![nitial parts of tool in friction stir welding [12, 16]](https://www.researchgate.net/profile/Saeede-Ghorbanpour/publication/308756388/figure/fig1/AS:554480676884480@1509209903986/nitial-parts-of-tool-in-friction-stir-welding-12-16_Q320.jpg)
Content uploaded by Saeede Ghorbanpour
Author content
All content in this area was uploaded by Saeede Ghorbanpour on Oct 28, 2017
Content may be subject to copyright.
J. Cent. South Univ. (2016) 23: 2146−2155
DOI: 10.1007/s11771-016-3271-1
Application of artificial neural network to predict Vickers microhardness of
AA6061 friction stir welded sheets
Vahid Moosabeiki Dehabadi1, Saeede Ghorbanpour1, Ghasem Azimi2
1. Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran;
2. Educational Workshop Center, Isfahan University of Technology, Isfahan, Iran
© Central South University Press and Springer-Verlag Berlin Heidelberg 2016
Abstract: The application of friction stir welding (FSW) is growing owing to the omission of difficulties in traditional welding
processes. In the current investigation, artificial neural network (ANN) technique was employed to predict the microhardness of
AA6061 friction stir welded plates. Specimens were welded employing triangular and tapered cylindrical pins. The effects of thread
and conical shoulder of each pin profile on the microhardness of welded zone were studied using tow ANNs through the different
distances from weld centerline. It is observed that using conical shoulder tools enhances the quality of welded area. Besides, in both
pin profiles threaded pins and conical shoulders increase yield strength and ultimate tensile strength. Mean absolute percentage error
(MAPE) for train and test data sets did not exceed 5.4% and 7.48%, respectively. Considering the accurate results and acceptable
errors in the models’ responses, the ANN method can be used to economize material and time.
Key words: friction stir welding; artificial neural network; aluminum 6061 alloy; Vickers microhardness
1 Introduction
Aluminum and aluminum alloys have been used
widely due to the light weight and high specific strength
in comparison with other metals [1−2]. Among the
aluminum alloys, AA6061 is more prevalent in aerospace
and automobile industries due to the high specific
strength and corrosion resistance [1−3]. The high-tech
applications of aluminum and its alloys specify the need
of methods for welding these metals [4]. Conventional
methods of welding, like fusion welding, have
restrictions for a large number of metallic alloys which
are difficult-to-weld or non-weldable and may result in
crack and porosity in welded aluminum alloy plates
[5−7].
Therefore, other novel methods like friction stir
welding are developing as the solid state, hot shear
joining process to overcome the aforementioned
difficulties [6−9]. FSW in aluminum alloys reduces the
deformation and residual stresses and enhances the
mechanical properties of weld in comparison with
traditional methods [10−11]. In the FSW, a
non-consumable rotating tool moves along the surfaces
of the two plates which are firmed. Friction between the
shoulder of the tool and plates makes a large amount of
heat which causes severe plastic deformation in the
plates, and the movement of the tool in the welding
direction makes a flow of metal. This method of welding
is appropriate for metals which cannot be welded by
traditional methods particularly aluminum alloys [7,
12−13].
Various modeling methods including artificial
neural network (ANN) methods have been employed in
order to develop the applications of FSW and reduce the
costs of experiments. ANNs are mathematical methods
which are capable to model the physical processes and
predict the final properties. In Refs. [5, 12, 14−17],
ANNs were used to predict and determine the
mechanical properties in friction stir welding.
OKUYUCU et al [5] utilized a single hidden layer
feed-forward ANN to study the relationship between
rotational and weld speed, and mechanical properties in
Al plates. The features which they considered as outputs
comprise of tensile and yield strength, hardness of HAZ
and weld metal, and elongation. BUFFA et al [12] used
two ANNs to predict the microhardness and the
microstructure of Ti-6Al-4V titanium alloy which was
welded by FSW method. SHOJAEEFARD et al [14]
developed an artificial neural network to model the FSW
parameters in Aluminum alloys welding. In their work,
the effect of weld and rotational speed on the mechanical
properties such as hardness and ultimate tensile strength
was evaluated. SHOJAEEFARD et al [15] investigated
the influence of weld and rotational speed on peak
temperature, HAZ width, and welding force in AA5083
Received date: 2015−07−22; Accepted date: 2015−12−25
Corresponding author: Vahid Moosabeiki Dehabadi, Master; Tel: +98−21−88888800; Cell: +98−913−3599995; E-mail: v.moosabeiki@sina.kntu.ac.ir
J. Cent. South Univ. (2016) 23: 2146−2155
2147
aluminum alloy. GHETIYA et al [16] designed a back
propagation neural network to predict the mechanical
properties of friction stir welded aluminum alloy. Tool
shoulder diameter, welding and rotational speed and
axial force were the parameters that their effect was
investigated on the tensile strength of the weld.
Although the material flows and consequently, the
mechanical properties of weld in FSW are highly
affected by tool geometry [3, 5, 13, 18], it was discussed
in a few literature [16]. Current study aims to investigate
the effect of tool pin profile including tool tilt angle and
thread of pin on the microhardness of weld and plates
using ANN method. Preparing the required data for
training the neural networks as well as determining the
effects of aforementioned parameters on the final
microhardness of welded plates, AA6061 aluminum
alloys were welded and samples were produced using
two kinds of pin profiles including triangle and tapered
cylindrical shaped pins. The obtained data were
conducted to establish neural networks which were able
to predict the Vickers microhardness of friction stir
processed plates.
2 Materials and methods
2.1 Samples preparation
AA6061 aluminum alloy sheets with the dimensions
of 130 mm×100 mm×6 mm were welded using various
tool pin profiles. The chemical composition of the sheets
is given in the Table 1.
Table 1 Chemical composition of AA6061 aluminum alloy
sheets (mass fraction, %)
B Zn Cr Mg Mn Fe Cu Si Al
0.06 0.1 0.03 0.35 6.03 0.5 0.1 0.3 Remained
The tool profile is one of the most vital parameters
and affects the weld zone properties. Basically, it
comprises of a shoulder and a pin as shown in Fig. 1.
Tools which were used in the current study were made of
non-consumable high carbon steel (H13), and the
physical features of the utilized tool are summarized in
Tab l e 2.
In order to evaluate the effects of thread and tool tilt
angle on the microhardness of the welded aluminum
alloy sheets, two various shapes of pin including triangle
and tapered cylindrical with three different geometries of
tools for each shape were manufactured and considered
in the present research. Among three manufactured pins
for each profile, one pin was threaded and the shoulder
had an 8° tilt angle, while, two remaining threadless pins
had flat and inclination cavity shoulder, which are
illustrated in Figs. 2 and 3. In order to fabricate targeted
Fig. 1 Initial parts of tool in friction stir welding [12, 16]
Table 2 Features of FSW tool in this work
Tool
material
Initial
hardness (HRC)
Hardness during
welding (HRC)
Pin
length/mm
H13 Steel 60 56 5.5
tools, after obtaining adequate height and diameter, tools
were heat treated followed by grinding to achieve the
sufficient hardness. Furthermore, since the pin is more
involved in welding compared with the body, its
hardness had been enhanced by 15HRC in comparison
with the body (60HRC in the tip versus 45HRC in the
body of pin).
Over the present investigation, friction stir welding
was processed in the room temperature and without any
filler, gas and flux. Having reduction in contact between
tools’ shoulder and sheets, the angle between tools and
sheets was chosen 87° instead of vertical position.
Foresaid position increased the weld speed hence the
axial force decreased. The rotational and traverse speeds
were adjusted and considered constant at 1000 r/min and
28 mm/min, respectively. After welding process, the
appearance, microstructure and microhardness of the
welded samples were investigated. Using Macro-Etch
technique on the AA6061 aluminum alloy sheets, the
microstructure of welded samples were studied and any
probable defects were detected. First of all, samples were
polished using abrasive papers (numbers 80, 320, 600,
and 1200) which were coated with tungsten carbide.
Following, samples were etched using the Keller’s
reagent, and the optimum time of etching was
determined to be 5 s considering the results of
experimental tests. In addition, to evaluate the tensile
strength, yield strength, and fracture type, tensile tests
were used and friction stir welded sheets were prepared
based on the ASTM-E08M standard as test samples.
Tensile tests were carried out using Hounsfield testing
machine which was able to apply a maximum load of
5000 kg.
J. Cent. South Univ. (2016) 23: 2146−2155
2148
Fig. 2 Various profiles of tapered cylindrical tool: (a) Threadless pin, flat shoulder; (b) Threaded pin, conical shoulder; (c) Threadless
pin, conical shoulder
Fig. 3 Various profiles of triangle tool: (a) Threadless pin, flat shoulder; (b) Threaded pin, conical shoulder; (c) Threadless pin,
conical shoulder
J. Cent. South Univ. (2016) 23: 2146−2155
2149
Vickers microhardness tests were employed to
measure the hardness of welded samples. Therefore, the
amount of load in tests was considered to be 98.07 mN
and duration of load applied was 10 s (HV 0.01).
Through the microhardness measurement process, the
average of three spots was considered an associated
microhardness result and utilized in the artificial neural
network analyses.
2.2 Artificial neural network
Artificial neural network models are able to learn
from examples and recognize the patterns between input
and output data sets, and generalize the results for
variable cases [19−20]. In the present investigation,
MATLAB R2014a was utilized to model the effect of
tool geometry and thread on the Vickers microhardness
in various distances from welding centerline. Two
applicable kinds of pin profiles were considered and
some experimental tests were carried out in order to
gather data. 51 and 48 data sets were appropriate to be
used in the modeling for triangle and tapered cylindrical
shaped pin respectively. Through the proposed analyses,
tool tilt angle, thread, and distance from welding
centerline considered input parameters, Vickers
microhardness of welded plates was chosen as the output.
An appropriate neural network was allocated to each pin
profile (triangle and tapered cylindrical shaped) and each
one had three inputs versus one output. Moreover, the
data related to spots from one side of centerline were
considered for the values of microhardness in two sides
of weld center were approximately symmetric.
In the training procedure, four sets of the acquired
experimental data for each pin profile were selected
randomly, in order to test the trained network (named test
data sets). The remaining sets were used in the training
procedure including train, validation and test steps. Each
ANN estimated the Vickers microhardness using the
experimental data sets and initial weights and biases.
Subsequently, the predicted answers of the network were
compared with the actual results, and errors as well as
mean absolute percentage error (MAPE), and mean
absolute error (MAE) were calculated using Eqs. (1),
(2) and (3), respectively. Over the training procedure, the
weights were adjusted, and ANN continued its hardness
predictions until it reached reliable errors. Mean squared
Error (MSE) in validation step which is described by
Eq. (4) is another important factor to terminate the
training process.
100
i
ii
A
YA
E (1)
N
ii
ii
A
YA
N1
100
||
1
MAPE (2)
N
i
ii AY
N1
||
1
MAE (3)
N
i
ii YA
N1
2
)(
1
MSE (4)
where Ai and Yi referred to experimental and predicted
Vickers microhardness of the data set number i
respectively; and N indicated the number of all utilized
data in the training process. The aforesaid procedure was
done for each of neural networks separately.
A neural network with appropriate architecture
performs properly and the results could be reliable.
Hence, adequate hidden layers containing proper neurons
had to assign to the network. Consequently, for each pin
profile, neural networks including one, two and three
hidden layers were examined along with various
numbers of neurons between two and ten in the hidden
layers. Coefficient of determination, MSE and MAPE
were chosen as vital criteria to evaluate the suitable
networks. Other factors which had significant influences
on the accuracy of answers were learning and transfer
functions. To assign the most appropriate functions to the
ANNs, MSE, MAPE, and R2 were appraised when
learning functions chose Learngd and Learngdm. Besides,
different transfer functions including tansig, purelin and
logsig were used between input and hidden layers, as
well as hidden and output layers through the training
procedure. Tables 3 and 4 summarize some results for
ANNs were trained using various combinations of
hidden layers and neurons; and different transfer
functions were for triangle and tapered cylindrical pins,
respectively. Comparing the major factors for ANNs in
Tables 3 and 4 indicates that the ANNs containing one
and two hidden layers had the best performance for
triangle and tapered cylindrical tools, respectively.
Therefore, 3-8-1 and 3-7-3-1 structures, which are shown
in Fig. 4, were the best architectures for predicting
microhardness of AA6061 welded plates using triangle
and tapered cylindrical pins.
3 Results and discussion
In general, the weld appearance in friction stir
welding has a higher quality compared with the
traditional methods of welding. In samples which were
welded using threaded pins, the weld quality was
significantly better than the others.
In other words, generating heat, which comes from
the friction between the pin and sheets, and traversing
force in threaded tools are higher than unthreaded ones
[21−22], which can lead to a better weld quality. In
addition, samples which were welded using tools with
concave shoulders, showed better appearance in
comparison with flat shoulder tools as materials are
J. Cent. South Univ. (2016) 23: 2146−2155
2150
Table 3 Results of some networks trained by various transfer functions, learning functions and structures for triangle pin profile
No. Structure Transfer function Validation MSE MAPE/% R2
1 3-8-1 Tansig-tansig 3.4972 5.5616 0.775
2 3-4-7-1 Logsig-tansig 7.012 6.3943 0.7268
3 3-5-6-1 Tansig-tansig 5.0315 5.7399 0.7545
4 3-6-4-1 Tansig-tansig 4.2268 5.522 0.7508
5 3-5-6-2-1 Tansig-logsig 4.8921 6.3865 0.681
Table 4 Results of some networks trained by various transfer functions, learning functions and structures for tapered cylindrical pin
profile
No. Structure Transfer function Validation MSE MAPE/% R2
1 3-6-1 Tansig-tansig 13.31 6.642 0.7268
2 3-7-3-1 Tansig-tansig 9.437 4.5464 0.8479
3 3-7-5-1 Tansig-tansig 5.4302 6.5267 0.7662
4 3-4-5-1 Logsig-tansig 8.8748 6.3262 0.8179
5 3-3-6-2-1 Tansig-logsig 9.8756 6.76 0.6908
6 3-6-5-2-1 Tansig-tansig 12.3 5.9338 0.7907
Fig. 4 Schematic of neural networks in this work for predicting Vickers microhardness in triangle (a) and tapered cylindrical (b) pin
profile tools
located under the curve in conical shoulder and held to
the vacancies near the shoulder which results in a better
quality in the welded sheets [23−25].
Figure 5 shows the friction stir welded area of two
samples after Macro-Etch process [6]. Plastic
deformation had not occurred in the heat affected zone
(HAZ) grains; however, the properties in HAZ had been
changed due to the high amount of temperature. The
properties in the HAZ area which may change
comprising of strength, toughness, elongation, and
corrosion resistance while the grain size and chemical
composition have been remained as the same. Heating in
the HAZ for aluminum alloys causes recovery of cold
work and coarsening of precipitates which consequently
lead to alteration in properties [26].
Thermo-mechanically affected zone (TMAZ) area
includes all the metal which experienced plastic
deformation. In this zone, sheets had been heated to high
temperature and due to the high values of forces, plastic
deformation has been observed in initial grains. TMAZ
can be divided to non-recrystallization TMAZ and
recrystallization TMAZ or nugget. for aluminum alloys;
non-recrystallization TMAZ is more important since the
reduction of microhardness values and corrosion
resistance occur in this zone [26]. Furthermore, the grain
size in nugget zone for aluminum alloys is usually small
and is distorted by the pin [27]. In addition, the shape of
this zone may be changed due to the pin’s figure. The
nugget zone’s profile in Fig. 5 is look more like to the
taper followed by the tapered cylindrical shaped pin.
Table 5 summarizes the results of metallographic tests
and probable defects for samples of triangle and tapered
J. Cent. South Univ. (2016) 23: 2146−2155
2151
Fig. 5 Transects of friction stir welded samples [6]: (a) Three distinct zones, stirred (nugget) zone, thermo-mechanically affected
zone (TMAZ) and heat-affected zone (HAZ); (b) Various micro structural zones
Table 5 Results of metallographic tests for samples which were welded using various profiles
Pin profile Tool feature Defect Defect location Macro-etch test
Tapered
cylindrical
Threadless pin,
flat shoulder
Tunnel in
bottom of weld
Deficiency of heat
transfer and
incomplete melting
Tapered
cylindrical
Threadless pin,
conical shoulder No defect —
Tapered
cylindrical
Threaded pin,
conical shoulder No defect —
Triangular Threadless pin,
flat shoulder
Tunnel in
bottom of weld
Deficiency of heat
transfer and
incomplete melting
Triangular Threadless pin,
conical shoulder No defect —
Triangular Threaded pin,
conical shoulder No defect
—
cylindrical pins. In samples which were welded using flat
shoulder and threadless pin, some defects were observed
at the bottom of the weld, which was caused due to
insufficient heat on the base metal. In other cases, no
defect exists since the heat transfer was adequate and the
metal flow was appropriate.
One of the mechanical properties which were
studied in the current research was microhardness and it
J. Cent. South Univ. (2016) 23: 2146−2155
2152
was measured using Vickers microhardness method.
Figures 6(a) and (b) represent the Vickers microhardness
results for tapered cylindrical and triangle pin profiles.
For all welded plates which were fabricated with six
proposed tools, the hardness profiles have been become
“W” shaped, which indicates a drop in the hardness
value. In nugget zone, due to the recrystallization, the
grains are situated coaxial and their sizes are become
smaller, which result in a higher microhardness. In
TMAZ, grains are stretched and deformed, which causes
a sharp decrease in the hardness values. HAZ is located
next to the TMAZ, and its structure looks like the base
metal which means a graduate increase in the hardness.
Fig. 6 Vickers microhardness test results for tapered cylindrical
(a) and triangle pin (b) profiles
In tensile test, samples were fabricated according to
ASTM-E08M tensile test standard and traverse speed
during the process was adjusted at 3 mm/min. In order to
compare the tensile results of welded sheets with base
metal, an AA6061 plate was tested and the corresponding
data is represented in Table 6. Besides, Table 7
summarizes the information related to tensile strength,
yield strength, elongation, and the position where sample
fractured. In all welded samples like the base metal, a
ductile failure was observed but the positions of failure
in samples were various. As can be seen in both profiles,
tensile strength of weld zone enhanced in comparison
with the base metal when pins were threaded and conical
shoulders were utilized. The maximum value of
elongation was observed when the welding process was
done using threadless tapered cylindrical pin with flat
shoulder. In other welded cases, the values of elongation
were less than that of AA6061. Furthermore, except the
sample which was welded using threadless triangular pin
with flat shoulder, in other welded plates failure occurred
in base metal. Threaded tapered cylindrical pin when the
shoulder was conical resulted in the superior tensile
strength. On the other hand, it is better to use flat
shoulder threaded tapered cylindrical pin when
elongation is the desired mechanical property.
For more illustration, yield strength, maximum
tensile strength, and elongation comparisons for different
pin profiles and base metal are given in Figs. 7−9.
A determinative criterion which was considered in
specifying the structures of the models was MSE in
validation step. Figures 10(a) and (b) illustrate the
performance of the models and mean squared error
values for two ANN models. MSE values for both
networks were less than 10, which indicates appropriate
trained models.
Figures 11 and 12 demonstrate the relationship
between the experimental values and the answers of the
ANNs for various steps of training procedure, and all
Table 6 Results of tensile test for base metal
Sample Yield strength/MPa Maximum tensile strength/MPa Elongation/% Failure feature
AA6061 22 140.5 15 Ductile failure
Table 7 Results of tensile test for welded samples using various pin profiles and different tools
No. Pin profile Tool feature Yie ld
strength/MPa
Maximum tensile
strength/MPa
Elongation/
%
Failure
position
1 Triangular Threadless pin, flat shoulder 25 122.8 5.2 Weld
2 Triangular Threadless pin, conical shoulder 20 138.6 10 Base metal
3 Triangular Threaded pin, conical shoulder 29 148.3 11 Base metal
4 Tapered cylindrical Threadless pin, flat shoulder 20 143.3 16.3 Base metal
5 Tapered cylindrical Threadless pin, conical shoulder 26 136.7 13.4 Base metal
6 Tapered cylindrical Threaded pin, conical shoulder 33 145.4 11 Base metal
J. Cent. South Univ. (2016) 23: 2146−2155
2153
Fig. 7 Yield strength values for different pin profiles compared
with base metal
Fig. 8 Maximum tensile strength values for different pin
profiles compared with base metal
Fig. 9 Elongation values for different pin profiles compared
with base metal
data were utilized in training procedure. The coefficient
of determination indicates the convergence between
experimental results and outputs of the model, and the
more closer the amount to one, the more perfect the
correlation between desired answers and predicted values.
Therefore, coefficient of determination (R2) between
actual data and predicted answers was calculated to
evaluate the performance of selected neural networks,
Fig. 10 MSE for ANN models for triangle pin profile (a) and
tapered cylindrical pin profile (b) tools
and 0.8479 and 0.775 were assessed as values of R2
which confirm the accuracy of designed models.
Mean absolute percentage error in training
procedure and for test data sets was another criterion to
evaluate the network’s performance. The values of
MAPE for all data were less than 5.56% and 4.55% for
triangle and tapered cylindrical pin profile tools,
respectively. Furthermore, MAPE values in test step for
both models were less than 7.48%, which implies the
reliable predictions of networks. Table 8 represents
MAPE and MAE values in training procedure and test
stage for both pin profiles.
4 Conclusions
1) AA6061 sheets were welded using triangle and
tapered cylindrical shaped pin tools. Vickers
microhardness test was performed in different distances
from the weld line. The quality of the weld in concaved
shoulders was better compared with the flat ones.
Furthermore, in all welds, the microhardness in TMAZ
has the least value while it experienced the maximum
amount in the base metal.
2) Two feed forward back propagation artificial
neural network techniques were employed to model and
predict the Vickers microhardness of AA6061 friction
J. Cent. South Univ. (2016) 23: 2146−2155
2154
Fig. 11 Comparison of actual and predicted values of Vickers microhardness for tapered cylindrical pin profile in training (a),
validation (b), test (c), and all data in training procedure (d)
Fig. 12 Comparison of actual and predicted values of Vickers microhardness for triangle pin profile in training (a), validation (b), test
(c), and all data in training procedure (d)
J. Cent. South Univ. (2016) 23: 2146−2155
2155
Table 8 MAPE and MAE for ANNs in this work
Pin profile Network
architecture
MAPE in
training stage/%
MAE in training stage
(HV)
MAPE for
test sets/%
MAE for
test sets (HV)
Triangular 3-8-1 5.398 1.9 7.48 2.62
Tapered cylindrical 3-7-3-1 4.41 1.67 6.024 2.068
stir welded plates. The values of MAPE in training
process for both ANNs were less than 4.83%, which
indicates that the answers of the models had acceptable
deviance with actual values of microhardness.
3) Considering the acceptable results of the ANN
models in this work, it can be concluded that this method
can be utilized to model the mechanical procedures.
Using mathematical modeling methods like ANN can
save time, material and costs and results in optimized
designs.
References
[1] NAGESWARA RAO P, JAYAGANTHAN R. Effects of warm rolling
and ageing after cryogenic rolling on mechanical properties and
microstructure of Al 6061 alloy [J]. Materials & Design, 2012, 39:
226−233.
[2] DAS H, CHAKRABORTY D, PAL T K. High-cycle fatigue behavior
of friction stir butt welded 6061 aluminium alloy [J]. Transactions of
Nonferrous Metals Society of China, 2014, 24(3): 648−656.
[3] RAJAKUMAR S, MURALIDHARAN C, BALASUBRAMANIAN
V. Statistical analysis to predict grain size and hardness of the weld
nugget of friction-stir-welded AA6061-T\n 6 aluminium alloy joints
[J]. International Journal of Advanced Manufacturing Technology,
2011, 57(1/2/3/4): 151−165.
[4] PENG D, SHEN J, TANG Q, WU C, ZHOU Y. Effects of aging
treatment and heat input on the microstructures and mechanical
properties of TIG- welded 6061-T6 alloy joints [J]. International
Journal of Minerals, Metallurgy, and Materials, 2013, 20(3):
259−265.
[5] OKUYUCU H, KURT A, ARCAKLIOGLU E. Artificial neural
network application to the friction stir welding of aluminum plates
[J]. Materials & Design, 2007, 28(1): 78−84.
[6] MISHRA R S, MA Z Y. Friction stir welding and processing [J].
Materials Science and Engineering R: Reports, 2005, 50(1/2): 1−78.
[7] ÇAM G, MISTIKOGLU S. Recent developments in friction stir
welding of Al-alloys [J]. Journal of Materials Engineering and
Performance, 2014, 23(6): 1936−1953.
[8] TOPIC I, HÖPPEL H W, GÖKEN M. Friction stir welding of
accumulative roll-bonded commercial-purity aluminium AA1050 and
aluminium alloy AA6016 [J]. Materials Science and Engineering: A,
2009, 503(1): 163−166.
[9] SUN Y, FUJII H, TAKADA Y, TSUJI N, NAKATA K, NOGI K.
Effect of initial grain size on the joint properties of friction stir
welded aluminum [J]. Materials Science and Engineering: A, 2009,
527(1): 317−321.
[10] UEMATSU Y, TOKAJI K, SHIBATA H, TOZAKI Y, OHMUNE T.
Fatigue behaviour of friction stir welds without neither welding flash
nor flaw in several aluminium alloys [J]. International Journal of
Fatigue, 2009, 31(10): 1443−1453.
[11] LIU H J, ZHANG H J, YU L. Effect of welding speed on
microstructures and mechanical properties of underwater friction stir
welded 2219 aluminum alloy [J]. Materials & Design, 2011, 32(3):
1548−1553.
[12] BUFFA G, FRATINI L, MICARI F. Mechanical and microstructural
properties prediction by artificial neural networks in FSW processes
of dual phase titanium alloys [J]. Journal of Manufacturing Processes,
The Society of Manufacturing Engineers, 2012, 14(3): 289−296.
[13] MISHRA R S, DE P S, KUMAR N. Friction stir processing [M].
New York: Springer, 2014.
[14] SHOJAEEFARD M H, BEHNAGH R A, AKBARI M, BESHARATI
GIVI M K, FARHANI F. Modelling and Pareto optimization of
mechanical properties of friction stir welded AA7075/AA5083 butt
joints using neural network and particle swarm algorithm [J].
Materials & Design, 2013, 44: 190−198.
[15] SHOJAEEFARD M H, AKBARI M, ASADI P. Multi objective
optimization of friction stir welding parameters using FEM and
neural network [J]. International Journal of Precision Engineering
and Manufacturing, 2014, 15(11): 2351−2356.
[16] GHETIYA N D, PATEL K M. Prediction of tensile strength in
friction stir welded aluminium alloy using artificial neural network
[J]. Procedia Technology, 2014, 14: 274−281.
[17] PALANIVEL R, MATHEWS P K. Prediction and optimization of
process parameter of friction stir welded AA5083-H111 aluminum
alloy using response surface methodology [J]. Journal of Central
South university, 2012, 19(1): 1−8.
[18] MOOSABEIKI V, AZIMI G, GHAYOOR M. Influences of tool pin
profile and tool shoulder curvature on the formation of friction stir
welding zone in AA6061 aluminum alloy [J]. Advanced Materials
Research, 2012, 445: 789−794.
[19] RAFIQ M, BUGMANN G, EASTERBROOK D. Neural network
design for engineering applications [J]. Computers & Structures,
2001, 79(17): 1541−1552.
[20] HAYKIN S S. Neural networks and learning machines [M]. Pearson
Education Upper Saddle River, 2009: 3.
[21] COLEGROVE P, SHERCLIFF H R. 3-dimensional CFD modelling
of flow round a threaded friction stir welding tool profile [J]. Journal
of Materials Processing Technology, 2005, 169(2): 320−327.
[22] CHOWDHURY S M, CHEN D L, BHOLE S D, CAO X. Tensile
properties of a friction stir welded magnesium alloy: Effect of pin
tool thread orientation and weld pitch [J]. Materials Science and
Engineering A, 2010, 527(21/22): 6064−6075.
[23] VENKATESHA B N, BHAGYASHEKAR M S. Preliminary studies
on mecahnical and metallurgical behaviour of friction stir welded
butt joints [J]. Procedia Engineering, 2014, 97: 847−853.
[24] LIENERT T J, STELLWAG JR W L, GRIMMETT B B, WARKE R
W. Friction stir welding studies on mild steel [J]. WELDING
JOURNAL-NEW YORK-, 2003, 82(1): 1−S.
[25] SUTTON M A, REYNOLDS A P, YANG B, TAYLOR R. Mixed
mode I/II fracture of 2024-T3 friction stir welds [J]. Engineering
Fracture Mechanics, 2003, 70(15): 2215−2234.
[26] LOHWASSER D, CHEN Z. Friction stir welding: From basics to
applications [M]. Elsevier, 2009.
[27] MILES M P, NELSON T W, STEEL R, OLSEN E, GALLAGHER M.
Effect of friction stir welding conditions on properties and
microstructures of high strength automotive steel [J]. Science and
Technology of Welding & Joining, 2009, 14(3): 228−232.
(Edited by YANG Hua)
