OPTIMIZATION OF THE FSW PROCESS FOR ALUMINUM ALLOYS AA5083
BY THE GREY-BASED TAGUCHI METHOD
T.P. Chen*,a,b, C.H. Chienb, W.B. Linc and Y.J. Chaod
Department of Electrical Engineering, Fortune Institute of Technology, Kaohsiung 83160, Taiwan.
b
Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung
80424, Taiwan.
c
Department of Mechanical Engineering, Chinese Military Academy, Kaohsiung 83059, Taiwan.
d
Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA.
a
ABSTRACT
Friction Stir Welding (FSW) can produce superior mechanical properties in the weld zone. The purpose of this paper is to find
the optimum operating conditions of FSW process for two plates of aluminum alloy AA5083 welded in butt joint. In the FSW
procedure, there were four major controllable four-level factors, i.e., the tool rotation speed, transverse speed (feed rate), tool
tilt angle with respect to the workpiece surface and pin tool length. The uncontrollable factors are the ultimate tensile strength
and elongation rate which can be converted to signal-to-noise ratios with the larger the better. In order to achieve the aim of
the multiple-response process of robustness, the grey-based Taguchi method is proposed. A grey relational grade obtained
from the grey relational analysis is used as the multiple performance characteristic. Analysis of variance (ANOVA) is the
statistic method used to interpret these experimental data assigned to the L16 orthogonal arrays. Taguchi technique with
ANOVA is used to find out the significant variables (factors) for the multiple performance characteristic.
Introduction
Friction Stir Welding (FSW) is a novel material joining technique invented [1] by the welding Institute (TWI) in 1991, which can
produce superior mechanical properties in the weld zone. It was initially applied to aluminum alloys. In recent years, FSW has
become one of the most important solid-state joining processes. The FSW may be gradually replaced conventional welding
methods applied to aluminum alloys materials. As compared to the conventional welding methods, FSW consumes
considerably less energy. No cover gas or flux is used, thereby making the process environmentally friendly. The joining does
not involve any use of filler metal and therefore any aluminum alloy can be joined without concern for the compatibility of
composition, which is an issue in fusion welding.
In FSW process [2], a non-consumable rotating tool with a specially designed pin and shoulder is inserted into the abutting
edges of sheets or plates to be joined and traversed along the line of joint. The heat is generated between the wear resistant
welding tool and the material of the workpieces. The heat causes the latter to soften without reaching the melting point and
allows traveling of the tool along the welding line. Comparing the moving velocity of the tool and the heat traveling time for
soften temperature, the optimal tool moving velocity was provided by [3]. The percentage of the generated heat from the tool
shoulder or the tool pin was investigated by [4]. The tool serves two primary functions: (a) heating of workpiece, and (b)
movement of material to produce the joint. The heat transfer process in the workpieces is one of the most important aspects in
the FSW study [3-6]. A good understanding of the thermomechanics in the workpieces can be helpful in evaluating the process
as well as the weld quality [7]. The study of the heat flow into FSW tools is also helpful in evaluating the weld quality [7-8].
Furthermore, the controllable parameters for FSW processing are not only the moving tool velocity [9] as mentioned above, but
also the rotation speed of the tool, length of the tool pin, tool tilt angle with respect to the workpiece surface [10], stirrer
geometry and so on.
The FSW process is a complex series of materials processing in the physical properties changed. It is not easily to be modeled
by simple mathematics, but can be studied by experiment [11] or by finite element method [12]. Taguchi technique for the
experimental data analysis is a common method using in conventional welding [13-17], but not in FSW. The grey-based
Taguchi technique for the experimental data analysis is a relative new method applied to conventional welding [18]. However,
the grey-based Taguchi method for the experimental data analysis is not available for any publications corresponding to the
FSW, to the best knowledge of the authors of this paper.
The objectives of this current paper are to study the effects of rotation speed of the tool, transverse speed (moving velocity of
the tool), tool tiled angle with respect to the workpiece surface and pin tool length on both the ultimate tensile strength and
elongation rate simultaneously. The four four-level controllable variables will be assigned to the L16 orthogonal arrays. The
values of uncontrollable variables will be converted to the signal-to-noise (S/N) ratio performance measures. Parameter design,
based on the Taguchi method, can optimize the performance characteristic through the setting of process parameters and can
reduce the sensitivity of the system performance to sources of variation. The multiple-response process of robustness, the
grey-based Taguchi method [18-19] is proposed. The ANOVA is the statistic method used to interpret these experimental data.
Experimental Procedures
The specimens used for the friction stir processing experiments were machined from AA5083 aluminum alloy plates, which
were bought from market, into 3.0 mm x 50 mm x 160 mm plates. Two plates of AA5083 aluminum alloys were friction stir
welded (FSW) in the butt configuration by using an adapted milling machine at Department of Mechanical Engineering,
Chinese Military Academy, Taiwan. The two plates were placed side to side and clamped firmly to prevent the abutting joint
faces from being forced apart. The FSW procedure was based on the TWI procedure described in the patent [1]. In the FSW
procedure, there were four mainly controllable four-level factors, i.e., the tool rotation speed, transverse speed (feed rate), tool
tilt angle with respect to the workpiece surface and pin tool length were provided latter in the next section. The welding
direction of aluminum alloy was along the line of the joint. The rotation of tool resulted in stirring and mixing of material around
the rotating pin and the translation of tool moved the stirred material from the front to the back of the pin and finished welding
process. The advanced side and retreating side of the welding sheet were defined according to the rotation of tool and the joint
line. The tilt of the tool towards trailing direction ensured that the shoulder of the tool held the stirred material by threaded pin
and moved material efficiently from the front to the back of the pin. The insertion depth of pin into the workpieces was
associated with the pin height (length). The tool shoulder contacting with the workpiece surface depends on the insertion depth
of pin, which resulted in generation of welds with inner channel, surface groove, excessive flash or local thinning of the welded
plates and so on.
Totally, sixteen FSW butt joints were produced. Each butt joint was cut to be five pieces of specimens for tensile test based on
ASTM standard. Initially, five specimens for each trial were the size 10x160mm sawed the vertical direction to the welding line
from each butt joint. Then each tensile specimen was milled to be the configuration and size as shown in Figure 1. The tensile
tests were carried out by Instron 8801 Universal Testing Machine, and taken their loading and elongation record of specimens.
Finally, the ultimate tensile strength and elongation rate can be calculated on the bases of their fracture loading and elongation
of specimens.
100
16
12
18
50
Welded
R8
Unit: mm
Figure 1. Configuration and size of the tensile specimens
Methods of analysis
Analysis of variance (ANOVA)
ANOVA with Taguchi technique [20-21] is the statistic method used to interpret experimental data. In this work, there are four
mainly controllable factors, i.e., four-level rotation speed (550/1100/1250/1800 rpm), Transverse speed (53/90/143/180
mm/min), tool tiled angle (1/2/3/4 degree) and pin tool length (2.5/2.7/2.9/3.1 mm), as shown in Table 1, are used for analysis
of variance. Their interactions are possible to be computed from resulted experimental data through analysis of variance. In
this paper, using Taguchi techniques, only 80 (16×5) experiments for L16 orthogonal arrays are needed for elongation rate (%)
and ultimate tensile strength (Mpa). By neglecting the values of the initial and the ending pieces, along welding direction, from
each five-piece trial corresponding to the same experimental condition, the resulted elongation rate and ultimate tensile
strength, are shown in Table 2. Total degree of freedom can be calculated as 47. The average values of ultimate tensile
strength (MPA) and elongation rate (%) with corresponding signal to noise ratios, i.e. SN1 and SN2, respectively, are calculated.
The desired characteristic of uncontrollable factors for the response can be measured by signal to noise ratio (SN). Based on
the use of Taguchi’s recommendation [20-21], signal to noise ratio for ultimate tensile strength and elongation rate is the larger
the better.
Table 1. Mainly controllable parameters and their levels
Symbol
Process parameter
unit
Level 1
A
Rotation speed
rpm
550
B
Transverse speed
mm/min
53
C
Tool tiled angle
degree
1
D
Pin tool length
mm
2.5
Level 2
1100
90
2
2.7
Table 2. The FSW process data of L16 orthogonal arrays
Process parameter levels
Ultimate tensile strength (MPA)
Trial no.
Ave.
S/N1
A
B
C
D
1
1
1
1
1
286.70
49.15
2
1
2
2
2
271.90
48.69
3
1
3
3
3
266.40
48.51
4
1
4
4
4
215.33
46.66
5
2
1
2
3
327.00
50.29
6
2
2
1
4
160.50
44.11
7
2
3
4
1
183.10
45.25
8
2
4
3
2
217.13
46.73
9
3
1
3
4
306.53
49.73
10
3
2
4
3
316.40
50.00
11
3
3
1
2
195.07
45.80
12
3
4
2
1
189.43
45.55
13
4
1
4
2
189.13
45.54
14
4
2
3
1
271.77
48.68
15
4
3
2
4
237.03
47.50
16
4
4
1
3
359.43
51.11
Level 3
1250
143
3
2.9
Level 4
1800
180
4
3.1
Elongation rate (%)
Ave.
S/N2
16.16
24.17
12.65
22.04
11.67
21.34
7.78
17.82
20.05
26.04
5.77
15.22
8.29
18.37
9.37
19.43
16.16
24.17
17.97
25.09
9.40
19.46
7.13
17.06
13.53
22.63
13.13
22.37
17.32
24.77
30.08
29.56
Grey relational analysis
In the grey relational analysis, a data preprocessing is first performed in order to normalize the raw data for analysis. In this
study, a linear normalization of the S/N ratio is performed in the range between zero and unity, which is also called the grey
relational generating [18-19]. The normalized S/N ratio
xij
for the
ith
performance characteristic in the
jth
experiment can
be expressed as:
xij =
η ij − min j η ij
max j η ij − min j η ij
(1)
In the Taguchi method for the larger the better, the S/N ratio is used to determine the deviation of the performance
characteristic from the desired value. The S/N ratio
observations
y ij
η ij for the ith performance characteristic in the jth
experiment for the m
in each trial can be expressed as:
η ij = −10 log10 (
1
∑ yij−2 )
m
(2)
Table 3 shows the normalized S/N ratio for ultimate tensile strength and elongation rate. Basically, the larger normalized S/N
ratio corresponds to the better performance and the best normalized S/N ratio is equal to unity. The grey relational coefficient
is calculated to express the relationship between the ideal (best) and actual normalized S/N ratio. The grey relational
coefficient
ξ ij =
ξ ij
for the
ith performance characteristic in the jth
min i min j xi0 − xij + ς max i max j xi0 − xij
xi0 − xij + ς max i max j xi0 − xij
experiment can be expressed as:
(3)
xi0 is the ideal normalized S/N ratio for the ith performance characteristic and ς the distinguishing coefficient which is
defined in the range 0 ≤ ς ≤ 1 . In this paper, the distinguishing coefficient is assumed to be 0.5, which is the most common
where
used in the literature. A weighting method is then used to integrate the grey relational coefficients of each experiment into the
grey relational grade. The overall evaluation of the multiple performance characteristics is based on the grey relational grade,
i.e.
m
γ j = ∑ wiξ ij
(4)
i =1
where
γj
is the grey relational grade for the
jth experiment, wi
the weighting factor for the
ith
performance characteristic,
and m the number of performance characteristics. In this paper, the weighting factors for ultimate tensile strength and
elongation rate are assumed to be 0.75 and 0.25, respectively. The grey relational grade is shown in Table 4 for the overall
performance characteristics from combination of ultimate tensile strength and elongation rate.
Once the optimal level of the FSW process parameters is selected, the final step is to predict and verify improvement of the
performance characteristic using the optimal level of FSW process parameters. The estimated grey relational grade
using the optimal level of FSW process parameters can be calculated as:
γˆ
[19]
q
γˆ = γ m + ∑ (γ i − γ m )
(5)
i =1
where
γm
= total mean of the grey relational grade,
γi
= mean of the grey relational grade at the optimal level, and q =
number of FSW process parameters that significantly affect the multiple performance characteristics.
Table 3. Data preprocessing of each performance characteristic
Trial no.
Ultimate tensile strength
Elongation rate
Ideal sequence
1
1
1
0.72
0.62
2
0.65
0.48
3
0.63
0.43
4
0.36
0.18
5
0.88
0.75
6
0.00
0.00
7
0.16
0.22
8
0.37
0.29
9
0.80
0.62
10
0.84
0.69
11
0.24
0.30
12
0.21
0.13
13
0.20
0.52
14
0.65
0.50
15
0.48
0.67
16
1.00
1.00
Results and discussions
In this work, there are four major controllable factors, i.e., four-level rotation speed (550/1100/1250/1800 rpm), transverse
speed (53/90/143/180 mm/min), tool tiled angle (1/2/3/4 degree) and pin tool length (2.5/2.7/2.9/3.1 mm), as shown in Table 1,
were chosen for analysis of variance. In Table 2, based on the Taguchi’s recommendation for the larger the better, signal to
noise ratios, SN1 and SN2 for ultimate tensile strength and elongation rate, respectively, were computed by the equation (2)
with their corresponding average values.
Usually, based on the mechanical property of materials considerations, ultimate tensile strength is more than elongation rate
concerned. Therefore, in this study, the weighting factors for ultimate tensile strength (UTS) and elongation rate (ELR) are
assumed to be 0.75 and 0.25, respectively. In practice, the weighting factor may depend on desired mechanical performance
of the products. In the Table 4, the grey relational grade is a single index for the overall performance characteristics from
combination of UTS and ELR. It has been shown that experiment 16 is the best multiple performance characteristics among 16
experiments because of the highest grey relational grade in the Table 4. In other words, the optimal FSW process for the best
multiple performance characteristics is, based on the experiment 16, the combination of control factors A4B4C1D3. The effect
of each FSW process parameter on the grey relational grade at different levels can be separated out because the
experimental design is orthogonal. In the Table 5, the optimal FSW process for the best multiple performance characteristics is
predicted to be the combination of control factors A4B1C1D3 which is the case excluding in the table of L16 orthogonal arrays.
The relative important FSW process factors are DBAC. Figure 2 shows the response graph of the grey relational grade, where
the larger grey relational grade, the better are the multiple performance characteristics. The effect at B1 and B4 has not much
different. The accuracy of the grey relational grade for optimal combination of the FSW process parameters with the
significantly effect multiple performance characteristics can be checked by the statistic method of ANOVA.
Table 4. Grey relational grade and its order of each performance characteristic
Trial no.
ultimate tensile strength ( ξ ij )
elongation rate ( ξ ij )
Weighting
Ideal sequence
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
0.75
1
0.64
0.59
0.57
0.44
0.81
0.33
0.37
0.44
0.72
0.76
0.40
0.39
0.39
0.59
0.49
1.00
0.25
1
0.57
0.49
0.47
0.38
0.67
0.33
0.39
0.41
0.57
0.62
0.42
0.36
0.51
0.50
0.60
1.00
Grey relational grade
Order
0.62
0.57
0.55
0.43
0.78
0.33
0.38
0.44
0.68
0.72
0.40
0.38
0.42
0.57
0.52
1.00
5
7
8
11
2
16
15
10
4
3
13
14
12
6
9
1
The ANOVA summary results of the grey relational grade, as shown in the Table 6, indicates that pin tool length, transverse
speed and rotation speed are the relatively significant FSW process parameters, respectively, for affecting the multiple
performance characteristics. This result agrees with result of the response table for the grey relational grade, as shown in
Table 5. Based on the previous discussions, the optimal FSW process for the best multiple performance characteristics is
predicted to be the case of rotation speed at level 4, transverse speed at level 1, tool tiled angle at level 1 and pin tool length
at level 3.
The final step is to predict and verify the optimal FSW process parameters combinations for the best multiple performance
characteristics. Standard FSW processing parameters is not available in the literature yet because FSW is a novel material
joining technique. Therefore, in the paper, the initial FSW process parameters are not able to provide. The objective of this
study becomes to obtain the optimal FSW process parameters combinations for the best multiple performance characteristics.
Based on the equation (5), the estimated grey relational grade using the optimal FSW parameters can then be obtained. Table
7 shows the results of the confirmation experiment using optimal FSW parameters from prediction and experiment. The
difference of grey relational grade between prediction and experiment is about 9% only, and prediction case is excluding the
effect of non-significant parameters. The fracture positions of the specimens with the combination of control factors A4B4C1D3
are all located at base material which is outside of the welded zone. This result was seen and verified during the test at the
case of experiment 16 but did not provide in Table 2. A4B4C1D3 is the experimental case of rotation speed at level 4,
transverse speed at level 4, tool tiled angle at level 1 and pin tool length at level 3. A4B1C1D3 is the estimative case of
rotation speed at level 4, transverse speed at level 1, tool tiled angle at level 1 and pin tool length at level 3. According to both
difference (as shown Table 7) and response (as shown in Table 5 or in Figure 2) of the grey relational grade, both cases has
not much different.
Table 5. Response table for the grey relational grade
Factors
A
B
C
D
1
0.54
0.62
0.59
0.49
2
0.48
0.55
0.56
0.46
3
0.55
0.46
0.56
0.76
4
0.63
0.56
0.49
0.49
Max-min
0.14
0.16
0.10
0.31
Rank
3
2
4
1
Levels
Total mean grey relational grade
0.55
G rey R elational G rade
0.8
0.6
A
B
C
D
0.4
0.2
0
A1 A2 A3 A4 B1 B2 B3 B4 C1 C2 C3 C4 D1 D2 D3 D4
Process Parameters Levels
Figure 2. Response graph of the grey relational grade
Source Sum of squares
A
0.0424
B
0.0536
C
0.0234
D
0.2450
ERROR
0.1156
Total
0.4800
Table 6. ANOVA summary of the grey relational grade
Degree of freedom
Mean square
F
3
0.0141
# 4.27
3
0.0179
# 5.39
3
0.0078
2.35
3
0.0817
# 24.65
35
0.0033
47
# At least 95% confidence.
Contribution (%)
6.76
9.08
2.79
48.95
32.42
100
Table 7. Results of welding performance using the optimal FSW process parameters
Prediction
Experiment
Level
A4B1C1D3
A4B4C1D3
Ultimate tensile strength (MPA)
359.43
Elongation rate (%)
30.08
Grey relational grade
0.91
1
Conclusions
The optimum operating conditions of FSW process have been obtained for two plates of aluminum alloy AA5083 welded in
butt joint. The optimal FSW process parameters combinations are rotation speed at 1900 rpm, transverse speed at 53 or 180
mm/min, tool tiled angle at 1 degree and pin tool length at 2.9 mm for the best multiple performance characteristics. The most
significant FSW process parameter is pin tool length for affecting the multiple performance characteristics. Tool tiled angle is
not a significant FSW process parameter for two plates of aluminum alloy AA5083 welded in butt joint.
Acknowledgments
The authors gratefully acknowledge the financial support from the National Science Council (grant no. 95-2221-E-268-004)
and the National Center for High Performance Computing of the Republic of China.
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