increases with
increasing f and t but reduces with increasing V. This
is because under high-speed cutting, the built up edge
phenomenon would disappears leading the reduction
of surface roughness [11]. Similar to K and F, the
optimized values of V, f and t for minimizing Ra are
1256 m/min, 1800 mm/min and 0.5 mm, respectively.
f and t on the relationship between F and K as well as
Ra and K, the five-level full factorial design was
assigned for cutting speed (V) of 1000, 1064, 1128,
1192, 1256 m/min; feed rate (f) of 800, 1050, 1300,
1550, 1800 mm/min and uncut chip thickness (t) of
0.5, 0.75, 1.00, 1.25, 1.50 mm, respectively.
It is seen that minimum values of F and Ra are
117N and 0.403m, respectively, which are all
obtained at K = 1.312. When K is equal to 1.154,
maximum values of F and Ra are obtained equal to
146N and 0.64m, respectively.
4.2 The relationship between F and K, Ra and K
This section analyzes the relationship between F
and K as well as, Ra and K based on Eqs. (2-4). By
eliminating V, f and t from Eqs. (2-4), the relationship
between F and K, Ra and K are found. As shown in
Figures 7-8, In order to verify the effect of various V,
The figures 7 and 8 also summarize the affected
trend of cutting parameters i.e., cutting speed, feed
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Journal of Science & Technology 119 (2017) 001-005
prediction of cutting forces in end milling with
rate and uncut chip thickness on the cutting force (F)
and surface roughness (Ra) related with chip
shrinkage coefficient (K) as discussing in detail on
section 4.1. From those figures, the optimal cutting
parameters can be obtained by minimizing cutting
force (F), surface roughness (Ra) and chip shrinkage
coefficient (K). In order to minimize the F and Ra, the
maximum of cutting speed (V) and minimum of feed
rate (f) also uncut chip thickness (t) should be set.
However, the decreasing of uncut chip thickness (t)
will increase the chip shrinkage coefficient (K)
therefore (t) will be chosen based on the productivity
of manufacturing process.
application to cor-nering cuts, International Journal of
Machine Tool Design and Research 22 (1982) 7–22.
[3] H.J. Fu, R.E. DeVor, S.G. Kapoor, A mechanistic
model for prediction of the force system in face
milling oper-ation, ASME Journal of Engineering for
Industry 106 (1984) 81–88.
[4] W.P. Wang, Solid modelling for optimizing metal
removal of three-dimensional end milling, Journal of
Manufac-turing Systems 7 (1984) 57–66.
[5] T. O. Zel, T. Altan, Modeling of high speed
machining processes for predicted tool forces stresses
and temperatures using FEM simulations, in:
Proceedings of the CIRP International Workshop on
Modeling of Machining Oper-ations, Atlanta, GA,
(1998) 225–234.
5. Conclusions
This paper presents an experimental study on
relationship between cutting force, surface roughness
and chip shrinkage coefficient when high speed
milling of A6061 aluminum alloy. Some conclusions
are given as follows:
[6] Tugrul Ozel, Taylan Altan, Process simulation using
finite element method - prediction of cutting forces,
tool stresses and temperatures in high-speed flat end
milling, International Journal of Machine Tools &
Manufacture 40 (2000) 713–738.
1. The relationship between the cutting force, surface
roughness and chip shrinkage coefficient through
cutting parameters e.g., cutting speed, feed rate, uncut
chip thickness are explicitly described by
mathematical functions.
[7] Mehmet Alper, İlhan ASİLTÜRK, Effects of Cutting
Tool Parameters on Surface Roughness, International
Refereed Journal of Engineering and Science, 4(8)
(2015) 15-22.
[8] Ali Mamedo, Ismail Lazoglu, An evaluation of micro
milling chip thickness models for the process
mechanics, the International Journal of Advanced
Manufacturing Technology, (2015) 1-7
2. The optimal cutting parameters for chip shrinkage
coefficient, cutting force and surface roughness can
be found by maximizing the cutting speed (V) and
minimizing the feed rate (f), which are useful for
practical milling of A6061 aluminum alloy.
[9] Swan MS. Incorporation of a general strain-to-failure
fracture criterion into a stress-based elasticity model
through a time-to-failure softening mechanism. M.Sc.
Thesis in Mechanical Engineering University of Utah,
USA.(2012)
Acknowledgements: This research is funded by
Vietnam National Foundation for Science and
Technology Development (NAFOSTED) under grant
number “107.02-2016.01”.
[10] K. Venkata, M. Krishnam, G. R. Janardhana,
Optimization of Cutting Conditions for Surface
Roughness in CNC End Milling, 12( 3) (2011) 383–
391.
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