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Investigations into FSW joints of dissimilar aluminum alloys
Materials Today: Proceedings xxx (xxxx) xxx
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Investigations into FSW joints of dissimilar aluminum alloys Meghnath Sen, Sachindra shankar, Somnath Chattopadhyaya Department of Mechanical Engineering, IIT (ISM), Dhanbad, India
a r t i c l e
i n f o
Article history: Received 6 August 2019 Accepted 27 September 2019 Available online xxxx Keywords: Friction stir welding process Unlike aluminum joint Process parameters Taguchi L9 optimization ANOVA Mechanical properties
a b s t r a c t Joining technique Friction stir welding (FSW) widely accepted for developing new joints of like and unlike metals for wide applications. In present experimental study, two dissimilar aluminium alloys, AA 5086 and AA6061 of 6 mm thickness are welded together using FSW. Nine experiments carried out on a vertical milling machine using a suitable fixture with strong holding aluminium plates on it. Experiments are executed by varying process parameters, which includes speed of tool rotation, welding speed and offset of tool at three levels each. Experiments are performed as per design of experiment using Taguchi’s L9 orthogonal array. Mechanical properties tensile strength is tested and compared with base metal properties. Optimum levels are determined with the help of a statistical optimization tool, ANOVA. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International conference on Materials and Manufacturing Methods.
1. Introduction In preceding year’s global trend takes a diversion to the lightweight production for more performance, environment friendly, better mobility and cheaper cost. Combining conventional metal with new metals offers a good technical properties regarding to a single material. Multi- material components joining is a challenge in the field of manufacturing science, which leads to the new developments of wide range of welding processes to join dissimilar metals. On the Earth’s surface Aluminum (Al) is the third largest element available. Aluminum has low density with attractive strength, higher corrosion resistance, high ductility, good workability, good recyclability and low electrical resistance. Recently joining of different dissimilar metals achieves a huge success in the field of weight reduction. A unique solid phase joining science, Friction stir welding process (FSW) is capable to assemble aluminum alloys with a sound quality weld joint. An innovative joining process at solid state, FSW was designed and developed at ‘The Welding Institute’ (TWI) in United Kingdom (UK) in the year 1991. This process joins two materials below melting point by fusing them and high temperature is induced by the tool due to rotation. The Friction stir welding (FSW) gives little distortion with high strength of joint compare to other welding process. Eminent efficiency of joint comes from higher processing speeds.
Friction stir processing technique is rapidly acquired as a advance welding practice, performed at solid state for joining wide range of applications [1,2] and multiple materials likes different grade of aluminum alloys, copper alloys , brass, titanium, steel, magnesium, metal matrix components and so on without voids, creaks or very less distortion, which are challenging to weld applying fusion methods. It has received a great deal of attention all over the world in a small lap of time after its developments due to its environment friendly, hazard free outputs, low distortion welding and high quality welding product. In FSW pressure and deformation plays a key role at a significant portion of bonding. Due to solid state welding defects commonly found in fusion welding are evaded. Therefore, reduction in inspection of weld and elimination of re-weld procedure provides a significant benefit in costing. Friction stir technique is a fully mechanized welding development use to produce assembled components with the help of rotation and non –consumable weld tool to the soften material strongly clamped at job table through produce of heat by generating friction among FSW tool and base metal work piece, which starts plastic work [3]. A non-consumable specialized FSW tool considered as a main member of the FSW Process. The tool contains a shoulder of particular shaped with a unique profile tool pin. Selection of the material and geometry of the tool based on the base material to be weld, base material specifications, material dimensions, type of joint to be performed and so on. Both lap joint and butt joint can be performed in this process.
https://doi.org/10.1016/j.matpr.2019.09.218 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International conference on Materials and Manufacturing Methods.
Please cite this article as: M. Sen, S. shankar and S. Chattopadhyaya, Investigations into FSW joints of dissimilar aluminum alloys, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.218
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good quality and low cost. Taguchi methods focus on best performance design of manufacturing process and product. It addresses quality in the area of ‘‘off-line quality control” and ‘‘on-line quality control”. It involves three steps for designing performance of a product-
Li et al. (1999) successfully joined AA 2024 and AA6061 alloy using FSW and showed that flow microstructure consists of dynamically recrystallized grains and during welding temperature rise to 60% to 80% of melting temperature. At speed variation from low to high up to 1200 rpm, there was a change in grain structure. At higher speed larger grain structure formed [4]. Koilraj et al. [5] applied Taguchi technique in his work to note the optimal process parameters of dissimilar Aluminum alloy 2219 and AA 5083 joint. L16 orthogonal array is used to inspect optimal parameters of the process based upon the tensile strength value of experimented weld joint. Along this there was a consideration that cylindrical threaded tool is best among all FSW tool. Author showed that optimum value at speed of tool rotation at 700 rpm, speed of welding 15 mm/min and D/d ratio 3 can gave best result of FSW. Rajkumar et al. [6] joined dissimilar aluminum alloys AA5052 and AA6061 together using a cylindrical pin with a constant speed of tool rotation at710 rpm with a fixed feed rate of 20 and 28 mm/min. Both of the metals joined successfully, while cylindrical threaded pin plays a key role for excellent bonding. Both of the samples had good tensile strength, better ductility and fine grain structure. Bayazid ei al. [7] joined Aluminum 6063 and aluminum 7075 alloy together using FSW process and H13 tool steel in present research work. A royal Taguchi approach is considered with L9 OA to find out the optimum process parameters followed by ANOVA analysis for significance of weld parameters. At weld speed 120 mm/min, rotational speed 1600 rpm it gave maximum tensile strength 143.59 MPa. ANOVA indicated the effectiveness of process variables on tensile strength were 59%, 30% and 7%. Ahmed et al. [8] studied joining of dissimilar aluminum alloy , AA5083 and AA7075 with a constant TRS of 300 rpm with altered WS of 50, 100,150 and 200 mm/min. Joints were successfully performed without defect. There was fine grain structure in the NZ. Author showed that increasing of weld speed lead to reduction of grain size. Other mechanical properties like hardness, weld strength were good. Hardness value lies in between 245 and 267 MPa and joint efficiency was 77 to 87%. Tarraf et al. [9] investigated three different similar and dissimilar aluminum joint using FSW with the application of ultrasonic sound waves. AA6061 –AZ31, AA6060AA7020 and AA7020-AA7020aluminum alloys were welded together with speed of tool rotation 1000, 1200and 1200 rpm and speed of welding at 300, 800 and 150 mm/min. All welding were successfully performed. Author concludes that there was no need of guided waves in the case of similar aluminum metal joining, but there were some effect on dissimilar metal joining. Several experiments carried out using different aluminum alloys from 1XXX to 7XXX series. But there is very less work on the joining of dissimilar alloy of AA5XXX to 6XXX. There is a scope of optimize welding parameters with the help of Taguchi approach for better performance, good mechanical properties, less cost and less time. A study with parameter optimization of FSW (AA5083 and AA6061) using Taguchi technique and ANOVA analysis is presented. Also influences of parameters for the process on the tensile strength of the joint were analyzed.
System or design of concept Design of parameter and Design of tolerance. Although it has a better ability to solve single response problems, there are some limitations also. The design of parameters using Taguchi method cover following steps
Proper selection of quality character to be optimized. Selection of no. of levels. Matrix experiment design. Conduct the designed matrix experiment. Experimental result analysis with the help of S/N ratio and ANOVA. Determination of optimal process parameters. Prediction of performance value. Verification of predicted value with a confirmation test. 2.1. Orthogonal array(OA) selection Orthogonal array selection basically depends upon three parameters Number of factors. Number of levels. and Experimental resolution or limitations of cost. In this research work three level designs and three factors are selected, as a result L9 OA is preferred for study and investigation. For each factor the degree of freedom i.e D.O.F = 2
2. Taguchi method Best quality starts with the engineering of quality that leads to the product and process design optimization for best performance,
Fig. 1. FSW Tool.
Table 1 Chemical properties and composition of AA 5083 and AA 6061. Element
Mg
Fe
Si
Cu
Mn
Cr
Ti
Al
AA 5083 AA 6061
4.31 1.08
0.25 0.17
0.156 0.63
0.06 0.32
0.58 0.52
0.1 0.1
0.017 0.02
Remainder Remainder
Please cite this article as: M. Sen, S. shankar and S. Chattopadhyaya, Investigations into FSW joints of dissimilar aluminum alloys, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.218
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M. Sen et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 2 Parameter values for the existing process and its three levels.
Fig. 2. Symmetric diagram of fixture.
As Degree of freedom (D.O.F) = (levels no. 1), i.e DOF = (3–1) = 2. So total degree of freedom i.e D.O.F = (3 2) = 6. Universally the degree of freedom (D.O.F) of OA must not be lesser than the total degree of freedom (D.O.F) of all factors. As D.O.F for L9 is 8, so it is appropriate for present study. 3. Experimental methodology Welding is performed through Taguchi technique using MINITAB 17. Here three factors speed of tool rotation, welding speed and tool offset are investigated. A vertical milling machine used in the present study to weld the Nine (09) sets of Aluminum plates. In order to obtain better weld quality, welding parameters likes TRS, WS and tool offset length has been varied and rest of welding parameters are kept constant. In present study AA 5083 aluminum alloy and AA 6061 aluminum alloy choose as starting metals for joining using Friction stir welding technique. Base metal pieces of dimension 100 mm (length) 100 mm (width) 6 mm (thick) were cut from the large sheets. Aluminum 6061 and Aluminum 5083 is very light metal, has better corrosion resistance to common atmosphere. It has high reflectivity and it retain toughness at very low temperature. Aluminum 6061 and Aluminum 5083 is a good conductor of heat and electricity and it is non-toxic and easily recycled. AA 5083 and AA 6061aluminum alloys are commonly used in the field of transportation sector, construction sector where greater mechanical behavior and properties; like tensile strength,
Level
A Speed of Tool Rotation (rpm)
B Speed of Welding (mm/min)
C Tool offset (mm)
Level 1 Level 2 Level 3
710 1000 1400
63 100 160
0.0 0.5 1.0
hardness etc are truly required. The Chemical composition (Table 1) of materials plays a vital aspect in the process of welding. A threaded profiled H13 steel tool (Fig. 1) is considered and used in this process. The tool has 24 mm shoulder diameter with Pin diameter 5 mm and length 6 mm. H13 tool has higher strength with low wear resistance and good thermal fatigue. According to the work piece dimensions a fixture is designed and developed for holding the work piece plates (Figs. 2 and 3). For the experimentation, Taguchi L9 approach has been considered. Three different parameters- speed of tool rotation (TRS); welding speed (WS) and tool offset are elected and experimental work carried out. The parameters selected for welding with various levels are accustomed in Table 2. The values of welding parameters are chosen as per availability of range of values in vertical milling machine. Plunge depth is kept constant for all the experiments. Fig. 4 shows different weld plates with the effect of parameters on welding surface. Sample number S5, S4, S3, S2, S1, S8 and S7 have better visual surface finish (Shown in Fig. 4) as well as good strength. 4. Experimental results Nine samples for tensile test are prepared utilizing wire cut EDM machine. They were prepared with the direction placed perpendicular to the direction of welding, so that zones of welding can be placed in the middle of the sample specimen. The sample specimen is prepared as per ASTM E8. The 100 mm length (L) specimen has 6 mm width (W) and thickness (T) with30 mm grip length (B), 32 mm reduce section length (A), 19 mm width of grip section(C) and 6 mm fillet radius(R). A Symmetric diagram of sample and an actual sample after cut are shown in Fig. 5. The tensile test was performed on the universal testing machine (UTM), manufactured by Hounsfield (H50Ks). The basic procedure of testing starts with holding the specimen sample in between one fixed crosshead and a movable crosshead. After that load is applied
Fig. 3. FSW set up with fixture.
Please cite this article as: M. Sen, S. shankar and S. Chattopadhyaya, Investigations into FSW joints of dissimilar aluminum alloys, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.218
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Fig. 4. Welding joints using FSW.
Fig. 5. (a) Symmetric sketch of tensile test specimen; (b) Sample after cutting by wire cut EDM.
Fig. 6. Testing in UTM.
and at initial condition ‘Neck formation’ takes place , which lead to breaking of the sample specimen as shown in Fig. 6. After performed the tensile test values of tensile strength of each specimen are listed in Table 3.
where, Yi ¼ Tensileforcevalueforthe0 i0 nooftest, ‘n’ = No. of tests or experiments and ‘N’ = Total number of data. The data of experiment are transferred into S/N ration and mean value (Fig. 7).
5. Investigation on experimental value.
5.2. Analysis of variance (ANOVA)
5.1. Signal to noise (S/N) ratio
The objective of using ANOVA is to figure out the importance of parameters for a existing process statistically. It shows a clear view on the response affected by process parameters and its level of significance. In this research ANOVA table is calculated based on S/N ratio and mean values. The parameters values for the process and its three levels are elaborated in Table 2.
S/N ratio approach followed in Taguchi method to calculate the character of quality. It can be divided into four stages- ‘nominal is best’ (2 stages), ‘smaller is better’ and ‘larger the better’. Since study objective is to find and maximize tensile strength and optimize parameters for the process, ‘larger is better’ characteristic of quality is employed in present study. For calculating S/N ratio the formula used is – n S 1 X 1 ¼ 10log10 N N i¼1 Y2i
ð1Þ
5.3. Analysis of experimental data. The data of tensile strength is investigated to show the outcomes of parameters for the process. The experimental values are convinced into S/N ratio and mean value using popular software
Please cite this article as: M. Sen, S. shankar and S. Chattopadhyaya, Investigations into FSW joints of dissimilar aluminum alloys, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.218
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M. Sen et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 3 Experimental data from tensile test experiments. Sample No
Speed of Tool Rotation (RPM)
Welding Speed (mm/min)
Tool Offset (mm)
Tensile strength (MPa)
S1 S2 S3 S4 S5 S6 S7 S8 S9
710 710 710 1000 1000 1000 1400 1400 1400
63 100 160 63 100 160 63 100 160
0.0 0.5 1.0 0.5 1.0 0.0 1.0 0.0 0.5
126.8 113.2 125.3 131.5 104.3 134.0 106.1 110.4 102.7
Fig. 7. Specimens earlier and later tensile test.
Table 4 Experimental layout –L9 OA, S/N ratio and mean value (Tensile strength). No
1 2 3 4 5 6 7 8 9
A
B
C
Speed of Tool Rotation
Speed of Welding
Tool offset
1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3
1 2 3 2 3 1 3 1 2
MINITAB 17. After calculation, S/N ratio and mean values are tabulated in Table 4, which named as ‘experimental layout –L9 OA, S/N ratio and mean value for tensile strength. The larger value of S/N ratio and larger mean values corresponding to better quality (Larger to better) characteristics for Tensile strength are tabulated on the Table 5and 6. Based on
S/N ratio
Mean Tensile Strength (Mpa)
42.0624 41.0769 41.9590 42.3785 40.3657 42.5421 40.5143 40.8594 40.2314
126.8 113.2 125.3 131.5 104.3 134.0 106.1 110.4 102.7
the calculated values of Tables 5 and 6, Figs. 8 and 9 after optimum level setting, it is obtained that A2B1C1 give optimum setting for tensile strength. Here A2 shows average mean value of speed of tool rotation at 2nd level, B1 shows average mean value of speed of welding at 1st level and C1 shows average mean value for tool offset at 1st level. After optimization, optimum level setting shows
Please cite this article as: M. Sen, S. shankar and S. Chattopadhyaya, Investigations into FSW joints of dissimilar aluminum alloys, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.218
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¼ ð123:3 þ 121:5 þ 123:7 2 117:144Þ ¼ 134:212MPa
Table 5 Table of response for S/N ratios (Tensile strength).
Where, T = overall mean value of tensile strength, A2 = average mean value for speed of tool rotation at 2nd level, B1 = average mean value for speed of welding at 1st level and C1 = average mean value for tool offset at 1st level.
Larger is better Level
1 2 3 Delta Rank
A
B
C
Speed of Tool Rotation (rpm)
Speed of Welding (mm/min)
Tool offset (mm)
41.70 41.76 40.54 1.23 1
41.65 40.77 41.58 0.88 2
41.82 41.23 40.95 0.87 3
After getting the optimum level for design process parameters, final step leads to verify that how much improvement is done in quality characteristics using the optimal value. For this speed of tool rotation set at 1000 rpm, speed of welding set at 63 mm/min and tool offset set at 0 mm. After FSW using this parameters tensile test predicted value (134.212 MPa) was very close to the actual test value (136.1Mpa). Regarding the confirmation test, it is observed that error among actual value and value of prediction for maximum tensile strength is very less 1.387%.
Table 6 Table of response for Means (Tensile Strength). Larger is better Level
1 2 3 Delta Rank
A
B
C
Speed of Tool Rotation (rpm)
Speed of Welding (mm/min)
Tool offset (mm)
121.8 123.3 106.4 16.9 1
121.5 109.3 120.7 12.2 2
123.7 115.8 111.9 11.8 3
5.6. ANOVA analysis.
that at 1000 rpm of speed of tool rotation with 63 mm/min of speed of welding and 0.0 mm of tool offset gives the maximum shear force. The graph of main effects of S/N ratio and means for tensile strength are plotted in Figs. 8 and 9. The setting for optimal parameters based upon S/N ratio and mean is A2B1C1. 5.4. Estimation of optimum parameter value for tensile force. Based upon the value of experiments, the level of optimum setting parameters is A2B1C1. The average parameters values at their level are appropriated from the Tables 6 and 3. The value of the response for tensile strength after prediction is -
Tensile strength value ðPredictedÞ ¼ A2 þB1 þ C1 2T
5.5. Test of Confirmation.
ð2Þ
ANOVA analysis is tool, selected to analyze the % contribution for parameters of a existing process. Responses using ANOVA technique presented in the Tables 7 and 8. Tables 7 and 8 presents the ANOVA result for tensile strength of the S/N ratio and means value. Table 7 shows that % contribution among all parameters. The highest % contribution is observe for the speed of tool rotation from all the parameters and maximum % value is 43.49% followed by speed of welding, which has % of contribution 21.92%. Among all the process tool offset has the lowest % contribution value 18.16%. Table 8 shows that % contribution among all parameters. The highest % contribution is observed for the speed of tool rotation from all the parameters and maximum % value is 43.33% followed by speed of welding, which has % of contribution 23.03%. Among all the process tool offset has the lowest % contribution value 18.08%. A 3-dimentional surface plotting of tensile strength versus speed of tool rotation and speed of welding and tool offset clearly shows that higher tensile strength of the joint obtained at a lower welding speed with a moderate speed of tool rotation and a smaller tool offset (Fig. 10 and 11). When values continuously changes, there is a small variation in the tensile strength. This variation is taken place due to generation of heat at the time of welding. All 3D plots and contour plots describe the variations.
Fig. 8. Main effects plot for S/N ratio based upon tensile strength.
Please cite this article as: M. Sen, S. shankar and S. Chattopadhyaya, Investigations into FSW joints of dissimilar aluminum alloys, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.218
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Fig. 9. Main effects plot for means based upon tensile strength.
Table 7 Analysis of variance (ANOVA) for Tensile strength (SNRA). Source
DOF
Seq SS
Contribution
Adj SS
Adj MS
F-Value
P-Value
A B C Error Total
2 2 2 2 8
2.865 1.444 1.196 1.082 6.588
43.49% 21.92% 18.16% 16.43% 100.00%
2.865 1.444 1.196 1.082
1.4327 0.7220 0.5981 0.5412
2.65 1.33 1.11
0.274 0.428 0.475
DOF – Degree of freedom, Adj SS – Adjusted summation of square, F – Ratio of Feisher, Adj MS – Adjusted square of mean, P – value of Probability (exceeds 95% confidence level), Seq SS – sequential summation of squares.
Table 8 Analysis of variance (ANOVA) for Tensile strength (Means). Source
DOF
Seq SS
Contribution
Adj SS
Adj MS
F-Value
P-Value
A B C Error Total
2 2 2 2 8
522.9 277.9 218.2 187.7 1206.6
43.33% 23.03% 18.08% 15.55% 100.00%
522.9 277.9 218.2 187.7
261.43 138.93 109.09 93.83
2.79 1.48 1.16
0.264 0.403 0.462
DOF – Degree of freedom, Adj SS – Adjusted summation of square, F – Ratio of Feisher, Adj MS- Adjusted square of mean, P – value of Probability (exceeds 95% confidence level), Seq SS – sequential summation of squares.
1400
Speed of Tool Rotation
1300
1200
1100
1000
900
Contour Plot of Tensile Strength vs Speed of Welding, Tool Offset
Contour Plot of Tensile Strength vs Tool Offset, Speed of Tool Ro
160
1.0 Tensile Strength < 105 105 – 110 110 – 115 115 – 120 120 – 125 125 – 130 > 130
150 140 Speed of Welding
Tensile Strength < 105 105 – 110 110 – 115 115 – 120 120 – 125 125 – 130 > 130
130 120 110 100
Tensile Strength < 105 105 – 110 110 – 115 115 – 120 120 – 125 125 – 130 > 130
0.8
Tool Offset
Contour Plot of Tensile Strength vs Speed of Tool Ro, Speed of Welding
0.6
0.4
90 0.2
80
800
70 0.0
70
80
90
100
110
120
Speed of Welding
130
140
150
160
0.0
0.2
0.4
0.6
Tool Offset
0.8
1.0
800
900
1000
1100
1200
1300
1400
Speed of Tool Rotation
Fig. 10. (a) Contour plot of TS vs TRS, WS; (b) Contour plot of TS vs WS, TO; (c) Contour plot of TS vs TO, TRS.
Please cite this article as: M. Sen, S. shankar and S. Chattopadhyaya, Investigations into FSW joints of dissimilar aluminum alloys, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.218
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S urface Plot of Tensile Strength vs Speed of Tool R o, Speed of Weldin g
S urface Plot o f Tensile Strength vs Speed of Welding, Tool Offset
S urface Plot of Tensile Strength vs Tool Offset, Speed of Tool R o
130
130
130
T en si l e S t r en g t h 120
T en si l e Strength 1 2 0
T ensile S t r en g t h 1 2 0
1400
110
110
150
1 .0
110
12 0 0 100
1 000 50 100
S peed o f W eldin g
800 150
Speed o f T o o l R otation
100
1 00 S peed o f W el d i n g
0 .0
0 .5 800
0 .5
T ool O ff set
100
1 .0
50
1 000
1 2 00
1400
T o ol O ff set
0.0
S peed of T o o l R otation
Fig. 11. (a) Surface plot of TS vs TRS, WS; (b) Surface plot of TS vs WS, TO; (c) Surface plot of TS vs TO, TRS.
6. Conclusions Assembled of unlike AA5083 and AA6061 aluminum alloys successfully done applying Friction stir welding technique (FSW). The succession conclusions have been appeared from the current research study In present research work FSW process parameters are optimized in respect of Tensile strength. The optimum level shows at1000rpm TRS, WS 63 mm/min and TO 0.0mmgives better joint property, when we optimize respect to tensile strength. In the present work, the interaction plot is done using MINITAB 17 and results are analyzed. Fig. 10(a) shows the interaction plot among speed of rotation and speed of welding with respect to tensile strength. Plot showed that at slow rotation of 1000 rpm of tool with 160 mm/min WS gives higher tensile strength. Fig. 10 (b) shows the interaction plot among speed of welding and tool offset with respect to tensile strength. Plot showed that at very high speed of 160 mm/min WS and 0.4 mm tool offset gives higher tensile strength. Fig. 10 (c).shows the interaction plot among speed of rotation and tool offset with respect to tensile strength. Plot showed that at slow rotation of 1000 rpm of tool with 0.4 mm tool offset gives higher tensile strength. A plot of 3D surface clarifies the maximum value and graph nature considering two variables at a time and tensile strength. The variables considered for such analysis are speed of tool rotation, speed of welding and tool offset.
Fig. 11 (a) shows that at 60 mm/min WS and 1000 rpm TRS gives highest tensile strength, while Fig. 11 (b) shows that at 150 mm/min WS and tool offset 0.0 mm gives higher value of tensile strength and Fig. 11 (c) shows that TRS 800 rpm and tool offset 1.0 mm gives higher value.
Acknowledgement The authors would acknowledge thanking IIT (ISM) Dhanbad for giving opportunities to carry out experiments. References [1] M. Shariq, M. Srivastava, R. Tripathi, S. Chattopadhyaya, P. Vilaca, N. Gubeljak, G. Krolczyk, Mater. Test. 60 (2018) 707–718. [2] M. Shariq, M. Srivastava, R. Tripathi, S. Chattopadhyaya, A.R. Dixit, Procedia Eng. 149 (2016) 465–471. [3] S. Malopheyev, I. Vysotskiy, V. Kulitskiy, S. Mironov, R. Kaibyshev, Mater. Sci. Eng. A 662 (2016) 136–143. [4] Y. Li, L.E. Murr, J.C. McClure, Mater. Sci. Eng. A 271 (1999) 213–223. [5] M. Koilraj, V. Sundareswaran, S. Vijayan, S.R. Koteswara Rao, Mater. Des. 42 (2012) 1–7. [6] V. RajKumar, M. VenkateshKannan, P. Sadeesh, N. Arivazhagan, K. Devendranath, Ramkumar, Procedia Eng. 75 (2014) 93–97. [7] S.M. Bayazid, H. Farhangi, A. Ghahramani, Procedia Mater. Sci. 11 (2015) 6–11. [8] M.M.Z. Ahmed, S. Ataya, M.M. El-Sayed Seleman, H.R. Ammar, E. Ahmed, J. Mater. Process. Technol. 242 (2017) 77–91. [9] J. Tarraf, S. Mustapha, M.A. Fakih, M. Harb, H. Wang, G. Ayoub, R. Hamade, J. Mater. Process. Technol. 255 (2018) 570–583.
Please cite this article as: M. Sen, S. shankar and S. Chattopadhyaya, Investigations into FSW joints of dissimilar aluminum alloys, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.218
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Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Investigations into FSW joints of dissimilar aluminum alloys Meghnath Sen, Sachindra shankar, Somnath Chattopadhyaya Department of Mechanical Engineering, IIT (ISM), Dhanbad, India
a r t i c l e
i n f o
Article history: Received 6 August 2019 Accepted 27 September 2019 Available online xxxx Keywords: Friction stir welding process Unlike aluminum joint Process parameters Taguchi L9 optimization ANOVA Mechanical properties
a b s t r a c t Joining technique Friction stir welding (FSW) widely accepted for developing new joints of like and unlike metals for wide applications. In present experimental study, two dissimilar aluminium alloys, AA 5086 and AA6061 of 6 mm thickness are welded together using FSW. Nine experiments carried out on a vertical milling machine using a suitable fixture with strong holding aluminium plates on it. Experiments are executed by varying process parameters, which includes speed of tool rotation, welding speed and offset of tool at three levels each. Experiments are performed as per design of experiment using Taguchi’s L9 orthogonal array. Mechanical properties tensile strength is tested and compared with base metal properties. Optimum levels are determined with the help of a statistical optimization tool, ANOVA. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International conference on Materials and Manufacturing Methods.
1. Introduction In preceding year’s global trend takes a diversion to the lightweight production for more performance, environment friendly, better mobility and cheaper cost. Combining conventional metal with new metals offers a good technical properties regarding to a single material. Multi- material components joining is a challenge in the field of manufacturing science, which leads to the new developments of wide range of welding processes to join dissimilar metals. On the Earth’s surface Aluminum (Al) is the third largest element available. Aluminum has low density with attractive strength, higher corrosion resistance, high ductility, good workability, good recyclability and low electrical resistance. Recently joining of different dissimilar metals achieves a huge success in the field of weight reduction. A unique solid phase joining science, Friction stir welding process (FSW) is capable to assemble aluminum alloys with a sound quality weld joint. An innovative joining process at solid state, FSW was designed and developed at ‘The Welding Institute’ (TWI) in United Kingdom (UK) in the year 1991. This process joins two materials below melting point by fusing them and high temperature is induced by the tool due to rotation. The Friction stir welding (FSW) gives little distortion with high strength of joint compare to other welding process. Eminent efficiency of joint comes from higher processing speeds.
Friction stir processing technique is rapidly acquired as a advance welding practice, performed at solid state for joining wide range of applications [1,2] and multiple materials likes different grade of aluminum alloys, copper alloys , brass, titanium, steel, magnesium, metal matrix components and so on without voids, creaks or very less distortion, which are challenging to weld applying fusion methods. It has received a great deal of attention all over the world in a small lap of time after its developments due to its environment friendly, hazard free outputs, low distortion welding and high quality welding product. In FSW pressure and deformation plays a key role at a significant portion of bonding. Due to solid state welding defects commonly found in fusion welding are evaded. Therefore, reduction in inspection of weld and elimination of re-weld procedure provides a significant benefit in costing. Friction stir technique is a fully mechanized welding development use to produce assembled components with the help of rotation and non –consumable weld tool to the soften material strongly clamped at job table through produce of heat by generating friction among FSW tool and base metal work piece, which starts plastic work [3]. A non-consumable specialized FSW tool considered as a main member of the FSW Process. The tool contains a shoulder of particular shaped with a unique profile tool pin. Selection of the material and geometry of the tool based on the base material to be weld, base material specifications, material dimensions, type of joint to be performed and so on. Both lap joint and butt joint can be performed in this process.
https://doi.org/10.1016/j.matpr.2019.09.218 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International conference on Materials and Manufacturing Methods.
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good quality and low cost. Taguchi methods focus on best performance design of manufacturing process and product. It addresses quality in the area of ‘‘off-line quality control” and ‘‘on-line quality control”. It involves three steps for designing performance of a product-
Li et al. (1999) successfully joined AA 2024 and AA6061 alloy using FSW and showed that flow microstructure consists of dynamically recrystallized grains and during welding temperature rise to 60% to 80% of melting temperature. At speed variation from low to high up to 1200 rpm, there was a change in grain structure. At higher speed larger grain structure formed [4]. Koilraj et al. [5] applied Taguchi technique in his work to note the optimal process parameters of dissimilar Aluminum alloy 2219 and AA 5083 joint. L16 orthogonal array is used to inspect optimal parameters of the process based upon the tensile strength value of experimented weld joint. Along this there was a consideration that cylindrical threaded tool is best among all FSW tool. Author showed that optimum value at speed of tool rotation at 700 rpm, speed of welding 15 mm/min and D/d ratio 3 can gave best result of FSW. Rajkumar et al. [6] joined dissimilar aluminum alloys AA5052 and AA6061 together using a cylindrical pin with a constant speed of tool rotation at710 rpm with a fixed feed rate of 20 and 28 mm/min. Both of the metals joined successfully, while cylindrical threaded pin plays a key role for excellent bonding. Both of the samples had good tensile strength, better ductility and fine grain structure. Bayazid ei al. [7] joined Aluminum 6063 and aluminum 7075 alloy together using FSW process and H13 tool steel in present research work. A royal Taguchi approach is considered with L9 OA to find out the optimum process parameters followed by ANOVA analysis for significance of weld parameters. At weld speed 120 mm/min, rotational speed 1600 rpm it gave maximum tensile strength 143.59 MPa. ANOVA indicated the effectiveness of process variables on tensile strength were 59%, 30% and 7%. Ahmed et al. [8] studied joining of dissimilar aluminum alloy , AA5083 and AA7075 with a constant TRS of 300 rpm with altered WS of 50, 100,150 and 200 mm/min. Joints were successfully performed without defect. There was fine grain structure in the NZ. Author showed that increasing of weld speed lead to reduction of grain size. Other mechanical properties like hardness, weld strength were good. Hardness value lies in between 245 and 267 MPa and joint efficiency was 77 to 87%. Tarraf et al. [9] investigated three different similar and dissimilar aluminum joint using FSW with the application of ultrasonic sound waves. AA6061 –AZ31, AA6060AA7020 and AA7020-AA7020aluminum alloys were welded together with speed of tool rotation 1000, 1200and 1200 rpm and speed of welding at 300, 800 and 150 mm/min. All welding were successfully performed. Author concludes that there was no need of guided waves in the case of similar aluminum metal joining, but there were some effect on dissimilar metal joining. Several experiments carried out using different aluminum alloys from 1XXX to 7XXX series. But there is very less work on the joining of dissimilar alloy of AA5XXX to 6XXX. There is a scope of optimize welding parameters with the help of Taguchi approach for better performance, good mechanical properties, less cost and less time. A study with parameter optimization of FSW (AA5083 and AA6061) using Taguchi technique and ANOVA analysis is presented. Also influences of parameters for the process on the tensile strength of the joint were analyzed.
System or design of concept Design of parameter and Design of tolerance. Although it has a better ability to solve single response problems, there are some limitations also. The design of parameters using Taguchi method cover following steps
Proper selection of quality character to be optimized. Selection of no. of levels. Matrix experiment design. Conduct the designed matrix experiment. Experimental result analysis with the help of S/N ratio and ANOVA. Determination of optimal process parameters. Prediction of performance value. Verification of predicted value with a confirmation test. 2.1. Orthogonal array(OA) selection Orthogonal array selection basically depends upon three parameters Number of factors. Number of levels. and Experimental resolution or limitations of cost. In this research work three level designs and three factors are selected, as a result L9 OA is preferred for study and investigation. For each factor the degree of freedom i.e D.O.F = 2
2. Taguchi method Best quality starts with the engineering of quality that leads to the product and process design optimization for best performance,
Fig. 1. FSW Tool.
Table 1 Chemical properties and composition of AA 5083 and AA 6061. Element
Mg
Fe
Si
Cu
Mn
Cr
Ti
Al
AA 5083 AA 6061
4.31 1.08
0.25 0.17
0.156 0.63
0.06 0.32
0.58 0.52
0.1 0.1
0.017 0.02
Remainder Remainder
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M. Sen et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 2 Parameter values for the existing process and its three levels.
Fig. 2. Symmetric diagram of fixture.
As Degree of freedom (D.O.F) = (levels no. 1), i.e DOF = (3–1) = 2. So total degree of freedom i.e D.O.F = (3 2) = 6. Universally the degree of freedom (D.O.F) of OA must not be lesser than the total degree of freedom (D.O.F) of all factors. As D.O.F for L9 is 8, so it is appropriate for present study. 3. Experimental methodology Welding is performed through Taguchi technique using MINITAB 17. Here three factors speed of tool rotation, welding speed and tool offset are investigated. A vertical milling machine used in the present study to weld the Nine (09) sets of Aluminum plates. In order to obtain better weld quality, welding parameters likes TRS, WS and tool offset length has been varied and rest of welding parameters are kept constant. In present study AA 5083 aluminum alloy and AA 6061 aluminum alloy choose as starting metals for joining using Friction stir welding technique. Base metal pieces of dimension 100 mm (length) 100 mm (width) 6 mm (thick) were cut from the large sheets. Aluminum 6061 and Aluminum 5083 is very light metal, has better corrosion resistance to common atmosphere. It has high reflectivity and it retain toughness at very low temperature. Aluminum 6061 and Aluminum 5083 is a good conductor of heat and electricity and it is non-toxic and easily recycled. AA 5083 and AA 6061aluminum alloys are commonly used in the field of transportation sector, construction sector where greater mechanical behavior and properties; like tensile strength,
Level
A Speed of Tool Rotation (rpm)
B Speed of Welding (mm/min)
C Tool offset (mm)
Level 1 Level 2 Level 3
710 1000 1400
63 100 160
0.0 0.5 1.0
hardness etc are truly required. The Chemical composition (Table 1) of materials plays a vital aspect in the process of welding. A threaded profiled H13 steel tool (Fig. 1) is considered and used in this process. The tool has 24 mm shoulder diameter with Pin diameter 5 mm and length 6 mm. H13 tool has higher strength with low wear resistance and good thermal fatigue. According to the work piece dimensions a fixture is designed and developed for holding the work piece plates (Figs. 2 and 3). For the experimentation, Taguchi L9 approach has been considered. Three different parameters- speed of tool rotation (TRS); welding speed (WS) and tool offset are elected and experimental work carried out. The parameters selected for welding with various levels are accustomed in Table 2. The values of welding parameters are chosen as per availability of range of values in vertical milling machine. Plunge depth is kept constant for all the experiments. Fig. 4 shows different weld plates with the effect of parameters on welding surface. Sample number S5, S4, S3, S2, S1, S8 and S7 have better visual surface finish (Shown in Fig. 4) as well as good strength. 4. Experimental results Nine samples for tensile test are prepared utilizing wire cut EDM machine. They were prepared with the direction placed perpendicular to the direction of welding, so that zones of welding can be placed in the middle of the sample specimen. The sample specimen is prepared as per ASTM E8. The 100 mm length (L) specimen has 6 mm width (W) and thickness (T) with30 mm grip length (B), 32 mm reduce section length (A), 19 mm width of grip section(C) and 6 mm fillet radius(R). A Symmetric diagram of sample and an actual sample after cut are shown in Fig. 5. The tensile test was performed on the universal testing machine (UTM), manufactured by Hounsfield (H50Ks). The basic procedure of testing starts with holding the specimen sample in between one fixed crosshead and a movable crosshead. After that load is applied
Fig. 3. FSW set up with fixture.
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Fig. 4. Welding joints using FSW.
Fig. 5. (a) Symmetric sketch of tensile test specimen; (b) Sample after cutting by wire cut EDM.
Fig. 6. Testing in UTM.
and at initial condition ‘Neck formation’ takes place , which lead to breaking of the sample specimen as shown in Fig. 6. After performed the tensile test values of tensile strength of each specimen are listed in Table 3.
where, Yi ¼ Tensileforcevalueforthe0 i0 nooftest, ‘n’ = No. of tests or experiments and ‘N’ = Total number of data. The data of experiment are transferred into S/N ration and mean value (Fig. 7).
5. Investigation on experimental value.
5.2. Analysis of variance (ANOVA)
5.1. Signal to noise (S/N) ratio
The objective of using ANOVA is to figure out the importance of parameters for a existing process statistically. It shows a clear view on the response affected by process parameters and its level of significance. In this research ANOVA table is calculated based on S/N ratio and mean values. The parameters values for the process and its three levels are elaborated in Table 2.
S/N ratio approach followed in Taguchi method to calculate the character of quality. It can be divided into four stages- ‘nominal is best’ (2 stages), ‘smaller is better’ and ‘larger the better’. Since study objective is to find and maximize tensile strength and optimize parameters for the process, ‘larger is better’ characteristic of quality is employed in present study. For calculating S/N ratio the formula used is – n S 1 X 1 ¼ 10log10 N N i¼1 Y2i
ð1Þ
5.3. Analysis of experimental data. The data of tensile strength is investigated to show the outcomes of parameters for the process. The experimental values are convinced into S/N ratio and mean value using popular software
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M. Sen et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 3 Experimental data from tensile test experiments. Sample No
Speed of Tool Rotation (RPM)
Welding Speed (mm/min)
Tool Offset (mm)
Tensile strength (MPa)
S1 S2 S3 S4 S5 S6 S7 S8 S9
710 710 710 1000 1000 1000 1400 1400 1400
63 100 160 63 100 160 63 100 160
0.0 0.5 1.0 0.5 1.0 0.0 1.0 0.0 0.5
126.8 113.2 125.3 131.5 104.3 134.0 106.1 110.4 102.7
Fig. 7. Specimens earlier and later tensile test.
Table 4 Experimental layout –L9 OA, S/N ratio and mean value (Tensile strength). No
1 2 3 4 5 6 7 8 9
A
B
C
Speed of Tool Rotation
Speed of Welding
Tool offset
1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3
1 2 3 2 3 1 3 1 2
MINITAB 17. After calculation, S/N ratio and mean values are tabulated in Table 4, which named as ‘experimental layout –L9 OA, S/N ratio and mean value for tensile strength. The larger value of S/N ratio and larger mean values corresponding to better quality (Larger to better) characteristics for Tensile strength are tabulated on the Table 5and 6. Based on
S/N ratio
Mean Tensile Strength (Mpa)
42.0624 41.0769 41.9590 42.3785 40.3657 42.5421 40.5143 40.8594 40.2314
126.8 113.2 125.3 131.5 104.3 134.0 106.1 110.4 102.7
the calculated values of Tables 5 and 6, Figs. 8 and 9 after optimum level setting, it is obtained that A2B1C1 give optimum setting for tensile strength. Here A2 shows average mean value of speed of tool rotation at 2nd level, B1 shows average mean value of speed of welding at 1st level and C1 shows average mean value for tool offset at 1st level. After optimization, optimum level setting shows
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¼ ð123:3 þ 121:5 þ 123:7 2 117:144Þ ¼ 134:212MPa
Table 5 Table of response for S/N ratios (Tensile strength).
Where, T = overall mean value of tensile strength, A2 = average mean value for speed of tool rotation at 2nd level, B1 = average mean value for speed of welding at 1st level and C1 = average mean value for tool offset at 1st level.
Larger is better Level
1 2 3 Delta Rank
A
B
C
Speed of Tool Rotation (rpm)
Speed of Welding (mm/min)
Tool offset (mm)
41.70 41.76 40.54 1.23 1
41.65 40.77 41.58 0.88 2
41.82 41.23 40.95 0.87 3
After getting the optimum level for design process parameters, final step leads to verify that how much improvement is done in quality characteristics using the optimal value. For this speed of tool rotation set at 1000 rpm, speed of welding set at 63 mm/min and tool offset set at 0 mm. After FSW using this parameters tensile test predicted value (134.212 MPa) was very close to the actual test value (136.1Mpa). Regarding the confirmation test, it is observed that error among actual value and value of prediction for maximum tensile strength is very less 1.387%.
Table 6 Table of response for Means (Tensile Strength). Larger is better Level
1 2 3 Delta Rank
A
B
C
Speed of Tool Rotation (rpm)
Speed of Welding (mm/min)
Tool offset (mm)
121.8 123.3 106.4 16.9 1
121.5 109.3 120.7 12.2 2
123.7 115.8 111.9 11.8 3
5.6. ANOVA analysis.
that at 1000 rpm of speed of tool rotation with 63 mm/min of speed of welding and 0.0 mm of tool offset gives the maximum shear force. The graph of main effects of S/N ratio and means for tensile strength are plotted in Figs. 8 and 9. The setting for optimal parameters based upon S/N ratio and mean is A2B1C1. 5.4. Estimation of optimum parameter value for tensile force. Based upon the value of experiments, the level of optimum setting parameters is A2B1C1. The average parameters values at their level are appropriated from the Tables 6 and 3. The value of the response for tensile strength after prediction is -
Tensile strength value ðPredictedÞ ¼ A2 þB1 þ C1 2T
5.5. Test of Confirmation.
ð2Þ
ANOVA analysis is tool, selected to analyze the % contribution for parameters of a existing process. Responses using ANOVA technique presented in the Tables 7 and 8. Tables 7 and 8 presents the ANOVA result for tensile strength of the S/N ratio and means value. Table 7 shows that % contribution among all parameters. The highest % contribution is observe for the speed of tool rotation from all the parameters and maximum % value is 43.49% followed by speed of welding, which has % of contribution 21.92%. Among all the process tool offset has the lowest % contribution value 18.16%. Table 8 shows that % contribution among all parameters. The highest % contribution is observed for the speed of tool rotation from all the parameters and maximum % value is 43.33% followed by speed of welding, which has % of contribution 23.03%. Among all the process tool offset has the lowest % contribution value 18.08%. A 3-dimentional surface plotting of tensile strength versus speed of tool rotation and speed of welding and tool offset clearly shows that higher tensile strength of the joint obtained at a lower welding speed with a moderate speed of tool rotation and a smaller tool offset (Fig. 10 and 11). When values continuously changes, there is a small variation in the tensile strength. This variation is taken place due to generation of heat at the time of welding. All 3D plots and contour plots describe the variations.
Fig. 8. Main effects plot for S/N ratio based upon tensile strength.
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Fig. 9. Main effects plot for means based upon tensile strength.
Table 7 Analysis of variance (ANOVA) for Tensile strength (SNRA). Source
DOF
Seq SS
Contribution
Adj SS
Adj MS
F-Value
P-Value
A B C Error Total
2 2 2 2 8
2.865 1.444 1.196 1.082 6.588
43.49% 21.92% 18.16% 16.43% 100.00%
2.865 1.444 1.196 1.082
1.4327 0.7220 0.5981 0.5412
2.65 1.33 1.11
0.274 0.428 0.475
DOF – Degree of freedom, Adj SS – Adjusted summation of square, F – Ratio of Feisher, Adj MS – Adjusted square of mean, P – value of Probability (exceeds 95% confidence level), Seq SS – sequential summation of squares.
Table 8 Analysis of variance (ANOVA) for Tensile strength (Means). Source
DOF
Seq SS
Contribution
Adj SS
Adj MS
F-Value
P-Value
A B C Error Total
2 2 2 2 8
522.9 277.9 218.2 187.7 1206.6
43.33% 23.03% 18.08% 15.55% 100.00%
522.9 277.9 218.2 187.7
261.43 138.93 109.09 93.83
2.79 1.48 1.16
0.264 0.403 0.462
DOF – Degree of freedom, Adj SS – Adjusted summation of square, F – Ratio of Feisher, Adj MS- Adjusted square of mean, P – value of Probability (exceeds 95% confidence level), Seq SS – sequential summation of squares.
1400
Speed of Tool Rotation
1300
1200
1100
1000
900
Contour Plot of Tensile Strength vs Speed of Welding, Tool Offset
Contour Plot of Tensile Strength vs Tool Offset, Speed of Tool Ro
160
1.0 Tensile Strength < 105 105 – 110 110 – 115 115 – 120 120 – 125 125 – 130 > 130
150 140 Speed of Welding
Tensile Strength < 105 105 – 110 110 – 115 115 – 120 120 – 125 125 – 130 > 130
130 120 110 100
Tensile Strength < 105 105 – 110 110 – 115 115 – 120 120 – 125 125 – 130 > 130
0.8
Tool Offset
Contour Plot of Tensile Strength vs Speed of Tool Ro, Speed of Welding
0.6
0.4
90 0.2
80
800
70 0.0
70
80
90
100
110
120
Speed of Welding
130
140
150
160
0.0
0.2
0.4
0.6
Tool Offset
0.8
1.0
800
900
1000
1100
1200
1300
1400
Speed of Tool Rotation
Fig. 10. (a) Contour plot of TS vs TRS, WS; (b) Contour plot of TS vs WS, TO; (c) Contour plot of TS vs TO, TRS.
Please cite this article as: M. Sen, S. shankar and S. Chattopadhyaya, Investigations into FSW joints of dissimilar aluminum alloys, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.218
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S urface Plot of Tensile Strength vs Speed of Tool R o, Speed of Weldin g
S urface Plot o f Tensile Strength vs Speed of Welding, Tool Offset
S urface Plot of Tensile Strength vs Tool Offset, Speed of Tool R o
130
130
130
T en si l e S t r en g t h 120
T en si l e Strength 1 2 0
T ensile S t r en g t h 1 2 0
1400
110
110
150
1 .0
110
12 0 0 100
1 000 50 100
S peed o f W eldin g
800 150
Speed o f T o o l R otation
100
1 00 S peed o f W el d i n g
0 .0
0 .5 800
0 .5
T ool O ff set
100
1 .0
50
1 000
1 2 00
1400
T o ol O ff set
0.0
S peed of T o o l R otation
Fig. 11. (a) Surface plot of TS vs TRS, WS; (b) Surface plot of TS vs WS, TO; (c) Surface plot of TS vs TO, TRS.
6. Conclusions Assembled of unlike AA5083 and AA6061 aluminum alloys successfully done applying Friction stir welding technique (FSW). The succession conclusions have been appeared from the current research study In present research work FSW process parameters are optimized in respect of Tensile strength. The optimum level shows at1000rpm TRS, WS 63 mm/min and TO 0.0mmgives better joint property, when we optimize respect to tensile strength. In the present work, the interaction plot is done using MINITAB 17 and results are analyzed. Fig. 10(a) shows the interaction plot among speed of rotation and speed of welding with respect to tensile strength. Plot showed that at slow rotation of 1000 rpm of tool with 160 mm/min WS gives higher tensile strength. Fig. 10 (b) shows the interaction plot among speed of welding and tool offset with respect to tensile strength. Plot showed that at very high speed of 160 mm/min WS and 0.4 mm tool offset gives higher tensile strength. Fig. 10 (c).shows the interaction plot among speed of rotation and tool offset with respect to tensile strength. Plot showed that at slow rotation of 1000 rpm of tool with 0.4 mm tool offset gives higher tensile strength. A plot of 3D surface clarifies the maximum value and graph nature considering two variables at a time and tensile strength. The variables considered for such analysis are speed of tool rotation, speed of welding and tool offset.
Fig. 11 (a) shows that at 60 mm/min WS and 1000 rpm TRS gives highest tensile strength, while Fig. 11 (b) shows that at 150 mm/min WS and tool offset 0.0 mm gives higher value of tensile strength and Fig. 11 (c) shows that TRS 800 rpm and tool offset 1.0 mm gives higher value.
Acknowledgement The authors would acknowledge thanking IIT (ISM) Dhanbad for giving opportunities to carry out experiments. References [1] M. Shariq, M. Srivastava, R. Tripathi, S. Chattopadhyaya, P. Vilaca, N. Gubeljak, G. Krolczyk, Mater. Test. 60 (2018) 707–718. [2] M. Shariq, M. Srivastava, R. Tripathi, S. Chattopadhyaya, A.R. Dixit, Procedia Eng. 149 (2016) 465–471. [3] S. Malopheyev, I. Vysotskiy, V. Kulitskiy, S. Mironov, R. Kaibyshev, Mater. Sci. Eng. A 662 (2016) 136–143. [4] Y. Li, L.E. Murr, J.C. McClure, Mater. Sci. Eng. A 271 (1999) 213–223. [5] M. Koilraj, V. Sundareswaran, S. Vijayan, S.R. Koteswara Rao, Mater. Des. 42 (2012) 1–7. [6] V. RajKumar, M. VenkateshKannan, P. Sadeesh, N. Arivazhagan, K. Devendranath, Ramkumar, Procedia Eng. 75 (2014) 93–97. [7] S.M. Bayazid, H. Farhangi, A. Ghahramani, Procedia Mater. Sci. 11 (2015) 6–11. [8] M.M.Z. Ahmed, S. Ataya, M.M. El-Sayed Seleman, H.R. Ammar, E. Ahmed, J. Mater. Process. Technol. 242 (2017) 77–91. [9] J. Tarraf, S. Mustapha, M.A. Fakih, M. Harb, H. Wang, G. Ayoub, R. Hamade, J. Mater. Process. Technol. 255 (2018) 570–583.
Please cite this article as: M. Sen, S. shankar and S. Chattopadhyaya, Investigations into FSW joints of dissimilar aluminum alloys, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.09.218