Friction Spot Welding of 7050-T76 Aluminium Alloy
P.S. Effertz
Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
Abstract
Friction spot welding is a solid state welding process suitable to obtain spot like-joints in overlap configuration. The
process is particularly useful to weld lightweight materials in similar and dissimilar combination, and therefore an
interesting alternative to other joining techniques (rivets, resistance welding, etc). In the current study the effect of
the process parameters, such as plunge depth, plunge time and rotational speed, are thoroughly investigated in order
to obtain the highest lap shear strength. The Taguchi method was used, as well as analysis of variance (ANOVA) to
study the importance of each working parameter. The results show that the plunge depth is the leading parameter in
order to obtain the best lap shear strength. An analytical model was used to predict the optimum performance of the
design, based on the optimal parameter. Confirmation tests were carried out to evaluate the agreement between the
predicted and the experimental values. The maximum response was obtained, although the reliability of the
predictions was relatively low.
Keywords
Frictions spot welding; Aluminum alloy; Taguchi method; ANOVA analysis;
Highlights

Friction spot welding of similar AA7050-T76 single-lap joints was performed.

The influence of joining parameters on the mechanical behavior of the joint was investigated

An analytical model was used to predict the response for the optimal parameter

The same model was used to predict the value of response for the confirmation tests
1.
Introduction
Friction Spot Welding (FSpW) is a solid state spot-like joining process developed and patented by
Helmholtz-Zentrum Geesthacht in Germany, formally known as GKSS Forschungszentrum [4]. FSpW has been
gaining ground when compared to mechanical bonging (rivets, bolts) or resistance spot welding (RSW), due to the
need of lightweight material, such as aluminium or magnesium, in automotive and aerospace applications to save
weight. Although RSW is still the primary method for single spot joining (automotive), there are inherent
disadvantages compared with FSpW, such as the high consumption of energy. This aggravates even more when
welding aluminium, for example, due to its high thermal conductivity. Thus, very high electrical currents are needed
in RSW to overcome thermal dissipation of aluminium. Furthermore, RSW is more expensive than FSpW and more
difficult to apply, since the process is sensitive to changing material and surface conditions [1-3]. The process also
1
aims to eliminate the disadvantages usually observed in other spot-like joining technologies, such as weight penalty,
difficulty of automation, requirement for sealants and corrosion problems in mechanical fastening, and the presence
of a keyhole after the process in FSSW, which can bring undesirable microstructural defects, such as porosity,
distortion; and lower mechanical proprieties, due to a smaller bonding area [5].
FSpW process is done using a tool composed by three components: the clamping ring, sleeve and pin, where
each one can move independently (see Figure 1). Both sleeve and probe have the same rotational speed ω, in the
same direction. On the other hand, the vertical speeds are different, Vp for the pin and Vs for the sleeve where
Vp≠Vs. The clamping ring only moves vertically with no rotational speed just to tighten the sheets together during
the process. It also restrains the plasticized material to flow around the sleeve, preventing burr formation. The
process can be divided in two variants: the probe plunge and sleeve plunge methods; defined according to which
component penetrates into the material during welding. The sequence in which the process works is divides in four
stages. Firstly, the sheets are fastened with a pre-determined clamping force. Subsequently, both pin and sleeve start
to rotate making contact with the surface of the upper sheet (Figure 2a). In the sleeve plunge variant, the sleeve
penetrates the upper sheet while the pin retracts forming a cavity to accommodate the plasticized material, due to the
heat produced by friction (Figure 2b). When the sleeve reaches a certain plunge depth, it remains rotating in the
same position (dwell time) in order to promote the mixing of the two materials. In the end of the dwelling time, the
sleeve will be retracted to the initial position and the pin pushes the displaced material within the cavity against the
sheet leaving a completely flat surface (Figure 2c). Finally, in the fourth stage the tool retracts and the surface is
completely refilled (Figure 2d).
Alternatively, as mentioned before, the process can be done by the probe plunge variant. The sequence of
operation is the same only in this variant the plunging of the material is done by the probe, whereas the sleeve will
retract forming an annular cavity to accommodate the plasticized material. With this variant, the forces transmitted
to the tool are less significant than those of the sleeve plunge variant, thus the process will require less power from
the machine. Although, the welded area is smaller than the sleeve plunge variant, which means that the joint will
have less mechanical resistance. [5-9].
Probe
Sleeve
Clamping
Ring
Fig. 1-FSpW tool
2
Fig. 2 - Illustration of FSpW process using sleeve plunge variant: a) Clamping and tool rotation; b) Sleeve
plunge and probe retraction; c) tool back to surface level; d) Tool removal
The design of experiments by Taguchi method has been widely used to improve the quality of material
manufacturing through the optimization of the process parameters, reducing experiment time and cost. The
orthogonal arrangement of the experiments provides optimum settings of parameters, which are insensitive to
variation in the environmental conditions and other noise effects [10-11]. The quality characteristics were evaluated
with an analysis of Signal/Noise (S/N) ratio, which can be divided in three categories: lower-the-better, larger-thebetter and nominal-the-best. A large S/N ratio is related to better quality characteristics regardless of the category.
Hence, for this study, the quality characteristic used is larger-the-better which is consistent with higher lap shear
strength.
Several investigations in the parameter optimization of solid state welding have been reported. Campanelli et
al. [1] used the Taguchi method for parameter optimization in friction spot welds of AZ31 magnesium alloy, and
concluded that plunge depth (PD) was the most important parameter to obtain high lap shear strength (LSS) results.
Shojaeefard et al. [12] concluded that traverse speed is a highly significant factor playing an important role in the
grain size in friction stir welding of AA1100. In friction stir spot welding of AA3003-H12, Tutar et al. [13] reported
that PD is the leading parameter to obtain high tensile shear load and expanded heat affected zone (HAZ),
thermomechanically affected zone (TMAZ) and stir zone (SZ) regions.
In this work, the Taguchi method and analysis of variance (ANOVA) were used to investigate the influence
of PD, plunging time (PT) and rotational speed (RS) on the LSS of similar AA7050-T76 joints welded by FSpW.
2. Experimental details
Precipitation hardened alloy AA7050-T76 coupons with 138×60×2mm were used in this work. The
nominal chemical composition of the used alloy can be found in Table 1.
Table - 1 Nominal chemical compositions and of base materials (wt.%).
Alloy
AA7050T76
Ti
0.02
Al
Bal.
Si
0.03
Fe
0.06
Cu
2.1
3
Mn
0.0079
Mg
2.2
Cr
0.0048
Zn
6.187
Zr
0.11
The overlap spot joints where performed using a Harms & Wende RPS 200 welding machine. The geometry
of the welding tool is 18 mm, 9 mm and 6 mm in diameter for the clamping ring, sleeve and pin, respectively. To
evaluate the weld strength, lap shear testing was conducted with a Zwick-Roell 1478 machine, using a crosshead
speed of 2 mm/min at room temperature. The specimens’ geometry follows the requirements of the ISO14273:2000
standard [14]. Several preliminary experiments were done in order to set the working limits of the FSpW
parameters. As shown in Table 2, each parameter is set up with three levels.
Table - 2 Welding parameters and levels
Symbol
PD
PT
RS
Welding parameter
Plunge Depth
Plunging Time
Rotational Speed
Unit
mm
s
rpm
Level 1
2.4
1.8
2600
Level 2
2.6
2
2800
Level 3
2.8
2.2
3000
To select an appropriate orthogonal array of experiments, the total degrees of freedom (DOF) need to be
computed. DOF are defined as the number of comparisons between process parameters that need to be made to
determine which level is better and specifically how much better it is [15]. The DOF associated to the interaction
between two parameters can be computed by the product of the DOF of the intervenient parameters. Although, in
this study the interaction between parameters is neglected, meaning that for this design there are six DOF in total.
In the orthogonal array designation, the number following the L indicates the number of testing setups
involved. In this study, an L9 orthogonal array was used since it respects the relation stated above and it can handle
three-level process parameters. The DOE layout is shown in Table 3.
Table 3. Welding parameters and levels
1
2
3
4
5
6
7
8
9
PD
2.4
2.4
2.4
2.6
2.6
2.6
2.8
2.8
2.8
PT
1.8
2
2.2
1.8
2
2.2
1.8
2
2.2
RS
2600
2800
3000
2800
3000
2600
3000
2600
2800
ANOVA was performed to investigate the importance of each FSpW process parameters and their
influence on the mechanical performance. In order to develop an adequate local functional relationship between the
LSS and the FSpW process inputs, a regression model was applied. To test the robustness of the model, three
different parameter combinations, within the range presented above, were tested. The combinations used are not
included in the experiments suggested by an L9 orthogonal Taguchi array.
4
3. Results and Discussions
The grain structure of the base material (BM) is characterized for experiencing no plastic deformation, nor
alterations in its microstructure and mechanical properties, due to the thermal cycling from the weld. On the other
hand, the HAZ undergoes a severe thermal cycle. However, for in this study considerable grain coalescence did not
happen.
The TMAZ where moderate plastic deformation occurs and a moderate thermal cycle are responsible for the
microstructural changes in this region, although recrystalization does not occur. The grain structure is more
elongated and highly deformed. [8][16-17].
In the SZ, the material is submitted to intense plastic deformation and high temperature exposure due to the
friction and stir promoted by the tool. These conditions lead to dynamic recrystalization of the material and mixture
between the upper and lower sheet in such a way that the original interface created by the superposition of the two
sheets becomes unnoticeable. The resulting microstructure consists in grains which are roughly equiaxed and often
an order of magnitude smaller than the grains in the BM zone [8][16-17].
Finally, the hook is a characteristic feature in FSpW and it represents a geometric defect originated at the
interface of the two welded sheets. The hook is formed due to the upward bending of the sheet interface caused by
the tool penetration into the bottom sheet [8]. The mechanical properties are affected by hook and also bonding area
[18].
c)
3.1. DoE: Taguchi L9 orthogonal array
The DoE experiments along with the LSS results and S/N ratio are shown in table 4. The values of S/N ratio
were evaluated considering the condition larger-the-better were. Note that the standard deviations obtained are low,
meaning that the weld conditions are highly reproducible. According to the standard for resistance spot welding for
aerospace applications, AWS D17.2/D17.2M, the required mean LSS must be at least 5715 N, for Al-alloys with 2
mm of thickness [19]. Hence, all the welds exceed that requirement, apart from the welds obtained with Condition 4
and 7. Condition 6 proved to be the experiment with the highest lap shear strength, over 11 kN. These outstanding
results are due to the fact that this particular alloy has very high yield strength when compared to other Al-alloys.
Table - 4 L9 orthogonal array with response, mean and S/N ratio
Condition
PD
(mm)
PT
(s)
RS
(rpm)
1
2
3
4
5
6
7
2.4
2.4
2.4
2.6
2.6
2.6
2.8
1.8
2
2.2
1.8
2
2.2
1.8
2600
2800
3000
2800
3000
2600
3000
LSS (N)
1
9332.22
8809.91
9486.07
5599.15
10170.87
11477.63
4482.61
5
2
9414.99
9051.76
9009.29
5003.57
10587.83
11531.20
4367.89
3
9754.77
8191.88
8484.11
5035.48
10258.27
10798.04
4382.08
Mean LSS
(N)
S/N ratio
(dB)
9500.66
8684.52
8993.16
5212.73
10338.99
11268.96
4410.86
79.55
78.75
79.05
74.31
80.29
81.03
72.89
8
9
2.8
2.8
2600
2800
2
2.2
6742.33
7256.30
5905.59
6812.46
7417.94
5677.52
6688.62
6582.09
76.39
76.22
3.1.1 Main Effects of mean LSS and S/N ratio
The effect of design parameters, such as PD, PT and RS, on the LSS and S/N ratio can be found in table 5.
Due to the orthogonality of the design, it is possible to separate the effect of each factor at each level. The mean
response states the average value of performance characteristic for each parameter at different levels. For example,
the mean S/N ratio for PD at level 1, 2 and 3 can be calculated by averaging the S/N ratios for experiments 1-3, 4-6
and 7-9, respectively. Assessing which parameter has the greatest effect on the response can be done by computing
its difference (Δ). It can be obtained by subtracting the highest value by the minimum within a particular parameter.
Hence, the greatest variation for both LSS and S/N ratio was observed for PD. On the other hand, RS shows the
lowest effect of all parameters.
Table 5 - Main effects of the mean LSS and S/N ratio
Level
1
2
3
Δ
Rank
PD
9059,44
8940,23
5893,86
3165,59
1
Mean LSS (kN)
PT
6374,75
8570,71
8948,07
2573,32
2
RS
9152,75
6826,45
7914,34
2326,30
3
PD
79,12
78,54
75,17
3,95
1
S/N ratio (dB)
PT
75,58
78,48
78,77
3,19
2
RS
78,99
76,43
77,41
2,56
3
For better understanding the plots of the effects for each parameter are shown. From figure 3, can be
observed that LSS is highest for 2.4 mm, 2.2 s and 2600 rpm of PD, PT and RS, respectively. It can also be
concluded that the S/N ratio is maximum for the same set of parameters. Hence, the Taguchi method suggests that
these are the optimized set of parameters.
Also, in the investigated range, PD plot suggests a reduction in the parameter to obtain a better weld quality;
PT plot indicates that an increase in quality is possible for values above 2.2 s; RS plot show a tendency for an
increase in the weld characteristics below 2600 rpm and above 3000 rpm.
Fig.3 – Main effect of the parameters in the LSS and S/N ratio
6
3.1.2 Analysis of variance (ANOVA)
The purpose of the ANOVA is to investigate which parameters of the process affect significantly the
performance characteristics.
Table 6 and 7 show the results of ANOVA for LSS and S/N ratio, respectively. The ANOVA analysis was
conducted using Eq. 3-13, presented in the Appendix.
Table 6 - Results of analysis of variance for LSS
DF
2
2
2
2
8
PD
PT
RS
Error
Total
SS
19,3155
11,5866
8,1288
5,2026
44,2335
MS
9,6578
5,7933
4,0644
2,6013
-
F-test
3,7127
2,2271
1,5625
-
P%
43,6671
26,1941
18,3771
11,7617
100
Table 7 - Results of analysis of variance for S/N ratio
DF
2
2
2
2
8
PD
PT
RS
Error
Total
SS
27.2936
18.6077
10.0252
6.6480
62.5745
MS
13.6468
9.3039
5.01257
3.32402
-
F-test
4.1055
2.7990
1.5080
-
P%
43.6178
29.7369
16.0211
10.6242
100
Based on the results of Table 6 and 7, the parameter which affects more significantly the LSS and S/N ratio is the
Plunge Depth.
3.2 Prediction for Optimum Performance
After determining the optimum condition, it is possible to predict the optimum response Yopt. For the larger
the better quality characteristic, the study of the main effects show that the optimum condition for both LSS and S/N
ratio is PD1PT3RS1 (PD1=2.4 mm, PT3=2.2 s, RS1=2600 rpm). Yopt was computed by Eq. 1. Hence,
where T represents the total of all results, and
,
and
are the mean values of the responses of PD, PT and
RS at level one, three and one, respectively. The results for the optimum performance are shown in Table 8.
Table 8 - Results for the optimum performance
Yopt
LSS (N)
11231.34
7
S/N ratio (dB)
81.66
3.3 Confirmation tests
Once the optimal level of the process parameters is selected, the final step is to verify the improvement of the
performance characteristics. Thus, three confirmation experiments using the optimal condition were carried out. The
results are shown in Table 9.
Comparing the experimental values with the predicted ones, it is visible that they do not match, meaning that
the optimal parameter does not correspond to the best. Note that the difference between the optimal process
parameter and condition 6 (Table 4) relies only on the value of the PD, which is 2.6 mm (level 2) instead of 2.4 mm
(level 1). However, the quality characteristics for condition 6 are much better than the ones from the optimal.
Comparing the experimental results of mean LSS and S/N ratio of condition 6 with the predicted one, very
good agreement is obtained, as shown in Table 10.
Table 9 - Results of the confirmation test for the optimal process parameters
PD
(mm)
PT
(s)
RS
(rpm)
2.4
2.2
2600
LSS (N)
1
6761.39
2
8402.62
3
6730.56
Mean LSS
(N)
S/N ratio
(dB)
7298.19
77.13
Table 10 – Comparison between the predicted results and Condition 6
Level
LSS value (N)
S/N ratio value (dB)
Prediction for LSS
Prediction for S/N ratio
Experiment
PD2PT3RS1
PD2PT3RS1
PD2PT3RS1
11112.02
-
11268.96
-
81.08
81.03
The plot for the predicted versus actual values, shown in Fig. 4, indicates a satisfactory agreement between
the performance prediction, which can be computed using Eq. 13, and the actual values of LSS and S/N ratio. The
regression coefficient R2 also suggests an acceptable fit between the experimental results of the design and the
predicted ones. Moreover, in order to verify the adequacy of the developed model, 5 confirmation experiments were
carried out with process parameters chosen within the range, that are not contemplated in the L9 array of
experiments given by Table 3. The obtained results are shown in table 11 for LSS.
11000
11000
Predicted Values
13000
Predicted Values
13000
9000
7000
5000
3000
3000
R² = 0,8824
9000
7000
5000
3000
3000
Taguchi
8000
8000
13000
Experimental Values
Experimental Values
Fig. 4 - Experiental vs Predicted values for LSS (left) and with validation tests (right)
8
Validation
13000
Table 11 – Experimental tests of model verification for LSS
Verification
1.
2.
3.
4.
5.
PD1PT3RS1 (optimal)
PD2PT3RS3
PD3PT3RS1
PD2PT3RS2
PD2PT1RS1
PD2PT2RS1
Experimental LSS
(N)
7298.19
8290.69
9514.91
8938.14
4534.74
8187.26
Predicted LSS
(N)
11231.34
9873.61
8065.65
8785.72
8538.70
10734.70
Error
(%)
35.02
16.03
17.35
1.73
46.89
23.73
The reason for the mismatch between the predicted optimum performance (Table 8) and the experimental
(Table 9), has to do with the fact that the Taguchi method merely considers the individual effect of the parameters in
the response, disregarding the interactions between them. The poor agreement between these values suggest that
additivity is not present and there will be poor reproducibility of small scale experiments to large scale, thus the
experimenter should not implement the predicted optimum condition on a large scale.
The experiments carried out with a set of parameters which are not included in the L 9 array (table 11), show a
high deviation between the prediction and the experimental values (average of 38%). Nevertheless, none of the
conditions tested surpass the maximum response obtained in the Taguchi array.
4. Conclusions
The effects of friction spot welding parameters on the mechanical behavior of aluminium AA7050-T76 joints
were investigated using parameter design of the Taguchi Method. The following conclusions can be drawn based on
the experimental results of this study:

The produced spot joints showed very good mechanical performance with maximum lap shear
strength of approximately 11.3 kN.

The Taguchi method successfully maximized the response and therefore the best welding condition
was found.

Statistical model was reliable for the selected range; nevertheless as it was observed from the
ANOVA tables, secondary or ever tertiary interaction can play an important effect on the response.

The Full factorial design or response surface methodology (RMS) is thus recommended for a deeper
understanding of the parameter interactions.
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