Scientific Research and Essays Vol. 5 (10), pp. xxx-xxx, 18 May, 2010
Available online at http://www.academicjournals.org/SRE
ISSN 1992-2248 © 2010 Academic Journals
Full Length Research Paper
Effect of cutting parameters on the surface roughness
of titanium alloys using end milling process
Khairi Yusuf1*, Y. Nukman1, T. M. Yusof1, S. Z. Dawal1, H. Qin Yang1, T.M.I. Mahlia2 and
K.F.Tamrin3
1
Department of Engineering Design and Manufacture, University of Malaya,
50603 Kuala Lumpur, Malaysia.
2
Department of Mechanical Engineering, University of Malaya,
50603 Kuala Lumpur, Malaysia
3
Wolfson School of Mechanical and Manufacturing Engineering, Loughborough University, United Kingdom.
Accepted 4 May, 2010
Titanium alloys have been widely used in industries, especially aerospace industries, due to their good
mechanical and chemical properties. However, machining of titanium alloys involves expensive tooling
cost at the expense of getting good surface roughness. This paper describes a comprehensive study of
end milling of titanium alloys. The study investigated the optimum parameters that could produce
significant good surface roughness whereby reducing tooling cost. It employed the Taguchi design
method to optimize the surface roughness quality in a Computer Numerical Control (CNC) end mills.
The control parameters were spindle speed, feed rate, depth of cut and type of end milling tool. On the
other hand, the noise parameters were coolant pressure and patterns of cut. Then, an orthogonal array
7
of L8 (2 ) and analysis of variance (ANOVA) were carried out to identify the significant factors affecting
the surface roughness. The best parameters were then chosen based on the signal-to-noise ratio (SNR).
The experimental results indicated that the most significant factors affecting the surface roughness of
Titanium alloy during end milling process were primarily the spindle speed of machine, secondly, the
type of end mills tool used, thirdly, the feed rate adopted and lastly, the depth of cut chosen.
Keyword: Titanium alloy, surface roughness, tool wear, ends milling, Taguchi method.
INTRODUCTION
Titanium and its alloys are used extensively in aerospace
because of their excellent combination of high specific
strength, which is maintained at elevated temperature,
their fracture resistant characteristics and their
exceptional resistance to corrosion. They are being used
increasingly in other industrial and commercial
application such as petroleum refinery, nuclear reactors,
∗
Corresponding author. E-mail: [email protected]. Tel:
+60-3-7967-4464. Fax: +60-3-7967-5330.
Abbreviations: CNC, Computer numerical control; Ra,
roughness average; Rms, root-mean-square; Rq, roughness;
Ry or Rmax, maximum peak-to-valley roughness; NPM, noise
performance measure, TPM, target performance measure;
SNR, signal noise ratio; HSS, high speed steel; WC, tungsten
carbide.
surgical implants and marine application (Henriques et
al., (2005)). They are also being used extensively for
aerospace industry, mainly in airframe construction,
where maximum ease of formability is desired (Kumar et
al., 2008). However, machining of titanium alloys involves
expensive tooling cost at the expense of getting good
surface roughness. The machining of titanium and its
alloys is generally cumbersome owing to several inherent
properties of the material. Titanium is very chemically
reactive and therefore, has a tendency to weld to the
cutting tool during machining thus, leading to premature
tool failure. Its low thermal conductivity increases the
temperature at the tool-work interface thus, affecting the
tool life adversely. Additionally, its high strength
maintained at elevated temperature further impairs the
machinability (Ezugwu and Wang, 1997).
Machining of titanium alloys at higher cutting speed will
cause rapid chipping at the cutting edge which leads to
optimization of the cutting conditions of end milling in
order to produce good surface roughness. By obtaining
the gain value, minimum tooling costs for titanium alloys
can be predicted.
MATERIALS AND METHODS
Figure 1. Titanium alloys surface features
observed using a 100× microscope objective.
catastrophic failure of the insert. A higher cutting speed
also result in rapid cratering and/or plastic deformation of
the cutting edge. The rapid tool failure and chipping at the
cutting edge has resulted in poor surface finish of the
machined surface. Since titanium alloys are generally
used for a component, which requires the greatest
reliability, therefore the surface integrity must be
maintained (Che-Haron and Jawaid, 2005). Several
researches have been conducted to find the way of
machining titanium alloys to get a good significant
surface roughness. Zoya and Krishnamurty (1998) used
a cubic boron nitride (CBN) tool in the machining of
titanium alloys. The machining performance was
assessed by monitoring process indicators such as the
cutting temperature and the cutting force. Ezugwu (2004)
investigated high speed machining of aero-engine alloys.
Titanium alloys were developed in order to satisfy the
need for a class of strong and lightweight materials for
aircraft engine and airframe manufacture. It was
concluded that machining of aero-engine alloys, including
titanium alloys; generate high temperatures at the cutting
edge which impair the performance of various cutting tool
materials. Che-Haron and Jawaid (2005) investigated the
effect of machining on surface integrity of titanium alloys.
The experiment was carried out under dry cutting
conditions. It was concluded that straight cemented
carbides are suitable for use in machining titanium alloy
64. Zhang et al. (2007) conducted the research of surface
roughness optimization in an end-milling operation using
the Taguchi design method. Unfortunately, it was not
applied to titanium alloys. Stressing on the Taguchi
method dominated the investigation. Kumar et al. (2008)
investigated the characteristics of titanium using
ultrasonic machining. Experiment has been conducted to
assess the effect of three factors-tool material. It was
concluded that the surface roughness of the machined
surface has been found to depend on grit size of the
slurry used. Ginta et al. (2009) developed a surface
roughness models in end milling titanium alloy using
uncoated tungsten carbide inserts. They suggested using
uncoated WC-Co inserts under dry conditions for titanium
alloys end-milling. The next work depicted in this paper is
an investigation of the cutting parameters effect of end
milling of titanium alloys. The end milling can produce the
flat surface and the rotation of cutter is perpendicular to
the work piece of titanium alloys. In this study, the
Taguchi method is used. The investigation included
Titanium alloys
This research used titanium alloys of Ti-6Al-4V which has alpha ( )
– beta ( ) as stabilizers. As a typical + - type alloy, it becomes
the most widely used alloys of titanium since it has good specific
strength and corrosion resistance (Gu YB et al., (2001)). However,
it generally contains impurity elements that affect its thermomechanical properties. The microstructure of this alloy is strongly
influenced by the processing history and heat treatment (Figure 1).
Alpha-beta alloys are heat treatable and most are weldable (Zao et
al., (1999)). Typical properties include:
- Strength level are medium to high
- High temperature creep strength is not as good as most alpha
alloys
- Cold forming may be limited but hot forming qualities are normally
good
- Many alloys can be super-plastically formed
Milling process
Titanium alloys which is widely used especially in aircraft industry
have special characteristics as mentioned above. The main limiting
factor in designing aircraft structures that involves dynamic loading
is the fatigue property of the materials which is related to the
surface quality. Therefore, the importance of surface integrity in
titanium alloys milling process should be in consideration.
The milling process used in this study is flat end milling process
that can produce a flat surface. The rotation of cutter is
perpendicular to the work piece of titanium alloys. The flat end
milling with the multiple flute end mills solid tool is used since the
center cutting end teeth alloys allows these end mills to drill into
work piece to start a machining operation (Gill et al., (2006)). The
advantage of this multiple flute is that it can produce better surface
roughness compared to the other two flutes and three flutes of end
mills.
Surface roughness evaluation
There are various simple surface roughness amplitude parameters
used in industry, such as roughness average (Ra), root-meansquare (rms) roughness (Rq), and maximum peak-to-valley
roughness (Ry or Rmax), etc. (Joseph and John, 2001). Since Ra
and Rq are the most widely used surface parameters in industry,
Ra was selected to express the surface roughness in this study.
The average roughness (Ra) is the area between the roughness
profile and its mean line, or the integral of the absolute value of the
roughness profile height over the evaluation length (Figure 2). The
Ra is specified by the following equation:
L
1
Ra =
Y ( x) dx
L0
( m)
(1)
Machining parameter
To improve the quality of surface roughness of titanium alloys and
Figure 2. Surface roughness profile (Chen and Yang, 2001).
processes with minimum cost and time constraints, the Taguchi
parameter design techniques are applied in design of experiment
(DOE). Minimum surface roughness average (Ra) was carried out
since the value represents better or improved surface roughness.
Therefore, a smaller-the-better method is implemented in this
experiment (Mori, (1990)).
The controllable parameters are selected because of their
potential effects on surface roughness performance in end milling
operations. The parameters are the cutting speed denoted as (A),
the feed rate denoted as (B), the depth of cut denoted as (C) and
the type of end mills tool denoted as (D).
By applying cutting speed, feed rate, depth of cut and type of end
mills tool as control factors and used them to measure responses
for surface roughness, signal-to-noise ratio can be calculated to
determine the optimum cutting condition (Lou MS et al., (1998)).
Since surface cutting speed is linearly correlated to feed rate, the
control variable of cutting speed was specified as spindle speed.
The spindle speed and feed rate is calculated as
to suitable four columns. Since only four controllable factors and
two levels are considered, the suitable of orthogonal array in this
research is L8 (27). The L8 (27) means eight trials or experiments
must be done and seven factors each has two different levels
(Table 2). The target performance measure (TPM) is essentially a
measure of the mean response. It is used to identify control factors
that largely affect the mean, and not the variability. These factors
are called target control factors. It can use target control factors to
adjust the mean response.
For noise performance measure (NPM) is essentially a measure
of the variation in response. It is used to identify control factors that
largely affect variation and not the mean. These factors are called
variability control factors. It can use variability control factors to
minimize variation. For NPM, signal noise ratio (SNR) is used as
usual. Then, the analysis of variance (ANOVA) is done by an own
created program using C language.
The calculation for TPM and NPM are:
TPM =
S=
1000.v
π .D
FR = N .F .S
y=
Y / n (4)
Smaller-the-better for NPM:
(2)
(3)
Where S = Spindle speed (rpm) D = Tool diameter (mm)
FR = Feedrate (mm/min) N = Number of flutes F = Feed (m/tooth)
Taguchi method and analysis of variant
The uncontrollable (Noise) factors are one of the important
attributes of Taguchi parameter design which is considered in the
analysis (Table 1). Coolant pressure (X) is one of the noise factors
to study. The purpose is to know whether the low or high coolant
pressure is the significant factor to affect the milling of titanium
alloys performance. In order to know whether the positive-xdirection or positive-y-direction of cut gives significant effects to
cutting operation, the pattern of cut (Y) is another noise factor used
in this study.
Among those seven control factors, only four control factors are
selected, therefore, each of the four control factors must be assign
= -10 log10 (
2
+
y 2) (5)
Where Y = Surface roughness n = Number of result
variance.
= Sample
After selection of the best four parameters, the predicted method is
used to predict the value and to check whether the experiment
value is closer to predicted value. Formulation of predicted value is:
Ypredicted =
y + (Aopt - y ) + (Bopt - y ) + (Copt - y )
(6)
After the optimal levels of all the control factors are identified, the
last step is conducting the confirmation run. The combination of the
optimal levels of all the factors should produce the optimal
magnitude of surface roughness (the smallest Ra). This conclusion
must be further supported through the confirmation runs. Ten trials
cut under the optimal parameter setup in the study for the purpose
of confirmation run, before checking the tool performance.
Comparison of the performance between tungsten carbide (WC)
and high speed steel (HSS) has formula as
Table 1. Parameters, codes and level values used in orthogonal array.
Parameter
Control factors
Spindle speed (rpm)
Feed rate (mm/min)
Depth of cut (mm)
Type of end mills tool
(Ø5 mm, 4-flute, flat end)
Noise factors
Coolant pressure (pipe)
Pattern of cut
Code
Level 1
Level 2
A
B
C
D
2546
815
0.4
Tungsten carbide
(WC)
2865
917
0.6
High speed steel
(HSS)
X
Low (1)
High (2)
Y
+x-direction
+y-direction
-
-
-
Response variable
Surface
roughness, Ra ( m)
Table 2. New L8 (27) orthogonal array effects.
Columns
Effects
New effects
1
A
A
2
B
B
3
AxB
-
2
MSD = (σ 2 + y )
Gain = 1 − 0.5
(7)
ηoptimun −ηbefore
3
(8)
EXPERIMENTAL SET UP
To improve the quality of surface roughness of titanium alloys and
processes with minimum cost and time constraints, the parameter
design techniques are applied in Design of experiment (DOE).
Figure 3 shows the vertical end milling operation. The 4-flute of end
mills tool with diameter 5 mm is chosen and mounted by solid
collect. Then Cincinnati Milacron computer numerical control (CNC)
vertical milling machine is set up. The work piece of titanium alloys
is clamped tightly against the solid jaws by soft plastic hammer to
withstand the cutting force and reduce the vibration. Then the
programming code by machine control unit (MCU) is written.
After completing the cutting operation, the air sprayer is used to
blow out the coolant oil and some wasted chips around the work
piece. The solid jaws and solid collets are opened to remove work
piece and end mills tool respectively. The optical microscope is
used to observe the feature of end mills tool and surface roughness
feature of work piece. The tool was not changed every time of
machining, but measurement of the tool wear was done every time
the cutting process finish. The tool wear is measured by optical
microscope equipped with measurement software and the surface
roughness is measured by perthometer. In order to get accurate
result, three measurements were taken and averaged as shown in
Figure 4. Measurement values collected was saved in Microsoft
excel file (Version 2007) for further analysis and discussion.
RESULT AND DISCUSSION
In this study, the measurement is done for surface
4
D
C
5
AxD
-
6
BxD
-
7
AxBxD
D
roughness and tool wear. The TPM and NPM are
calculated using equation (4) and equation (5) as shown
in Table 3.
The analysis of variance (ANOVA) is shown in Table 4
and Table 5. The most significant factors of Rho% are
factor A (spindle speed), factor D (type of end mills tool),
and factor B (feed rate). The least significant factor is
factor C (depth of cut). Spindle speed is largely affecting
the surface roughness quality and also largely affects the
tool life. Both tables show the good result, because the
Rho% values for each factor A, factor B, factor C and
factor D in TPM ANOVA and NPM ANOVA are almost
same. Therefore, both noise factors (coolant pressure
and pattern of cut) are not significantly affected by the
surface roughness quality and also the tool life. The
pooled error of Rho% for both TPM (20.079%) and NPM
(16.429%) are considered lower, which means the
running of this experiment is good enough. Figure 5 and
Figure 6 show the comparison between the factors A in
TPM and NPM response. Smaller values of surface
roughness and higher value of the signal noise ratio at A2
lead to choose the spindle speed at level 2 which is 2865
rpm. Since smaller values of surface roughness are
desired and higher signal noise ratio values indicate the
good performance the following factors are selected. That
are B2 (917 mm/min feed rate at level 2), C1 (0.4 mm
depth of cut at level 1) and D1 (tungsten carbide at level
1). It concluded that the best parameters are A2, B2, C1,
and D1. The best four parameters which are selected
are: A2, B2, C1, and D1. Four factors in TPM and NPM
are not conflicted, because best four factors selected
from TPM and NPM are same. A factor that affects both
the mean and variance must be used very carefully.
Figure 3. Procedure to setup the experiment.
Figure 4. Three spots for taking surface roughness measurements.
However, such a factor allows some flexibility in
balancing target requirements. From Table 6, it can be
seen that the predicted values for target performance
measure (TPM) as 1.475 m and noise performance
measure (NPM) as -3.432 dB are closer to experiment
values from Table 3. The experiment values are 1.470
m and -3.228 dB. It shows that the research is done
correctly. With this prediction, it can be concluded that
the machine produced the best surface roughness (Ra =
1.470 m) within the range of specified cutting conditions
and the best four parameters are located at trial 7 of
experiment (Table 3).
The optimal levels for the controllable factors were
spindle speed 2865 rpm, feed rate 917 mm/min, depth of
cut 0.4 mm and type of end mills tool is tungsten carbide
(WC). In term of the experimental results, since there was
no noise factors can significantly affect the end milling
performance, any level for noise factors can be chosen.
The noise factors in confirmation run are high coolant
pressure and cutting in positive-x-direction. Table 7
shows the results of the confirmation running. Compared
with the experimental results in Table 3, the mean
surface roughness of the 10 confirmation samples 1.441
µm (Table 7), which was very close to the smallest value
Table 3. Results of experiment orthogonal array L8 (27)
X
Y
No. exp.
1
2
3
4
5
6
7
8
A
1
1
1
1
2
2
2
2
B
1
1
2
2
1
1
2
2
C
1
2
1
2
1
2
1
2
1
1
N1 ( m)
1.867
2.029
1.961
1.713
1.662
1.758
1.470
1.704
D
1
2
2
1
2
1
1
2
1
2
N2 ( m)
1.855
2.022
1.956
1.703
1.644
1.767
1.445
1.709
2
1
N3 ( m)
1.849
2.062
1.974
1.714
1.665
1.732
1.442
1.708
2
2
N4 ( m)
1.852
2.020
1.978
1.704
1.656
1.755
1.443
1.708
Average
TPM ( m)
1.856
2.033
1.967
1.708
1.657
1.753
1.450
1.708
1.767
NPM (dB)
-5.370
-6.164
-5.878
-4.652
-4.386
-4.877
-3.228
-4.647
-4.900
N1, N2, N3, N4: Results of surface roughness
X: Coolant pressure Y: Pattern of cut
Table 4. Target performance measure (TPM) response.
Source
A
B
C
D
Error
Pooled Error
St
Mean
ST
Pool
Yes
Yes
-
Sq
0.497
0.109
0.037
0.179
0.137
0.174
0.958
99.865
100.823
DOF
1
1
1
1
27
28
31
1
32
Mq
0.497
0.109
0.037
0.179
0.005
0.006
0.031
–
–
F-ratio
80.116
17.487
–
28.784
–
1.000
–
–
–
Sq'
0.491
0.102
–
0.172
–
0.192
0.958
–
–
Rho%
51.245
10.679
–
17.996
–
20.079
100.000
–
–
Table 5. Noise performance measure (NPM) response.
Source
A
B
C
D
Error
Pooled Error
St
Mean
ST
Pool
Yes
-
Sq
3.034
0.716
0.273
1.087
0.853
0.853
5.964
192.109
198.072
DOF
1
1
1
1
27
27
31
1
32
1.442 µm of surface roughness in Table 3 and also the
predicted value (1.475 µm) in Table 6. The confirmation
run indicated that the selection of the optimal levels for all
the parameters produced the best surface roughness. In
order to study the performances of tungsten carbide to
reduce tooling cost, the confirmation running for high
speed steel (HSS) is created to compare the confirmation
running for tungsten carbide (WC). From the Table 3, it
Mq
3.034
0.716
0.273
1.087
0.032
0.032
0.192
–
–
F-ratio
96.005
22.656
0.273
34.390
–
1.000
–
–
–
Sq'
3.003
0.684
0.242
1.055
–
0.980
5.964
–
–
Rho%
50.348
11.477
4.052
17.695
–
16.429
100.000
–
–
can be seen that the best surface roughness for HSS is
located at trial 5. The best parameters at trial 5 are A2
(spindle speed, 2865 rpm), B1 (feed rate, 815 mm/min),
C1 (depth of cut, 0.4 mm) and D2 (high speed steel,
HSS). Noise factors are not significant factors, so just
select any level of noise factors. Ten trials of cut are
running and mean of three readings are collected in
Table 7. Comparison of the performance between WC
Figure 5. Target performance measure (TPM) response.
Figure 6. Noise performance measure (NPM) response.
Table 6. Predicted value.
Source
A
B
C
D
Predicted value
Use
A2
B2
C1
D1
TPM ( m)
1.642
1.708
1.733
1.692
1.475
NPM (dB)
-4.285
-4.601
-4.716
-4.532
-3.432
Table 7. Confirmation running for tungsten carbide (WC).
WC
Exp.
1
2
3
4
5
6
7
8
9
10
Surface roughness, Ra (µm)
2
3
1
1.416
1.436
1.458
1.434
1.443
1.504
1.445
1.441
1.471
1.388
1.438
1.451
1.472
1.478
1.408
1.451
1.378
1.428
1.351
1.453
1.438
1.457
1.470
1.432
1.458
1.454
1.433
1.432
1.442
1.467
Average
Average
1.437
1.460
1.452
1.426
1.453
1.419
1.414
1.453
1.448
1.447
1.441
Table 8. Confirmation running for WC and HSS.
Number
1
2
3
4
5
6
7
8
9
10
Mean
Variance,
Mean standard deviation, MSD
Signal noise ratio, (SNR)
Gaining
and HSS is shown in confirmation table (Table 8).
The gain in loss reduction is improved by using the
WC. The SNR of WC is higher than SNR of HSS;
therefore selection of the WC can produce the better
surface roughness and reducing the tooling cost of
24.5%. Since the WC has better physical and mechanical
properties than HSS, therefore the tool wear length for
HSS end mills tool should be longer than WC end mills
tool. The measurement of the length for both is done
under
optical
microscope
by
using
software
measurement. Figure 7 shows the length of the tool wear
of HSS and Figure 8 shows the length of the tool wear of
WC after end milling process. They can obviously be
compared that the length of tool wear of HSS is longer
than the length of tool wear of WC (the difference is
1210.19 m). Observation of the micro structure of
titanium alloys material, tungsten carbide (WC) and high
speed steel (HSS) reveal characteristics that have a
HSS
1.654
1.681
1.675
1.654
1.638
1.661
1.656
1.664
1.656
1.639
1.658
0.014
2.748
-4.390
-
WC
1.437
1.460
1.452
1.426
1.453
1.419
1.414
1.453
1.448
1.447
1.441
0.016
2.076
-3.173
24.5
Improve
0.217
0.220
0.223
0.228
0.185
0.242
0.242
0.211
0.207
0.192
0.217
0.002
0.672
1.217
-
Unit
µm
µm
µm
µm
µm
µm
µm
µm
µm
µm
µm
µm
(µm)²
dB
%
tremendous influence on their technological utility. Some
of the features that contribute to the strength of material
and tool, and virtually all of the features that initiate
mechanical failure are resolved by optical microscopy.
Thus, preparation of optical microscopy specimens, their
observation using optical microscopes, and interpretation
of photographs taken with optical microscopes
(micrographs) play a vital role to understand the origin of
material and tool properties.
Conclusion
The experimental results indicate the most significant
factors are spindle speed (A), second is type of end mills
tool (D), third is feed rate (B) and forth is depth of cut (C).
In addition, the two noise factors, coolant pressure (X)
and pattern of cut (Y), are not significantly affecting to
Figure 7. High speed steel (HSS) tool wear observed using a 100x microscope objective.
Figure 8. Tungsten carbide (WC) tool wear observed using a 100x microscope objective.
surface roughness quality and tool life. The spindle speed
at 2865 rpm (A2), feed rate at 815 mm/min (B2), depth of
cut 0.4 mm (C1) and Tungsten Carbide (D1) are
identified as optimal through Taguchi parameter design
were able to produce the best surface roughness in order
to reduce tooling cost until 24.5%.
Titanium alloys present a unique set of machining
problems. Many of those problems can be alleviated or
eliminated by adhering to the set of guidelines, that are,
performing shallow finish machining pass to remove the
damaged layer, using large volumes of recommended
cutting fluids, using abrasion and heat resistant cutting
tools like coated carbide tool, replacing cutting tools at
the first sign of wear.
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