International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 03, March 2019, pp. 341–352, Article ID: IJMET_10_03_035
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
Scopus Indexed
INVESTIGATIONS ON FRICTION STIR
WELDING PROCESS TO OPTIMIZE THE
MULTI RESPONSES USING GRA METHOD
Bazani Shaik
Research Scholar, Department of Mechanical Engineering,
JNTUA College of Engineering, Ananthapuramu, Andhra Pradesh, INDIA
Dr. G. Harinath Gowd
Professor, Department of Mechanical Engineering,
Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, INDIA
Dr. B. Durga Prasad
Professor, Department of Mechanical Engineering,
JNTUA College of Engineering, Ananthapuramu, Andhra Pradesh, INDIA
ABSTRACT
The research mainly focused on optimizing the multi responses i.e. tensile
strength, impact Strength and elongation while conducting the experiments on
Friction Stir Welding process. With the help of trial experiments and based on the
literature, the following input parameters i.e. tool rotational speed, weld speed and tilt
angle are identified as the most influencing parameters and are used in the current
study. Experiments are carried out using the Taguchi L9 design. Al7075-T651 and
Al6082-T651 aluminium alloys are taken as the parent materials. The tool used is
Taper threaded tool. This FSW uses no filler metal to join two work pieces. A study
has been made while selecting the tool type. Detailed influences are discussed in the
paper. The Grey relational analysis method is applied to optimize the output
responses. Further plots are drawn between the input process parameters and the
output responses. Overall the method finds best in multi-response optimization of FSW
process.
Key words: Aluminium alloys, FSW process, Multi-Response Optimization, Taguchi
based GRA
Cite this Article: Bazani Shaik, Dr. G. Harinath Gowd, Dr. B. Durga Prasad,
Investigations on Friction Stir Welding Process to Optimize the Multi Responses
Using GRA Method, International Journal of Mechanical Engineering and
Technology 10(3), 2019, pp. 341–352.
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Investigations on Friction Stir Welding Process to Optimize the Multi Responses Using GRA
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1. INTRODUCTION
The Friction Stir Welding (FSW) process is very widely used to join the high strength
aluminum alloys as it is very difficult to weld them using fusion welding technique. Another
disadvantage with the Fusion welding process is poor solidification and porosity in the fusion
zone. This FSW process was invented by Thomas et al. in the year 1991. Since then it is
growing its importance in the field of aerospace and automobile industries. The FSW process
is very advantages over other methods as the material subjected to FSW does not melt and
recast, Less distortion, lower residual stresses and almost no welding defects [1]. Also FSW
process completely eliminates the radiation effect and the harmful emissions of gases which
come during the fusion welding process. FSW is also considered as an environmental friendly
and energy efficient process. This process doesnot require any shielding gas. It produces
joints with excellent metallurgical properties in the joint area. This FSW process uses a non
consumable tool which is rotating with shoulder surface and the unique pin design is plunged
in between the abutting surface of the plates to be joined. The base plates are fixed to the
worktable with a specially designed fixture and support back plates. Because of the frictional
heat generated between the rotating tool and the work material, the work material around the
tool pin gets softened which allows the material to flow along the welding line without
reaching its melting point. The FSW tool is designed to serves the following two functions. 1.
Heating the work pieces and 2. To make the material flow along the joining line to produce
the joint. The principle of working is shown in Fig 1.
Figure 1 A Schematic View of Friction Stir Welding [1]
The strength of FSW joint depends on the right combination of input parameters. As the
process is complex, the following parameters (i.e. Rotational Speed, Welding Speed, Axial
force, Tool geometry, pin length, tool shoulder diameter, pin diameter, Tool tilt angle etc)
should be controlled in a proper manner to obtain the acceptable welding joints. So keeping
its growing importance and its suitability to weld the sample of Aluminum alloys, this FSW
process is chosen as the research area. Mainly investigations carried out on Aluminum
alloys7075Al and 7050Al. Using FSW process, both the alloys were butt welded. To find the
optimal process parameters to achieve the best welded joints interms of strength without
compromising the quality and cost, several papers related to the FSW process and the
methodologies published by researchers refereed and the following sections explains the key
points in brief. The dissimilar aluminum alloys of AA2024and AA6061 investigated by using
response surface method on three level factors box Behnken matrix data regression and
graphical analyses done and ANOVA model also checked out. By controlling of welding
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Bazani Shaik, Dr. G. Harinath Gowd, Dr. B. Durga Prasad
speed 40mm/min and rotational speed 500rpm, Tool pin diameter by validation of responses
on tensile strength optimizes error 5% with high accuracy of friction stir welding process
[2].Welding of 0.5 mm thin Al6061-T6 on different welding speeds of 150mm/min and
200mm/min, efficiency joint 74%, elongation 8% and bending value 1800 reduced heat loss
and fixture of close clamping of both sides welding of thin sheets. Heat affected zone and
thermo mechanic affected zone increased with increase of welding speed and observed on thin
sheets distortion is higher welds at lower welding speeds[3]. Dissimilar welding of reinforced
aluminum plates and Tic nanoparticles or done free weld defects and mechanical properties of
elastic modulus and hardness has nano scale and micro scale. The friction stir process of
passes of rotational speed 1500rpm and welding speed 85mm/min and optical, scanning
electron micrographs on EDS analysis. By the addition of nano particle tensile strength and
elongation are slightly increased[4]. TheDissimilar welding is carried out on Al1100 and
Al441 steel plates following the Taguchi design by varying the following parameters tool
offset, depth plunge, rotational speed and welding speed [5].Examined the Microstructural
developments at different zones whenjoining Al1050 and Al.-matrix nano composite with a
rotational speed 1200rpm, welding speed 50mm/min. The tensile strength improved by 128
Mpa and hardness of stir zone by170% geometrical locations and texture components are
varied at stir zone[6]. Investigated SAF2205 and 304 stainless steel fatigue, residual stresses
are used by base model of continuum damage mechanics and specimen width of least 30
%weld sample of residual relaxation due to cutting consideration and Joining’s of Al3003 and
SUS304 steel of fatigue crack propagation and fracture toughness examined temperature of
5000C and dwell time 60s [7,8]. Studied the effects of rotational speed, tilt angle, welding
speed on the tensile strength by carrying out the experiments based on L9 design on the
following alloys Al-5083-H321. The grey relational analysis method is also tested in [9].
After thorough investigations, it is found that the Grey Relational Analysis method is used
very limited by the researchers. Hence an attempt is been made to investigate the FSW
process using GRA method.
2. EXPERIMENTAL WORK
The experimentation was done at the FSW setup available at Annamalai university,
Tamilnadu. The specifications of the machine are shown in the Table 1. and the setup on
which the experiments are carried out is shown in Fig 2.
Table 1. Machine Specifications
Table Size
Workable Area of Table
Drive Motor
Motor Capacity
X-Axis Motor -3KW(Servo)
Y-Axis Motor-3KW(Servo)
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2m Length x 1m Width
1m Length x 1m Width
1RPM to 3000RPM
13KW(Servo)
Range-1mm/min to 1000mm/min
Range-1mm/min to 1000mm/min
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Investigations on Friction Stir Welding Process to Optimize the Multi Responses Using GRA
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Figure 2 Experimental Setup of FSW
The Aluminum alloys Al7075T651 and Al6082T651are chosen as the work materials
because of its increasing importance in the aerospace and automobile industries. The
dissimilar butt welding joints are prepared using the FSW process. The chemical composition
of both the alloys are presented in the Table 2. and its mechanical properties are shown in the
Table 3.
Table 2.The Chemical compositions on Al7075-T651 and Al6082-T651 (percentage of weight)
Elements
Al7075T651
Al6082T651
Si
Fe
Cu
Mn
Mg
Cr
Ni
Zn
Ti
Al
0.12
0.2
1.4
0.63
2.53
0.2
0.004
5.62
0.03
89.26
1.05
0.26
0.04
0.68
0.8
0.1
0.005
0.02
0.01
97.03
Table 3. Mechanical properties of Aluminum Alloys
Al alloy
7075-T651
6082-T651
Tensile Strength (MPa)
220
330
Impact Strength
(J)
15
10
Elongation (%)
17
9
The base plates are cut in to 100mm × 50mm×6mm samples and a total of 9 butt welded
joints are prepared using the FSW setup. Taguchi L9 orthogonal array is used to design the
experiments. Research is carried out on the Taper threaded tool. The tool is specially made
using the M2Grade SHSS is shown in Fig 3. and dissimilar weld position of friction stir
welding shown in Fig 4. The probe length is 6mm. Whereas the diameter of the shoulder is
18mm. The process is controlled with the help of a computer. Edge preparation is done for the
base materials. A dissimilar butt weld is being made by clamping the materials using fixtures
by placing AA7075T651 and AA6082T651 on Advancing Side and Retreating Side
respectively by opting the parameters-rotational speed(RS), Welding Speed(WS), Tilt
Angle(TA) are investigated for pilot study and literature review i.e. Three levels shown in
Table4. The test specimens are prepared according to the ASTM standards. Grey relational
analysis of Taguchi are used on orthogonal array of L9 selection for responses tensile
strength, Elongation, Impact strength are most important moderation quality of butt joint
weld. Grey relational analysis are used for output variables to get maximum values.
Orthogonal array L9 experimental plan given in Table 5. Al7075T651 in advancing side and
Al6082T651 in retreating side for best joining and development of mechanical properties. The
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Bazani Shaik, Dr. G. Harinath Gowd, Dr. B. Durga Prasad
friction stir welding specimens are cut in sections of transverse as per ASTME8. The weld
specimens are appropriately prepared for metallurgical examination and Investigated to
improve micro structures of aluminum alloys..The tensile test specimensTensile test
specimen’s failure of weakest zoneare shown in Fig 7. and specimens of impact strength
shown in Fig 8. The tensile specimens tested at room temperature on 100KN Universal
Testing Machine Model F-100.Tensile strength test results are shown in Table 7. Elongation
percentage are also shown in Table 7.
Table 4 Levels of Process parameters
Sno
1
2
3
Parameters
Rotational
speed
Welding
speed
Tilt angle
Notation
-1
Levels
0
-1
1150
1250
1350
Unit
RS
rpm
WS
mm/min
40
50
60
TA
Degree
1
2
3
Table 5 Experimental plan through L9 orthogonal array
Sno
Rotational Speed(rpm)
1
2
3
4
5
6
7
8
9
-1
-1
-1
0
0
0
1
1
1
Weld
Speed(mm/min)
-1
0
1
-1
0
1
-1
0
1
Tilt Angle(0)
-1
0
1
0
1
-1
1
-1
0
Figure 3 Taper threaded tool
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Figure 4 Dissimilar weld position of friction stir welding
Figure 7 Tensile test specimen’s failure of weakest zone
Figure 8 Specimens of Impact Testing
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Table 7. Average response values of different responses
Sno
1
2
3
4
5
6
7
8
9
Rotational
Speed
rpm
1150
1150
1150
1250
1250
1250
1350
1350
1350
Weld
Speed
mm/min
40
50
60
40
50
60
40
50
60
Tilt
Angle
Degree
1
2
1
2
2
2
3
3
3
Tensile
Strength
Mpa
171.5
175.39
168.51
160.04
161.82
151.29
162.58
164.23
157.8
Impact
Strength
Joules
9.38
12
10.87
11.25
12
9.18
13
14.8
10
Elongation
%
9.44
10.11
9.33
5.64
8.14
5.14
10
10.78
10.14
The Table 7. gives the measured output responses i.e. Tensile strength, Impact strength
and Elongation. The tensile strength is maximum when the rotational speed is 1150 rpm, weld
speed is 50mm/min and tilt angle is 2degree. At this combination the joint shows more
strength. Whereas the impact strength is 12 joules and elongation is 10.11 Percent. The
Impact strength is maximum when the rotational speed is 1350 rpm, weld speed is 50mm/min
and tilt angle is 3 degrees. The maximum elongation is at the point where rotational speed is
1350 rpm, weld speed is 50mm/min and tilt angle is 3 degrees. It can also be observed that
Impact strength and Elongation are maximum at the same combination. Also the tensile
strength is more than the average value at that combination. Hence the same combination can
also be treated as the best combination obtained from the experimental results. The
microstructures were taken at that combination and are presented in this paper. This can be
further optimized by applying the GRA method and it can be further improvised.
3. TAGUCHI BASED GREY RELATIONAL ANALYSES:
The unknown information process isdetermining system on statistical optimization
Techniques.
The Taguchi method is power full technique for optimization for different engineering
problems.
A characteristics of quality can optimize problem by Technique of Taguchi. The multiple
responses of problem optimize inadequate by Taguchi. The analyzing and solving of
responses multiple for engineering problems. Grey relational analysis of Taguchi is best
method. In 1982 Deng suggest GRA analyzes incomplete or unknown information on
outcome of different responses of parameters.
The steps of GRA Technique as Follows:

Grey relational generation is also called as original data of normalization.

Grey relational coefficient compute.

Grey relational grade compute.

Finding optimum sequence.

Analyses of variance.

Optimal prediction of Grey relational grade.

Experiment confirmation performance.
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3.1. Grey relational generation
The analysis of preprocessing data is also called as generation of grey relation. The relational
sequence of data composed values of experiments are form in comparable sequence interval 0
and 1. The three different characteristics quality applied i.e. nominal is better, larger is better,
smaller is better most part performed. The original sequence is minimizing ‘smaller the better
‘characteristic used normalize sequence reference.
Selected quality characteristics type is ‘large is better’ with calculation of grey relational
generation Equation (1)
(1)
Although
of series max
as well as min
is max together with min series
of values,
after data processing generation of sequence.
i=1,2,3….m and k=1,2,3…. n, data of experimental is n and experiments of m.
3.2. Coefficient of grey relation
The Normalizing process on coefficient of grey relational is calculated to identify relationship
between comparability sequence and reference sequence.
Grey relational coefficients calculated corresponding variations considering Equations (2)
and (3).
|
|
(2)
(3)
Where
series of variation and series of relation
series of interference
, identification coefficient, which generally 0.5 parameters are weightage equal.Grey
relational coefficient experiment are calculated by orthogonal array of L9 of equation (3).
3.3. Grey relational grade
For compatibility series and reference series grey relation grade performed for calculate
strength relationship and values are 0 and 1. The GRG has better relation for higher value.
Generally, for calculating grey relation grade is grey relational coefficient average summation
of equation (4).
∑
(4)
Although is grey relational grade execution of characteristics of number n experiment
ith. The normalized or ideal value are closer to experimental results and correspondence to
GRG of larger value.
3.4. Parameter prediction for optimal value
The effects of different parameters are calculated and best response of grey relational grade
are closer as a 1 and optimal welding condition of parameters has highest mean GRG value.
3.5. ANOVA Performance
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Statistical significance of different parameters for performance of ANOVA probability p
value is used and contribution of parameter response can resolute results of ANOVA.
3.6. Prediction parameters for optimal level
The optimal level of different parameters for resolution of prediction of GRG value by using
equation (5)
∑
(5)
Although
is mean total of GRG,q is parameters number ,
optimal level qth parameter.
is GRG mean value of
4. SIGNAL-TO-NOISE RATIO
Table 4 shows responses of average values on input parameter settings. The responses in three
parameters calculated by signal-to-noise ratio. Higher values of Tensile strength, Elongation,
Impact strength gives better concert on welding.
The equation (6) is a signal-to-noise ratio of calculation.
Signal-to-noise ratio
( )∑
(6)
Although experimental reproduction of n number and Yijk variable response of ith
characteristic execution of experiment jth experiment with trail kth.
Figure 9 Plot for Tensile Strength of signal-to-noise-ratio
The process parameters of each level for calculation of signal-to-noise-ratio value is
considered rotational speed RS 1250 rpm, welding speed WS 50 mm/min and tilt angle TA
20are better characteristics performance of S/N ratios shown in Fig. 9.
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Figure 10 Plot for Elongation of signal-to-noise-ratio
The process parameters of each level for calculation of signal-to-noise-ratio value is
considered rotational speed RS 1350 rpm, welding speed WS 50mm/min and tilt angle TA
30are better characteristics performance of S/N ratios shown in Fig. 10.
Figure 11 Plot for Impact Strength of signal-to-noise-ratio
The process parameters of each level for calculation of signal-to-noise-ratio value is
considered rotational speed RS 1250 rpm, welding speed WS50mm/min and tilt angle TA
20are better characteristics performance of S/N ratios shown in Fig. 11.
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5. CONCLUSIONS
The following conclusions are drawn from the research carried out on FSW process.

Multi responses i.e.tensile strength, impact Strength and elongation were optimized applying
the method of Taguchi based Grey relational analysis.

Two dimensional plots are drawn and analyzed to find the exact relationships between the
input process parameters and the output responses.

Taguchi based L9 is used to carry out the experiments by varying the process parameters
i.e.rotational speed, welding speed and tilt angle.

The tensile strength is maximum 175.39 Mpa, when the rotational speed is 1150 rpm, weld
speed is 50mm/min and tilt angle is 2degree.

The Impact strength is maximum 14.8J, when the rotational speed is 1350 rpm, weld speed is
50mm/min and tilt angle is 3 degrees.

The maximum elongation 10.78% is at the point where rotational speed is 1350 rpm, weld
speed is 50mm/min and tilt angle is 3 degrees.

It can also be observed that Impact strength and Elongation are maximum at the same
combination. Also the tensile strength is more than the average value at that combination.
Hence the same combination can also be treated as the best combination obtained from the
experimental results.

SN Plots drawn and analyzed the results using GRA technique. Overall the GRA method finds
suitable for multi response optimization of FSW process
REFERENCES
[1]
Z. Y. Ma, A. H. Feng, D. L. Chen & J. Shen (2017): Recent Advances in Friction Stir
Welding/Processing of Aluminum Alloys: Microstructural Evolution and Mechanical
Properties, Critical Reviews in Solid State and Materials Sciences,
DOI:10.1080/10408436.2017.1358145.
[2]
K. Jagathesh, M.P. Jenarthanan, P. Dinesh Babu & C. Chanakyan (2016): Analysis of
factors influencing tensile strength in dissimilar welds of AA2024 and AA6061 produced
by Friction Stir Welding (FSW), Australian Journal of Mechanical Engineering,
DOI:10.1080/14484846.2015.1093229.
[3]
Shuja Ahmed, Probir Saha, Development and testing of fixtures for friction stir welding of
thin aluminum sheets, Journal of Materials Processing Tech.252 (2018) 242–248.
[4]
D. A. Dragatogiannis, E. P. Koumoulos, I. Kartsonakis, D. I. Pantelis, P. Karakizis & C.
A. Charitidis (2015): Dissimilar Friction Stir Welding Between 5083 and 6082 Al Alloys
Reinforced with Tic Nanoparticles, Materials and Manufacturing Processes,
DOI:10.1080/10426914.2015.1103856.
[5]
H. A. Derazkola, H.J. Aval and M. Elyasi, Analysis of process parameters effects on
dissimilar friction stir welding of AA1100 and A441 AISI steel, Science and Technology
of Welding and Joining, Institute of Materials, Minerals and Mining,
DOI:10.1179/1362171815Y.0000000038.
[6]
F. Khodabakhshi, A. Simchi, A. H. Kokabi, A. P. Gerlich, M. Nosko & P. Švec (2016):
Influence of hard inclusions on microstructural characteristics and textural components
during dissimilar friction-stir welding of an PM Al–Al2O3–SiC hybrid nanocomposite
http://www.iaeme.com/IJMET/index.asp
351
editor@iaeme.com
Investigations on Friction Stir Welding Process to Optimize the Multi Responses Using GRA
Method
with AA1050 alloy, Science and
DOI:10.1080/13621718.2016.1251714.
Technology
of
Welding
and
Joining,
[7]
Zhang, W., Jiang, W., Zhao, X., Tu, S-T., Fatigue life of a dissimilar welded joint
considering the weld residual stress: Experimental and finite element simulation,
International Journal of Fatigue (2018),
doi:https://doi.org/10.1016/j.ijfatigue.2018.01.002.
[8]
Hidehito Nishida, Tomo Ogura, Ryoichi Hatano, Hirotaka Kurashima, Misuo Fujimoto &
Akio Hirose (2016): Fracture toughness and fatigue crack behavior of A3003/SUS304 lap
friction stir welded joints, Welding International, DOI:10.1080/09507116.2016.1223206.
[9]
Jitender Kundu & Hari Singh (2016) Friction stir welding: multi-response optimization
using Taguchi-based GRA, Production & Manufacturing Research, 4:1, 228-241, DOI:
10.1080/21693277.2016.1266449.
[10]
Bazani Shaik, Dr.G. Harinath Gowd and Dr.B. Durga Prasad, Experimental Investigations
On Friction Stir Welding Process to Join Aluminum Alloys, International Journal of
Applied Engineering Research ISSN 0973-4562 Volume 13, Number 15 (2018) pp.
12331-12339.
[11]
Bazani Shaik, G. Harinath Gowd and B. Durga Prasad, An Optimization and Investigation
Of Mechanical Properties and Microstructures On Friction Stir Welding Of Aluminium
Alloys, International Journal of Mechanical and Production Engineering Research and
Development (IJMPERD), ISSN(P): 2249-6890; ISSN(E): 2249-8001, Vol. 9, Issue 1,
Feb 2019, 227-240.
http://www.iaeme.com/IJMET/index.asp
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editor@iaeme.com