Parametric Optimisation of Friction-Stir-Spot-Welded Al 6061-T6 Incorporated with Silicon Carbide Using a Hybrid WASPAS–Taguchi Technique

Abstract

Friction stir spot welding (FSSW) is one of the most popular fusion joining processes. The process is a solid-state welding process that allows welding of weldable as well as non-weldable materials. As a part of this investigation, weld samples of Al6061-T6 were reinforced with silicon carbide (SiC) powder with an average particle size of 45 µm. Initially, a Taguchi L9 orthogonal array was developed with three factors, i.e., rotational speed of the tool, pre-dwelling time, and diameter of the hole that was filled with SiC before welding. The effects of the SiC particles and process parameters were investigated as tensile–shear load and micro-hardness. The optimisation of parameters in order to maximise the output responses—i.e., strength and hardness of the welded joints—was performed using a hybrid WASPAS–Taguchi method. The optimised process parameters obtained were a 3.5 mm guiding hole diameter, 1700 rpm tool rotation speed, and 14 s of pre-dwelling time.

Full text

Citation: Chaudhary, N.; Singh, S.;
Garg, M.P.; Garg, H.K.; Sharma, S.; Li,
C.; Tag Eldin, E.M.; El-Khatib, S.
Parametric Optimisation of
Friction-Stir-Spot-Welded Al 6061-T6
Incorporated with Silicon Carbide
Using a Hybrid WASPAS–Taguchi
Technique. Materials 2022,15, 6427.
https://doi.org/10.3390/
ma15186427
Academic Editors: Emanuela Cerri
and Bolv Xiao
Received: 17 July 2022
Accepted: 9 September 2022
Published: 16 September 2022
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materials
Article
Parametric Optimisation of Friction-Stir-Spot-Welded Al
6061-T6 Incorporated with Silicon Carbide Using a Hybrid
WASPAS–Taguchi Technique
Neeru Chaudhary 1, Sarbjit Singh 1, Mohinder Pal Garg 2, * , Harish Kumar Garg 2, Shubham Sharma 3, 4, * ,
Changhe Li 4, Elsayed Mohamed Tag Eldin 5 ,* and Samah El-Khatib 5
1Department of Mechanical Engineering, Punjab Engineering College (Deemed to be University),
Chandigarh 160012, India
2Department of Mechanical Engineering, DAV University, Jalandhar 144001, India
3
Mechanical Engineering Department, University Center for Research & Development, Chandigarh University,
Mohali 140413, India
4School of Mechanical and Automotive Engineering, Qingdao University of Technology,
Qingdao 266520, China
5Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt
*Correspondence: mpgargacad@gmail.com (M.P.G.); shubham543sharma@gmail.com or
shubhamsharmacsirclri@gmail.com (S.S.); elsayed.tageldin@fue.edu.eg (E.M.T.E.)
Abstract:
Friction stir spot welding (FSSW) is one of the most popular fusion joining processes. The
process is a solid-state welding process that allows welding of weldable as well as non-weldable
materials. As a part of this investigation, weld samples of Al6061-T6 were reinforced with silicon
carbide (SiC) powder with an average particle size of 45
µ
m. Initially, a Taguchi L9 orthogonal
array was developed with three factors, i.e., rotational speed of the tool, pre-dwelling time, and
diameter of the hole that was filled with SiC before welding. The effects of the SiC particles and
process parameters were investigated as tensile–shear load and micro-hardness. The optimisation of
parameters in order to maximise the output responses—i.e., strength and hardness of the welded
joints—was performed using a hybrid WASPAS–Taguchi method. The optimised process param-
eters obtained were a 3.5 mm guiding hole diameter, 1700 rpm tool rotation speed, and 14 s of
pre-dwelling time.
Keywords: friction stir spot welding; SiC microparticles; MCDM; WASPAS; Taguchi
1. Introduction
In industries such as the automotive and aerospace sectors, reduction in fuel utilisation
and the emission of harmful gases is one of the priorities for years to come. This can be
achieved by using lightweight materials, such as aluminium and its alloys, instead of
traditional iron-based alloys. Amongst various joining techniques, initially, resistance
spot welding (RSW), laser welding, etc., were employed for joining aluminium and its
alloys [
1
,
2
]. However, these welding processes have many disadvantages, such as porosity,
cracking, severe wear of the electrode tip, etc. As a result, various industries were on the
lookout for alternative methods, and the automobile industry came up with a process
known as friction stir spot welding (FSSW). FSSW is a most promising method for joining
various materials, because it does not add weight to the material to be joined, and is also a
cost-effective method [
3
,
4
]. FSSW starts with plunging the rotating tool into the workpieces,
and after reaching a predefined depth the tool rotates and stirs the material in the stirring
zone. During stirring, the temperature of the workpiece material rises due to friction, and
reaches a value where the workpiece becomes soft and plastically deformed. When this
process is ideally employed, the mixing of the workpiece material takes place without any
significant change in its phase and microstructure, proving that the process is a solid-state
Materials 2022,15, 6427. https://doi.org/10.3390/ma15186427 https://www.mdpi.com/journal/materials

Materials 2022,15, 6427 2 of 22
joining process [
5
]. This can be attributed to the fact that the melting of the material leads
to coarsening of the grains as grain boundaries break down during melting, and when the
material cools down, those grains combine to form bigger grains, affecting the material
properties. However, during the FSSW process, the material only deforms plastically, and
finer grains are formed. A schematic of the FSSW process is shown in Figure 1. The quality
of the weld produced by the FSSW process depends on various parameters, such as plunge
depth, tool rotation speed, dwell time, plunge rate, tool configuration, etc.
Materials 2022, 15, x FOR PEER REVIEW 2 of 23
due to friction, and reaches a value where the workpiece becomes soft and plastically
deformed. When this process is ideally employed, the mixing of the workpiece material
takes place without any significant change in its phase and microstructure, proving that
the process is a solid-state joining process [5]. This can be attributed to the fact that the
melting of the material leads to coarsening of the grains as grain boundaries break down
during melting, and when the material cools down, those grains combine to form bigger
grains, affecting the material properties. However, during the FSSW process, the material
only deforms plastically, and finer grains are formed. A schematic of the FSSW process is
shown in Figure 1. The quality of the weld produced by the FSSW process depends on
various parameters, such as plunge depth, tool rotation speed, dwell time, plunge rate,
tool configuration, etc.
Figure 1. Schematic of the FSSW process: (a) placing reinforcement in a predrilled hole; (b)
pre-dwelling time; (c) plunging and stirring; (d) retracting.
Many research investigations have been carried out on the effects of process pa-
rameters on the output characteristics of welded joints, such as micro-hardness, tensile–
shear load, etc. Uematsu et al. [6] described an inverse relationship of tool rotation speed
and tool holding time with respect to tensile–shear strength, i.e., tensile–shear strength
improved with the decrease in tool rotation speed and increase in tool holding time.
However, cross-tension strength was inversely proportional to both. Gerlich et al. [7] also
made a similar observation about tool rotation speed and strain rate, i.e., the weld
strength increased with the decrease in tool rotation speed and increase in strain rate. In
addition, they observed that size of the welded area did not change at higher tool rotation
speeds. Conversely, Lathabai et al. [8] and Yuan et al. also [9] noted a bell-shaped curve
between weld strength and tool rotation speed. Hence, higher tool rotation speed re-
Figure 1.
Schematic of the FSSW process: (
a
) placing reinforcement in a predrilled hole; (
b
) pre-
dwelling time; (c) plunging and stirring; (d) retracting.
Many research investigations have been carried out on the effects of process parameters
on the output characteristics of welded joints, such as micro-hardness, tensile–shear load,
etc. Uematsu et al. [
6
] described an inverse relationship of tool rotation speed and tool
holding time with respect to tensile–shear strength, i.e., tensile–shear strength improved
with the decrease in tool rotation speed and increase in tool holding time. However, cross-
tension strength was inversely proportional to both. Gerlich et al. [
7
] also made a similar
observation about tool rotation speed and strain rate, i.e., the weld strength increased with
the decrease in tool rotation speed and increase in strain rate. In addition, they observed
that size of the welded area did not change at higher tool rotation speeds. Conversely,
Lathabai et al. [
8
] and Yuan et al. also [
9
] noted a bell-shaped curve between weld strength
and tool rotation speed. Hence, higher tool rotation speed resulted in lower weld strength.
This may be attributed to the increased heat input with the increase in the tool rotational
speed, leading to coarsening of the grains in the weld zone, which resulted in reduced weld
strength [10].

Materials 2022,15, 6427 3 of 22
During FSSW, the tool experiences thrust forces while plunging into the workpiece,
leading to wear of the tool and damage to the microstructure of the workpiece material.
Hence, to reduce this damage and improve the performance of the process, researchers
have investigated different methods of preheating workpieces before starting the plunging
stage [
11
]. Shen et al. [
12
] used resistance heating rods for preheating, and observed an
increase in the bonding area and reduction in the number of voids. Another investigation
was carried out by Shen et al. [
13
] on the effects of preheating and different configurations of
dissimilar aluminium–magnesium (Al–Mg) workpieces on weld strength. It was concluded
that weld strength was improved with preheating in the case of Mg as the upper workpiece
because of increased bond width, whereas in the case of Al as the upper workpiece, a
decrease in weld strength was observed because of increased intermetallic compounds as
compared to having Mg on top. Another method of preheating—i.e., heating with induction
coil—was used by Sun et al. [
14
], and an enhancement in weld strength was observed.
Hence, it can be stated that preheating assists in improving the mechanical properties of
FSSW. However, the processes used for preheating the FSSW require special equipment,
making the process cumbersome and costly. Therefore, this issue needs consideration, in
order to provide a technique to preheat the weld in a simple and cost-effective manner.
It can be concluded from the literature that improving weld strength is priority, and in
order to achieve this, the incorporation of reinforcements in the weld zone during FSSW
has been found to be a reliable option. Initially, friction stir processing was used to alter
the surface properties of materials such as brass, aluminium, etc., using nano- and micro-
reinforcement particles. The use of these particles resulted in a substantial reduction in the
grain size of the welded samples [
15
–
17
]. After that, the technique was used for improving
the performance of FSSW. Researchers have reported different studies on the effects of
distribution of reinforcements such as B
4
C, SiC, etc., combined with other parameters such
as tool rotation speed, dwell time, etc., on the mechanical and microstructural properties
of FSSW [
18
]. Among various reinforcements, SiC is considered a valuable contender
because of its properties, such as lower thermal expansion coefficient, higher melting
point, etc. Investigations have been carried on FSSW of aluminium alloys [
19
], magnesium
alloys [
20
], copper [
21
], and other materials reinforced with silicon carbide during welding,
and researchers have observed an increase in weld strength as well as the micro-hardness
of welds, with homogeneous distribution of SiC particles in the welded area. Hence, it can
be concluded that the incorporation of SiC particles in FSSW is a prominent method of
enhancing the weld properties. However, the incorporation of SiC particles alone is not
enough; their homogeneous distribution in the welded area is also a major contributor.
This can be achieved by proper stirring and mixing of the SiC particles with plastically
deformed workpiece materials by selecting appropriate process parameters.
With regards to the above discussion, it is clear that tool rotation speed, along with pre-
heating or pre-dwelling time, helps in achieving material flow by generating the required
amount of heat via friction, which can help in homogeneous mixing of reinforcements in the
welded area, and serves the purpose of improving weld performance. However, selecting
the right combination of levels of these parameters is a complicated task. Hence, optimisa-
tion of parameters is necessary to simplify the selection of parameters to obtain improved
results. Therefore, various mathematical and statistical models have been developed in
order to reduce the human resources and time consumed during experimentation [
22
].
Acharya et al. [
23
] welded dissimilar materials using FSSW and optimised the process
parameters using the Taguchi method. Bozkurt and Bilici [
24
] also used the Taguchi method
to optimise the process parameters to weld dissimilar aluminium alloys. An attempt was
made by Bilici et al. [
25
] to optimize FSSW tool materials and process parameters using the
Taguchi method. Meanwhile, Pradhan et al. [
26
] attempted a hybrid RSM–WASPAS–grey
wolf technique to determine the optimal process parameters for dissimilar FSSW.
The Taguchi method is a robust statistical tool that is frequently used for optimising
and analysing industrial processes. The Taguchi method can be applied for cost-effective
system design, and it also helps to understand the impact of individual and combined

Materials 2022,15, 6427 4 of 22
process parameters [
25
]. Meanwhile, multi-criteria decision-making (MCDM) has proven to
be a dynamic decision-making method that considers several factors in order to choose the
appropriate process parameters. Hence, both techniques have proven to be better and more
economical techniques for optimisation. However, there is limited literature available on
the optimisation of the FSSW process using MCDM or hybrid MCDM–Taguchi techniques.
This paper presents a combined technique of the Taguchi method and multi-criteria
decision making (MCDM)—i.e., weighted aggregated sum product assessment (WASPAS)—
to optimise process parameters and analyse the results obtained at optimal values. The best-
suited hybrid MCDM–Taguchi model—i.e., the WASPAS–Taguchi model—was established
for understanding the effects and importance of process parameters on output quality
characteristics, i.e., tensile–shear load and micro-hardness.
2. Materials and Methods
2.1. Workpiece Material and Process Parameters
In the present study, commercial grade Al6061-T6 with 120 mm
×
30 mm
×
3 mm
dimensions was chosen as the workpiece material, and measured test specimens were
prepared using a diamond cutter. The weld specimens were retained in fixtures with
an overlap of 30
×
30 mm
2
, as shown in Figure 2. Silicon carbide (SiC) particles with
an average particle size of 45
µ
m were used for reinforcement. The reinforcement was
positioned in predrilled guide holes before starting the welding process. Different diameters
of the guiding holes—e.g., 2.5, 3.0, and 3.5 mm—were used to vary the quantity of SiC. In
addition to the quantity of reinforcements, the tool rotational speed, pre-dwelling time,
dwell time, etc., were taken as process parameters, the values of which are shown in Table 1.
Materials 2022, 15, x FOR PEER REVIEW 4 of 23
and process parameters using the Taguchi method. Meanwhile, Pradhan et al. [26] at-
tempted a hybrid RSM–WASPAS–grey wolf technique to determine the optimal process
parameters for dissimilar FSSW.
The Taguchi method is a robust statistical tool that is frequently used for optimising
and analysing industrial processes. The Taguchi method can be applied for cost-effective
system design, and it also helps to understand the impact of individual and combined
process parameters [25]. Meanwhile, multi-criteria decision-making (MCDM) has proven
to be a dynamic decision-making method that considers several factors in order to choose
the appropriate process parameters. Hence, both techniques have proven to be better and
more economical techniques for optimisation. However, there is limited literature avail-
able on the optimisation of the FSSW process using MCDM or hybrid MCDM–Taguchi
techniques.
This paper presents a combined technique of the Taguchi method and multi-criteria
decision making (MCDM)—i.e., weighted aggregated sum product assessment
(WASPAS)—to optimise process parameters and analyse the results obtained at optimal
values. The best-suited hybrid MCDM–Taguchi model—i.e., the WASPAS–Taguchi
model—was established for understanding the effects and importance of process pa-
rameters on output quality characteristics, i.e., tensile–shear load and micro-hardness.
2. Materials and Methods
2.1. Workpiece Material and Process Parameters
In the present study, commercial grade Al6061-T6 with 120 mm × 30 mm × 3 mm
dimensions was chosen as the workpiece material, and measured test specimens were
prepared using a diamond cutter. The weld specimens were retained in fixtures with an
overlap of 30 × 30 mm
2
, as shown in Figure 2. Silicon carbide (SiC) particles with an av-
erage particle size of 45 µm were used for reinforcement. The reinforcement was posi-
tioned in predrilled guide holes before starting the welding process. Different diameters
of the guiding holes—e.g., 2.5, 3.0, and 3.5 mm—were used to vary the quantity of SiC. In
addition to the quantity of reinforcements, the tool rotational speed, pre-dwelling time,
dwell time, etc., were taken as process parameters, the values of which are shown in Ta-
ble 1.
Figure 2. Test specimen configuration.
Table 1. Process parameters and their levels.
Sr. No. Process Parameters Level 1 Level 2 Level 3
1. Guiding Hole Diameter (mm) 2.5 (G
1
) 3.0 (G
2
) 3.5 (G
3
)
2. Tool Rotation Speed (rpm) 1300 (T
1
) 1700 (T
2
) 2100 (T
3
)
3. Pre-Dwelling Time (s) 6 (P
1
) 10 (P
2
) 14 (P
3
)
Figure 2. Test specimen configuration.
Table 1. Process parameters and their levels.
Sr. No. Process Parameters Level 1 Level 2 Level 3
1. Guiding Hole Diameter (mm) 2.5 (G1) 3.0 (G2) 3.5 (G3)
2. Tool Rotation Speed (rpm) 1300 (T1) 1700 (T2) 2100 (T3)
3. Pre-Dwelling Time (s) 6 (P1) 10 (P2) 14 (P3)
4. Dwell Time (s) 20 20 20
5. Plunge Rate (mm/min) 15 15 15
6. Plunge Depth (mm) 4.8 4.8 4.8
2.2. Tools and Methods
The experimentation was carried out at Siemens’ Center of Excellence in Manufactur-
ing in a computer-numerical-controlled vertical machining centre, as shown in Figure 3.
The FSSW tool used was made of high-carbon high-chromium steel with an average hard-

Materials 2022,15, 6427 5 of 22
ness of HRC 58, and with the shoulder having a concavity angle of zero. The tool pin
was square-shaped, 4.8 mm long, and 5 mm in diameter (d), with grooves on it, while
the tool shoulder was 16 mm in diameter (D), as shown in Figure 4a,b. Micro-hardness
and tensile–shear load were calculated as response outputs. The welded samples were
fractured on a universal testing machine (UTM, Fuel Instruments & Engineers Pvt. Ltd.,
Tal, India) (model UTE 40 HGFL with 40 KN load cell) at a speed of 1 mm/min by grad-
ually increasing the load, and the corresponding tensile–shear load was measured. The
experiments were repeated thrice in order to maintain the accuracy of the measured output
quality characteristics. Additionally, the micro-hardness of the weld samples was mea-
sured using the Vickers micro-hardness measuring device (model HV 1000 B) at load of
100 g for a dwell time of 20 s. The macrostructural and microstructural behaviour of the
welded joint was examined with distinct tools and techniques, such as scanning electron
microscopy (SEM, model: JEOL JSM-IT500 LV with direct magnification of 5
×
to 300,000
×
),
stereo-zoom microscopy (Stemi 508 with magnification capacity between 2
×
and 250
×
),
energy-dispersive spectroscopy (EDS, model Oxford instruments Ultim Max with element
detection range of Be (4)–Am (95)), and optical microscopy (OM, model Zeiss Axiocam
ICc 1) etc. Sample preparation for the study was prepared by polishing samples with
different emery papers and then polishing them with Brasso using a velvet cloth to obtain
a scratchless surface. Temperature measurement was carried out using a K-type thermo-
couple and a data logger. Thermocouples were placed in four pre-drilled holes in the
workpieces, highlighted in yellow in Figure 4c, and the obtained temperature readings
were recorded using the universal data logger (Figure 3d). The optimisation model was
developed using MS Excel.
Materials 2022, 15, x FOR PEER REVIEW 5 of 23
4. Dwell Time (s) 20 20 20
5. Plunge Rate (mm/min) 15 15 15
6. Plunge Depth (mm) 4.8 4.8 4.8
2.2. Tools and Methods
The experimentation was carried out at Siemens’ Center of Excellence in Manufac-
turing in a computer-numerical-controlled vertical machining centre, as shown in Figure
3. The FSSW tool used was made of high-carbon high-chromium steel with an average
hardness of HRC 58, and with the shoulder having a concavity angle of zero. The tool pin
was square-shaped, 4.8 mm long, and 5 mm in diameter (d), with grooves on it, while the
tool shoulder was 16 mm in diameter (D), as shown in Figure 4a,b. Micro-hardness and
tensile–shear load were calculated as response outputs. The welded samples were frac-
tured on a universal testing machine (UTM, Fuel Instruments & Engineers Pvt. Ltd., Tal,
India) (model UTE 40 HGFL
with 40 KN load cell) at a speed of 1 mm/min by gradually
increasing the load, and the corresponding tensile–shear load was measured. The ex-
periments were repeated thrice in order to maintain the accuracy of the measured output
quality characteristics. Additionally, the micro-hardness of the weld samples was meas-
ured using the Vickers micro-hardness measuring device (model HV 1000 B) at load of
100 g for a dwell time of 20 s. The macrostructural and microstructural behaviour of the
welded joint was examined with distinct tools and techniques, such as scanning electron
microscopy (SEM, model: JEOL JSM-IT500 LV with direct magnification of 5× to
300,000×), stereo-zoom microscopy (Stemi 508 with magnification capacity between 2×
and 250×), energy-dispersive spectroscopy (EDS, model Oxford instruments Ultim Max
with element detection range of Be (4)–Am (95)), and optical microscopy (OM, model
Zeiss Axiocam ICc 1) etc. Sample preparation for the study was prepared by polishing
samples with different emery papers and then polishing them with Brasso using a velvet
cloth to obtain a scratchless surface. Temperature measurement was carried out using a
K-type thermocouple and a data logger. Thermocouples were placed in four pre-drilled
holes in the workpieces, highlighted in yellow in Figure 4c, and the obtained temperature
readings were recorded using the universal data logger (Figure 3d). The optimisation
model was developed using MS Excel.
Figure 3. Experimental setup: (a) CNC milling machine; (b) FSSW tool; (c) fixture and thermocou-
ple arrangement; (d) data logger.
Figure 3.
Experimental setup: (
a
) CNC milling machine; (
b
) FSSW tool; (
c
) fixture and thermocouple
arrangement; (d) data logger.
2.3. Design of Experiments
The Taguchi method has proven to be a simple and efficient tool for designing experi-
ments that have applications in various areas. This technique helps in reducing the cost
of research by using an optimal orthogonal array, without affecting the efficiency of the
process. Since the present research consisted of three input parameters with three levels,
an L9 orthogonal array (OA) design was designated according to the Taguchi design of

Materials 2022,15, 6427 6 of 22
experiments. The experimental design according to the L9 orthogonal array is shown in
Table 2.
Materials 2022, 15, x FOR PEER REVIEW 6 of 23
Figure 4. (a) Schematic of the FSSW tool; (b) 3D view of the FSSW tool; (c) thermocouple positions.
2.3. Design of Experiments
The Taguchi method has proven to be a simple and efficient tool for designing ex-
periments that have applications in various areas. This technique helps in reducing the
cost of research by using an optimal orthogonal array, without affecting the efficiency of
the process. Since the present research consisted of three input parameters with three
levels, an L9 orthogonal array (OA) design was designated according to the Taguchi de-
sign of experiments. The experimental design according to the L9 orthogonal array is
shown in Table 2.
Table 2. Process parameters and experimental results.
Expt. No. Guiding Hole
Diameter (mm)
Tool Rotation
Speed (rpm)
Preheating Time
(s)
Tensile–Shear Load
(N) ± ST Dev.
Micro-Hardness (Hv 0.1)
± ST Dev.
1. 2.5 1300 6 4297.67 ± 13.78 86.50 ± 2.01
2. 2.5 1700 10 4562.79 ± 7.38 89.00 ± 1.25
3. 2.5 2100 14 4436.22 ± 10.85 88.70 ± 2.01
4. 3 1300 10 4480.55 ± 5.04 88.80 ± 1.47
5. 3 1700 14 4649.79 ± 3.04 90.30 ± 1.06
6. 3 2100 6 4392.25 ± 9.31 87.90 ± 1.43
7. 3.5 1300 14 4702.05 ± 7.44 91.02 ± 0.68
8. 3.5 1700 6 4688.10 ± 7.09 90.80 ± 1.86
9. 3.5 2100 10 4621.06 ± 9.07 89.60 ± 1.90
3. Results and Discussions
3.1. Temperature Profile
The temperature was monitored at four different sites; two of them were placed in
the centre line of the guiding hole in the upper workpiece (T1), while the other two were
placed in the lower workpiece (T2) at a distance of 5 mm on either side of T2. The tem-
perature was monitored as soon as the pre-dwelling stage began, and it was found that
Figure 4. (a) Schematic of the FSSW tool; (b) 3D view of the FSSW tool; (c) thermocouple positions.
Table 2. Process parameters and experimental results.
Expt. No. Guiding Hole
Diameter (mm)
Tool Rotation
Speed (rpm)
Preheating Time
(s)
Tensile–Shear Load
(N) ±ST Dev.
Micro-Hardness
(Hv 0.1) ±ST Dev.
1. 2.5 1300 6 4297.67 ±13.78 86.50 ±2.01
2. 2.5 1700 10 4562.79 ±7.38 89.00 ±1.25
3. 2.5 2100 14 4436.22 ±10.85 88.70 ±2.01
4. 3 1300 10 4480.55 ±5.04 88.80 ±1.47
5. 3 1700 14 4649.79 ±3.04 90.30 ±1.06
6. 3 2100 6 4392.25 ±9.31 87.90 ±1.43
7. 3.5 1300 14 4702.05 ±7.44 91.02 ±0.68
8. 3.5 1700 6 4688.10 ±7.09 90.80 ±1.86
9. 3.5 2100 10 4621.06 ±9.07 89.60 ±1.90
3. Results and Discussions
3.1. Temperature Profile
The temperature was monitored at four different sites; two of them were placed in the
centre line of the guiding hole in the upper workpiece (T1), while the other two were placed
in the lower workpiece (T2) at a distance of 5 mm on either side of T2. The temperature was
monitored as soon as the pre-dwelling stage began, and it was found that the temperature
rose significantly during the pre-dwelling phase due to friction that occurred between the
tool and the upper surface of the workpiece, as well as the SiC particles. After that, the tool
plunged into the workpieces, and the temperature continued to rise as a result of friction
between the rotating tool and the stirred material. After reaching the highest temperature
(peak) possible during the stirring stage, the temperature began to fall gradually until it
reached room temperature after retraction of the tool.

Materials 2022,15, 6427 7 of 22
Different temperature profiles were obtained with different combinations of process
parameters. The temperature profiles obtained during FSSW of the Al6061-T6 welds are
shown in Figure 5. It can be observed from the curves that the temperature obtained with
a 2.5 mm guiding hole diameter, 1300 rpm tool rotation speed, and 6 s of pre-dwelling
time was 379
◦
C, while a temperature of 442
◦
C was obtained for the weld produced with
a 3.5 mm guiding hole diameter, 1300 rpm tool rotation speed, and 14 s of pre-dwelling
time. However, the weld produced with a 3.5 mm guiding hole diameter, 2100 rpm
tool rotation speed, and 10 s of pre-dwelling time reached the highest temperature, at
460
◦
C. The temperature curves of the different thermocouples indicate that the increase
in temperature at the point near the upper surface of the upper workpiece was greater
than that at the other three points. This may be explained by the fact that the material
directly below the tool shoulder, also known as point T1, suffered plastic deformation
over a wider region, which led to a greater temperature increase when compared to the
other sites. When the tool first made contact with the workpiece, the temperature curve
exhibited some fluctuation for a short period of time, but after that, the curve rose smoothly,
and continued to do so until the tool started plunging. During plunging of the tool in
the workpieces, the temperature profile showed fluctuations, and the observations were
consistent with those of Ilman et al. [
27
]. The authors are of the opinion that the FSSW
process is similar to the drilling process, which involves plunging of a rotating tool into
the material of the workpiece and the generation of unequal and significant forces; hence,
the fluctuation in the temperature profile was obtained until the stirring stage. However,
after the tool reached the predetermined depth, the fluctuation reduced, and a smooth
curve with peak temperature was attained, after which the temperature declined after the
retraction of the tool.
The effect of pre-dwelling time on temperature can clearly be seen from the tempera-
ture profiles. As the pre-dwelling time increased from 6 s (Figure 5a) to 10 s (Figure 5b),
and then to 14 s (Figure 5c), the temperature attained by the end of this stage also increased
due to prolonged friction. This rise in temperature increased the heat input and made it
easier to plunge the tool into the workpieces; hence, comparatively less fluctuation can be
seen in Figure 5b,c. Hence, pre-dwelling time assisted in increasing the overall temperature
during the process and obtaining the required heat and material flow. Another finding in
terms of SiC quantity is that the temperature increased with the increase in the guiding
hole’s diameter, i.e., by increasing the quantity of SiC, which may be attributed to the fact
that increasing quantity of SiC enhanced the frictional heating due to the hard nature of
the SiC particles. Another parameter affecting the temperature profile was tool rotation
speed. Frictional heating increased with the increase in the tool’s rotation speed; hence, the
temperature of the workpiece also increased with the increase in the tool’s rotation speed.
It can be concluded from Figure 5that the temperature of the weld (Figure 5a) at a guiding
hole diameter of 2.5 mm was less than that of the other two welds with a guiding hole
diameter of 3.5 mm. The peak temperatures shown in Figure 5b,c do not differ significantly.
Hence, it can be concluded that the guiding hole diameter significantly affected the increase
in temperature in the welds.
3.2. Behavioural Analysis of FSSW Welds in Terms of Tensile–Shear Load
A pictographic demonstration of the steps involved during tensile–shear analysis of
weld samples is shown in Figure 6. The welded samples were made equiaxed by gluing
two parts of equivalent dimensions at the edges of both workpieces in order to hold the
welded samples in the UTM, aligned in a straight line. Then, a progressively increasing
load was applied, and the average of three experimental values of tensile–shear load for
each experiment of the Taguchi L9 orthogonal array was recorded, as shown in Table 2.

Materials 2022,15, 6427 8 of 22
Materials 2022, 15, x FOR PEER REVIEW 8 of 23
Figure 5. Temperature profile of the FSSW welds obtained at (a) 2.5 mm guiding hole diameter,
1300 rpm tool rotation speed, and 6 s of pre-dwelling time; (b) 3.5 mm guiding hole diameter, 1300
rpm tool rotation speed, and 14 s of pre-dwelling time; and (c) 3.5 mm guiding hole diameter, 2100
rpm tool rotation speed, and 10 s of pre-dwelling time.
3.2. Behavioural Analysis of FSSW Welds in Terms of Tensile–Shear Load
A pictographic demonstration of the steps involved during tensile–shear analysis of
weld samples is shown in Figure 6. The welded samples were made equiaxed by gluing
Figure 5.
Temperature profile of the FSSW welds obtained at (
a
) 2.5 mm guiding hole diameter,
1300 rpm tool rotation speed, and 6 s of pre-dwelling time; (
b
) 3.5 mm guiding hole diameter,
1300 rpm tool rotation speed, and 14 s of pre-dwelling time; and (
c
) 3.5 mm guiding hole diameter,
2100 rpm tool rotation speed, and 10 s of pre-dwelling time.

Materials 2022,15, 6427 9 of 22
Materials 2022, 15, x FOR PEER REVIEW 9 of 23
two parts of equivalent dimensions at the edges of both workpieces in order to hold the
welded samples in the UTM, aligned in a straight line. Then, a progressively increasing
load was applied, and the average of three experimental values of tensile–shear load for
each experiment of the Taguchi L9 orthogonal array was recorded, as shown in Table 2.
Figure 6. (a) 9 FSSW specimens; (b) FSSW test sample, (c) test sample secured in the UTM; (d)
fractured sample.
A graphical representation of how the tensile–shear load varied with variation in the
process parameters is shown in Figure 7. It was observed that the tensile–shear load ini-
tially increased and then decreased with increasing tool rotation speed. In order to eval-
uate the effects of different parameters on the tensile–shear load, the mean tensile–shear
load of each parameter in the different experiments was calculated and correspondingly
plotted in graphs, as shown in Figure 7. The tensile–shear load first increased to 4633.56
N at 1700 rpm, and then decreased to 4482.17 N at 2100 rpm. The increase in the tool ro-
tation speed resulted in increased heat input due to friction. This increased heat input
softened the workpieces, facilitating the flow of material and the mixing of the two
workpiece materials. Throughout the process, grain refinement took place due to stirring
of the material in the stir zone (SZ). Consequently, a finer microstructure was achieved in
the SZ, which increased the elongation of joints during tensile–shear testing; hence, the
joints fractured at a higher load. During this process, the workpieces were heated up, and
took longer to cool down, as shown in Figure 5. The temperature increase at 2100 rpm
was greater than at the other two tool rotation speeds. Hence, the greater temperature
increases and consequent delay in the cooling of the material led to growth of the grain
boundaries and consequent coarsening of grains. This reduced the ductility of the joints,
leading to early fracture of the weld. This behaviour of the rotation speed is comparable
to that found in previous investigations [9].
Figure 6.
(
a
) 9 FSSW specimens; (
b
) FSSW test sample, (
c
) test sample secured in the UTM;
(d) fractured sample.
A graphical representation of how the tensile–shear load varied with variation in the
process parameters is shown in Figure 7. It was observed that the tensile–shear load initially
increased and then decreased with increasing tool rotation speed. In order to evaluate the
effects of different parameters on the tensile–shear load, the mean tensile–shear load of
each parameter in the different experiments was calculated and correspondingly plotted
in graphs, as shown in Figure 7. The tensile–shear load first increased to 4633.56 N at
1700 rpm, and then decreased to 4482.17 N at 2100 rpm. The increase in the tool rotation
speed resulted in increased heat input due to friction. This increased heat input softened the
workpieces, facilitating the flow of material and the mixing of the two workpiece materials.
Throughout the process, grain refinement took place due to stirring of the material in the
stir zone (SZ). Consequently, a finer microstructure was achieved in the SZ, which increased
the elongation of joints during tensile–shear testing; hence, the joints fractured at a higher
load. During this process, the workpieces were heated up, and took longer to cool down, as
shown in Figure 5. The temperature increase at 2100 rpm was greater than at the other two
tool rotation speeds. Hence, the greater temperature increases and consequent delay in the
cooling of the material led to growth of the grain boundaries and consequent coarsening of
grains. This reduced the ductility of the joints, leading to early fracture of the weld. This
behaviour of the rotation speed is comparable to that found in previous investigations [
9
].
On the other hand, weld strength considerably improved with an increase in the
guiding hole diameter, i.e., with increase in the amount of SiC particles. An increase in
tensile–shear load from 4432.23 N to 4670.4 N with the increase in the guiding hole’s
diameter from 2.5 mm to 3.5 mm was recorded during our investigation. When the tool
penetrated the workpieces, the reinforcements were mixed with the material in the stir
zone. The presence of SiC particles increased the frictional heat input due to their high
hardness. However, the increase in the heat input did not lead to grain growth in the base
material, because of the lower thermal coefficient of the SiC particles. This property of the
SiC particles prevented their expansion due to the increase in heat input, and also acted as
a barrier to other grains present in the vicinity. Therefore, when the quantity of SiC was
increased, the resistance to grain growth also increased. Hence, a finer microstructure was
obtained in the stir zone, making the FSSW joints stronger. Secondly, grain refinement
occurred due to the higher number of nucleation sites. SiC particles act as nucleation sites;
hence, the higher the number of SiC particles, the higher the number of nucleation sites and
the greater the opposition to grain growth. All of these factors contributed to enhancing the
weld strength and confirmed the positive influence of SiC on FSSW quality. This behaviour
of weld strength with respect to SiC is similar to the findings of previous research [17].

Materials 2022,15, 6427 10 of 22
Materials 2022, 15, x FOR PEER REVIEW 10 of 23
Figure 7. Effects of the process parameters on tensile–shear load.
On the other hand, weld strength considerably improved with an increase in the
guiding hole diameter, i.e., with increase in the amount of SiC particles. An increase in
tensile–shear load from 4432.23 N to 4670.4 N with the increase in the guiding hole’s
diameter from 2.5 mm to 3.5 mm was recorded during our investigation. When the tool
penetrated the workpieces, the reinforcements were mixed with the material in the stir
zone. The presence of SiC particles increased the frictional heat input due to their high
hardness. However, the increase in the heat input did not lead to grain growth in the base
material, because of the lower thermal coefficient of the SiC particles. This property of the
SiC particles prevented their expansion due to the increase in heat input, and also acted
as a barrier to other grains present in the vicinity. Therefore, when the quantity of SiC
was increased, the resistance to grain growth also increased. Hence, a finer microstruc-
ture was obtained in the stir zone, making the FSSW joints stronger. Secondly, grain re-
finement occurred due to the higher number of nucleation sites. SiC particles act as nu-
cleation sites; hence, the higher the number of SiC particles, the higher the number of
nucleation sites and the greater the opposition to grain growth. All of these factors con-
tributed to enhancing the weld strength and confirmed the positive influence of SiC on
FSSW quality. This behaviour of weld strength with respect to SiC is similar to the find-
ings of previous research [17].
Similarly, a direct proportional relationship was witnessed between weld strength
and pre-dwelling time. Weld strength increased from 4459.34 N to 4596.02 N with the
increase in pre-dwelling time from 6 s to 14 s. This was due to the fact that contact time
between the tool pin and the upper workpiece increased with pre-dwelling time, and
increased the heat input due to prolonged friction. This process softened the upper
workpiece and made it easier to plunge the tool into the workpieces and develop suffi-
cient material flow. The induced material flow around the tool pin led to mixing of the
workpieces, and resulted in improved tensile–shear load. These findings have been veri-
fied and justified by previous research related to FSSW of aluminium alloys [12].
3.3. Behavioural Analysis of FSSW Welds in Terms of Micro-Hardness
A pictographic demonstration of the micro-hardness testing of the welded speci-
mens is shown in Figure 8. The FSSW weld was first cut from middle, and then a
cross-section was finished using emery paper. After achieving a good surface finish, the
sample was placed on a micro-hardness tester, and values of micro-hardness at the dis-
tance of 4 mm from the centre of the keyhole—up to 13 mm on one side and 2.0 mm be-
low the top surface—were calculated. The average experimental values of mi-
Figure 7. Effects of the process parameters on tensile–shear load.
Similarly, a direct proportional relationship was witnessed between weld strength and
pre-dwelling time. Weld strength increased from 4459.34 N to 4596.02 N with the increase
in pre-dwelling time from 6 s to 14 s. This was due to the fact that contact time between
the tool pin and the upper workpiece increased with pre-dwelling time, and increased the
heat input due to prolonged friction. This process softened the upper workpiece and made
it easier to plunge the tool into the workpieces and develop sufficient material flow. The
induced material flow around the tool pin led to mixing of the workpieces, and resulted in
improved tensile–shear load. These findings have been verified and justified by previous
research related to FSSW of aluminium alloys [12].
3.3. Behavioural Analysis of FSSW Welds in Terms of Micro-Hardness
A pictographic demonstration of the micro-hardness testing of the welded specimens
is shown in Figure 8. The FSSW weld was first cut from middle, and then a cross-section was
finished using emery paper. After achieving a good surface finish, the sample was placed
on a micro-hardness tester, and values of micro-hardness at the distance of 4 mm from the
centre of the keyhole—up to 13 mm on one side and 2.0 mm below the top surface—were
calculated. The average experimental values of micro-hardness in the three experiments
for each Taguchi L9 orthogonal array are listed in Table 2, and the impact of the process
parameters on micro-hardness is presented in Figure 9. It was observed that micro-hardness
first increased with the tool rotation speed, and then reduced. While a directly proportional
relationship was observed between micro-hardness and both guiding hole diameter and
pre-dwelling time. This can be attributed to the Hall–Petch effect, where the grain size
is inversely proportional to the hardness. According to the Hall–Petch equation [
28
],
HV = H
0
+ k
H
d
−1/2
, where HV is hardness, d indicates grain size, and H
0
and k
H
are the
constants; therefore, it can be seen from the equation that hardness decreases with the
increase in grain size.
As discussed above, the increase in the tool rotation speed and the pre-dwelling time
led to refinement of the grains in the stir zone (SZ). Therefore, enhanced micro-hardness
was observed with the increase in these parameters. However, the value of micro-hardness
first increased with tool rotation speed and then reduced, because of coarsening of the
grains, which occurred due to increased heat input and a slower cooling rate. Moreover, the
increase in hardness of the FSSW welds was mostly affected by presence of reinforcements
in the stir zone, because the micro-hardness of a reinforced weld is correlated with the
size of the grains, the presence of reinforcements, the density of dislocations, and the
heat input [
28
]. The reinforcement particles in the stir zone acted as a hindrance for

Materials 2022,15, 6427 11 of 22
dislocations, and these dislocations were accumulated against the reinforcements. Hence,
the dislocation density increased with the increase in the number of reinforcements in the
stir zone, along with grain refinement that occurred due to pinning of the grain boundaries
by SiC particles, which resulted in increased hardness. The change in hardness between
different regions of the welds at with different process parameters is presented in Figure 10.
It can be observed from the hardness profiles that the stir zone (SZ) had a higher hardness,
which gradually decreased through the thermo-mechanically affected zone (TMAZ) and
attained the minimum value. Thereafter, the hardness increased further toward the base
metal, exhibiting a W-shaped appearance. All hardness profiles showed a higher Vickers
micro-hardness near the keyhole, representing the SZ of dynamically recrystallised fine
grains. The results given in Figure 10 show that the micro-hardness value was not uniform
throughout the SZ. Hence, as per the Hall–Petch equation, tensile–shear strength and
increased micro-hardness, the grain size reduced [
28
]. Fortuitously, the results of the
tensile–shear tests obtained in this work are in excellent accordance with the micro-hardness
profiles. The tensile–shear load and micro-hardness exhibited similar variations in relation
to the process parameters.
Materials 2022, 15, x FOR PEER REVIEW 11 of 23
cro-hardness in the three experiments for each Taguchi L9 orthogonal array are listed in
Table 2, and the impact of the process parameters on micro-hardness is presented in
Figure 9. It was observed that micro-hardness first increased with the tool rotation speed,
and then reduced. While a directly proportional relationship was observed between mi-
cro-hardness and both guiding hole diameter and pre-dwelling time. This can be at-
tributed to the Hall–Petch effect, where the grain size is inversely proportional to the
hardness. According to the Hall–Petch equation [28], HV = H
0
+ k
H
d
−1/2
, where HV is
hardness, d indicates grain size, and H
0
and k
H
are the constants; therefore, it can be seen
from the equation that hardness decreases with the increase in grain size.
Figure 8. (a) FSSW weld; (b) FSSW weld cut in half; (c) micro-hardness tester.
Figure 9. Effects of process parameters on micro-hardness.
As discussed above, the increase in the tool rotation speed and the pre-dwelling time
led to refinement of the grains in the stir zone (SZ). Therefore, enhanced micro-hardness
was observed with the increase in these parameters. However, the value of mi-
cro-hardness first increased with tool rotation speed and then reduced, because of
coarsening of the grains, which occurred due to increased heat input and a slower cool-
ing rate. Moreover, the increase in hardness of the FSSW welds was mostly affected by
presence of reinforcements in the stir zone, because the micro-hardness of a reinforced
Figure 8. (a) FSSW weld; (b) FSSW weld cut in half; (c) micro-hardness tester.
Materials 2022, 15, x FOR PEER REVIEW 11 of 23
cro-hardness in the three experiments for each Taguchi L9 orthogonal array are listed in
Table 2, and the impact of the process parameters on micro-hardness is presented in
Figure 9. It was observed that micro-hardness first increased with the tool rotation speed,
and then reduced. While a directly proportional relationship was observed between mi-
cro-hardness and both guiding hole diameter and pre-dwelling time. This can be at-
tributed to the Hall–Petch effect, where the grain size is inversely proportional to the
hardness. According to the Hall–Petch equation [28], HV = H
0
+ k
H
d
−1/2
, where HV is
hardness, d indicates grain size, and H
0
and k
H
are the constants; therefore, it can be seen
from the equation that hardness decreases with the increase in grain size.
Figure 8. (a) FSSW weld; (b) FSSW weld cut in half; (c) micro-hardness tester.
Figure 9. Effects of process parameters on micro-hardness.
As discussed above, the increase in the tool rotation speed and the pre-dwelling time
led to refinement of the grains in the stir zone (SZ). Therefore, enhanced micro-hardness
was observed with the increase in these parameters. However, the value of mi-
cro-hardness first increased with tool rotation speed and then reduced, because of
coarsening of the grains, which occurred due to increased heat input and a slower cool-
ing rate. Moreover, the increase in hardness of the FSSW welds was mostly affected by
presence of reinforcements in the stir zone, because the micro-hardness of a reinforced
Figure 9. Effects of process parameters on micro-hardness.

Materials 2022,15, 6427 12 of 22
Materials 2022, 15, x FOR PEER REVIEW 12 of 23
weld is correlated with the size of the grains, the presence of reinforcements, the density
of dislocations, and the heat input [28]. The reinforcement particles in the stir zone acted
as a hindrance for dislocations, and these dislocations were accumulated against the re-
inforcements. Hence, the dislocation density increased with the increase in the number of
reinforcements in the stir zone, along with grain refinement that occurred due to pinning
of the grain boundaries by SiC particles, which resulted in increased hardness. The
change in hardness between different regions of the welds at with different process pa-
rameters is presented in Figure 10. It can be observed from the hardness profiles that the
stir zone (SZ) had a higher hardness, which gradually decreased through the ther-
mo-mechanically affected zone (TMAZ) and attained the minimum value. Thereafter, the
hardness increased further toward the base metal, exhibiting a W-shaped appearance. All
hardness profiles showed a higher Vickers micro-hardness near the keyhole, represent-
ing the SZ of dynamically recrystallised fine grains. The results given in Figure 10 show
that the micro-hardness value was not uniform throughout the SZ. Hence, as per the
Hall–Petch equation, tensile–shear strength and increased micro-hardness, the grain size
reduced [28]. Fortuitously, the results of the tensile–shear tests obtained in this work are
in excellent accordance with the micro-hardness profiles. The tensile–shear load and mi-
cro-hardness exhibited similar variations in relation to the process parameters.
Materials 2022, 15, x FOR PEER REVIEW 13 of 23
Figure 10. Micro-hardness profiles of samples obtained at (a) 2.5 mm guiding hole diameter, (b) 3.0
mm guiding hole diameter, and (c) 3.5 mm guiding hole diameter.
4. Optimisation of Process Parameters Using the Hybrid WASPAS–Taguchi
Technique
Multi-criteria decision-making (MCDM) methods have been used to address issues
that require decision-making where there is more than one criterion. There are different
methods that have been used to treat decision-making issues, and one of them is
WASPAS. WASPAS is a multi-objective optimisation-making (MODM) method, which is
one category of MCDM methods, and is used to determine the weight of output charac-
teristics or alternatives, and to rank process parameters to choose the best alternatives
among several. This is a combination of two well-established models—i.e., the weighted
sum model (WSM) and the weighted product model (WPM)—that makes the proposed
solution more stable. This technique works effectively and is compatible with other
methods. WASPAS allows determination of weights according to the importance of each
attribute, which helps in rational decision-making processes [29]. The present paper
presents the optimisation of process parameters using WASPAS—an MCDM tech-
nique—combined with a very popular statistical analysis technique, known as the
Taguchi method. Figure 11 shows the steps involved in the hybrid WASPAS–Taguchi
technique.
Figure 10.
Micro-hardness profiles of samples obtained at (
a
) 2.5 mm guiding hole diameter,
(b) 3.0 mm guiding hole diameter, and (c) 3.5 mm guiding hole diameter.

Materials 2022,15, 6427 13 of 22
4. Optimisation of Process Parameters Using the Hybrid WASPAS–Taguchi Technique
Multi-criteria decision-making (MCDM) methods have been used to address issues
that require decision-making where there is more than one criterion. There are different
methods that have been used to treat decision-making issues, and one of them is WASPAS.
WASPAS is a multi-objective optimisation-making (MODM) method, which is one category
of MCDM methods, and is used to determine the weight of output characteristics or
alternatives, and to rank process parameters to choose the best alternatives among several.
This is a combination of two well-established models—i.e., the weighted sum model (WSM)
and the weighted product model (WPM)—that makes the proposed solution more stable.
This technique works effectively and is compatible with other methods. WASPAS allows
determination of weights according to the importance of each attribute, which helps in
rational decision-making processes [
29
]. The present paper presents the optimisation of
process parameters using WASPAS—an MCDM technique—combined with a very popular
statistical analysis technique, known as the Taguchi method. Figure 11 shows the steps
involved in the hybrid WASPAS–Taguchi technique.
4.1. Step 1: Determination of the Normalised Decision Matrix
The following steps were used to eliminate every irregularity in the experimental
results by changing them to dimensionless quantities: The equation below was used for
normalisation. Hence, the outcomes were obtained in a 0–1 range.
xij =xi j
maxxij
to maximise. (1)
where i= 1, 2, 3
. . . . . .
..., n and j= 1, 2, 3
. . .
..., m; n = the number of criteria or responses
(tensile–shear load and micro-hardness); m = the number of alternatives or the experiment
number, to be ranked (Experiments 1–9). Table 3shows the normalised decision matrix.
Table 3. Normalised decision matrix.
Expt.
No.
Guiding Hole
Diameter
(mm)
Tool Rotation
Speed (rpm)
Preheating
Time (s)
Normalised Value
Tensile–Shear
Load (N)
Micro-Hardness
(Hv 0.1)
1. 2.5 1300 6 0.457 0.475
2. 2.5 1700 10 0.485 0.488
3. 2.5 2100 14 0.472 0.487
4. 3.0 1300 10 0.476 0.487
5. 3.0 1700 14 0.494 0.496
6. 3.0 2100 6 0.467 0.489
7. 3.5 1300 14 0.5 0.5
8. 3.5 1700 6 0.499 0.498
9. 3.5 2100 10 0.491 0.492
4.2. Step 2: Creation of the Performance Matrix by WSM and WPM
WASPAS has been described as the hybrid of two MCDM techniques, i.e., the weighted
sum method (WSM) and the weighted product method (WPM). Hence, the total perfor-
mance matrix is a combination of performance matrices for WSM and WPM. The perfor-
mance matrix for WSM is given as follows [30]:
WSM =Q1
i=∑n
j−1xij ∗wj(2)
The performance matrix for WPM is given as follows:
WPM =Q2
i=∏n
j=1xij wj(3)
where w
j
is the weight of the j
th
alternative (Experiments 1–9). The performance matrix for
WSM and WPM is shown in Table 4.

Materials 2022,15, 6427 14 of 22
Materials 2022, 15, x FOR PEER REVIEW 14 of 23
Figure 11. Flowchart of the hybrid WASPAS–Taguchi technique.
4.1. Step 1: Determination of the Normalised Decision Matrix
The following steps were used to eliminate every irregularity in the experimental
results by changing them to dimensionless quantities: The equation below was used for
normalisation. Hence, the outcomes were obtained in a 0–1 range.
𝑥




= 



to maximise. (1)
where i = 1, 2, 3 ……..., n and j = 1, 2, 3 …..., m; n = the number of criteria or responses
(tensile–shear load and micro-hardness); m = the number of alternatives or the experi-
ment number, to be ranked (Experiments 1–9). Table 3 shows the normalised decision
matrix.
Figure 11. Flowchart of the hybrid WASPAS–Taguchi technique.
Table 4. Performance matrix for WSM and WPM.
Expt. No. Performance Score (Q1
i)Performance Score (Q2
i)
1. 0.932 0.217
2. 0.974 0.237
3. 0.958 0.229
4. 0.964 0.232
5. 0.990 0.245
6. 0.949 0.225
7. 1.000 0.250
8. 0.997 0.248
9. 0.983 0.242
4.3. Step 3: Calculation of Variance
Calculation of variance (
σ2
) is required for the final performance matrix, as errors
occur during determination when the initial values of the criteria are stochastic or have
a random probability distribution. Hence, calculation of
σ2
is needed to determine the
dispersal of outcomes in the distribution. Variance for the WSM and WPM can be calculated
using Equations (4) and (5), respectively, and is shown in Table 5[31–33].

Materials 2022,15, 6427 15 of 22
Table 5. Variance for WSM and WPM.
Expt. No. Variance (σ2Q1
i) Variance (σ2Q2
i)
1. 0.001086 0.001174
2. 0.001186 0.001204
3. 0.001149 0.001226
4. 0.001162 0.001218
5. 0.001226 0.001234
6. 0.001128 0.001205
7. 0.001250 0.001250
8. 0.001243 0.001244
9. 0.001209 0.001213
For WSM:
σ2Q1
i=
n
∑
j=1
σ2(xij )∗w2
j(4)
For WPM:
σ2Q2
i=
n
∑
j=1
σ2(xij )∗"∏n
j=1xij wj.∗wj
xij wj∗xij (1−wj)#2
(5)
where
σ2xij 
is the variance of the normalised decision matrix at a 95% confidence interval,
calculated using Equation (6):
σ2xij =0.05 ∗xij 2(6)
4.4. Step 4: Determination of λi
Likewise, variance
λi
also affects the dispersal of outcomes in the distribution, and it
can be determined using Equation (7) [32,34–36]:
λi=σ2Q2
i
σ2Q1
i+σ2Q2
i(7)
4.5. Step 5: Creating the Final Performance Matrix
The final preference score can be determined using Equation (8), which combines the
preference matrices of WSM and WPM, as shown in Table 6.
Qi=λQ1
i+(1−λ)Q2
i(8)
where
λ
= 0, 0.1,
. . .
,1; here,
λ
= 0 implies that the WASPAS method is converted to the
WPM method, while λ= 1 converts the WASPAS method to the WSM method.
Table 6. λi, Final performance matrix and rank.
Expt. No. Variance (λi) Final Performance Score (Qi) Rank
1. 0.5194 0.5885 9
2. 0.5038 0.6084 5
3. 0.5161 0.6062 7
4. 0.5117 0.6069 6
5. 0.5016 0.6190 3
6. 0.5166 0.5997 8
7. 0.5000 0.6250 1
8. 0.5002 0.6231 2
9. 0.5008 0.6133 4

Materials 2022,15, 6427 16 of 22
4.6. Step 6: Taguchi Analysis of the Final Performance Matrix
Based on the results of the final performance matrix, it was determined that experiment
number 7, obtained under the process parameters G
3
T
1
P
3
, with the maximum final perfor-
mance score, delivered better results in terms of both output responses in comparison with
other experiments. However, the final optimised values were derived using the Taguchi
technique. For analysis using the Taguchi method, the final performance score from Table 6
was used to determine the S/N responses corresponding to the process parameters and
their levels. The process parameter level with the largest S/N ratio, as shown in Table 7,
was considered the optimal process parameter condition. Therefore, the process parame-
ters G
3
T
2
P
3
—i.e., guiding hole diameter of 3.5 mm, tool rotation speed of 1700 rpm, and
pre-dwelling time of 14 s—were considered the optimal process parameters. The difference
between the maximum and minimum S/N ratios of the performance scores was 0.277 for
guiding hole diameter, 0.149 for tool rotation speed, and 0.186 for pre-dwelling time, as
shown in Table 7. These values provide information about factors that highly affect output
characteristics. Therefore, the parameter with the highest numeric value—i.e., guiding hole
diameter—had the greatest effect on the output responses, followed by pre-dwelling time
and tool rotation speed.
Table 7. Response table of S/N ratios (the larger the better).
Level Guiding Hole Diameter Tool Rotation Speed Preheating Time
1−4.422 −4.341 −4.384
2−4.341 −4.196 −4.299
3−4.145 −4.345 −4.198
Delta 0.277 0.149 0.186
Rank 1 3 2
Similarly, analysis of variance (ANOVA) was conducted on the final performance
scores for assessment of significant and non-significant process parameters, and also
to determine the statistical significance of the process parameters with respect to the
weld quality, as shown in Table 8. The level of significance considered for ANOVA was
5%, i.e., 95% confidence level. Moreover, ANOVA provided a clear vision of how the
process parameters influenced the response, along with their level of significance [
22
,
33
,
34
].
Meanwhile, the relative power of each factor was indicated by the percentage contribution
of each parameter, which is a function of sum of squares, in order to reduce the disparity in
the experimental results. Percentage contribution also indicates variation in parameters,
which can be reduced by precisely controlling the levels of the parameters. Hence, ANOVA
showed that the guiding hole diameter had the greatest effect on the response characteristics,
followed by tool rotation speed and pre-dwelling time, with percentage contributions of
52.85%, 19.52%, and 23.45%, respectively. Furthermore, the p-value of each parameter was
less than 0.05, showing that the parameters had a significant effect on the responses. Hence,
a 3.5 mm guiding hole diameter, 1700 rpm tool rotation speed, and 14 s of pre-dwelling
time was found to be the optimal combination of process parameters using the specially
established hybrid WASPAS–Taguchi model.
Table 8. ANOVA analysis of the final performance scores.
Sr. No. Source Sum of Square Degree of Freedom F-Value p-Value Percentage Contribution
1 Guiding Hole Diameter 0.116 2 0.058 0.00225 52.85
2 Tool Rotation Speed 0.043 2 0.022 0.00083 19.52
3 Preheating Time 0.052 2 0.026 0.00100 23.45
4 Error 0.009 20 4.18

Materials 2022,15, 6427 17 of 22
5. Confirmation Test and Comparison of FSSW with and without SiC Particles
Obtained under Optimal Process Parameters
In order to verify the predicted optimal parameters (3.5 mm guiding hole diameter,
1700 rpm tool rotation speed, and 14 s of pre-dwelling time), experimental runs were
carried out under optimal conditions, and both tensile–shear load and micro-hardness
were calculated as output responses. The obtained values of tensile–shear load and micro-
hardness were 5145 N and 101 Hv0.1, respectively, which were greater than the tensile–shear
load and micro-hardness value of the other nine experiments. Hence, the authenticity of
the optimised results obtained via the hybrid WASPAS–Taguchi method was verified.
The morphological analysis of FSSW under optimal parameters was carried out, and
the results are shown in Figure 12. The macrograph of the weld showed a clear SZ and hook
on either side of the keyhole, bent upwards like a mountain. A homogeneous distribution of
SiC particles could be seen from the SEM analysis of the weld, as shown in Figure 12d. The
SiC particles showed complete bonding with the base material, and EDS analysis showed
evidence of their presence and the results are identical with the same [
37
–
39
]. This shows
that the optimal parameters helped in inducing sufficient heat for the flow of the material
and thorough mixing of the reinforcements and the matrix. There were no voids or partially
bonded regions obtained. Hence, a stronger weld was obtained under optimal parameters
as compared to the other nine experiments, confirming the success of the established model
and these results are completely identical with the previous studies [40–42].
Thus, the objective of the present study was to analyse the consequences of adding SiC
particles in conventional FSSW in terms of the mechanical and microstructural behaviour
of the weld. To carry out our investigation, the results of FSSW with SiC were compared
with conventional FSSW, both obtained under optimal conditions. The sample without SiC
gave a tensile-shear load of 4169 N and micro-hardness of 85 Hv0.1; when compared with
the results of welding with SiC, it was evident that the incorporation of SiC particles as
reinforcement increased the weld strength by 23.41% and hardness by 18.8%, as shown in
Figures 13 and 14, respectively. The enhancement of the properties of the weld with SiC was
obtained because of the amazing properties of reinforcement. The low thermal expansion
coefficient of SiC offered resistance to the growth of aluminium grains when surrounded
by SiC particles. In addition, the difference in the thermal expansion coefficients of SiC
and aluminium led to the formation of strain fields or residual stress fields around the SiC
particles during cooling of the weld which is comparable with the existing works [
42
–
44
].
The formation of strain fields led to piling up of dislocations, and when the welded samples
were exposed to tensile–shear loading, the SiC particles and piled-up dislocations acted as
a barricade and prevented crack propagation. Consequently, greater load was required to
break through the SiC particles and accumulated dislocations. The increase in the number
of dislocations occurred due to partial relief of the stresses caused by the different thermal
expansion coefficients of SiC and the aluminium alloy [
35
,
44
,
45
]. Moreover, the SiC particles
acted as a guard, and prevented damage to the aluminium alloy grains which shows similar
findings with the existing works [
46
–
48
]. This process continued until the applied load
increased to a level where the SiC–aluminium interface was damaged. It can be inferred
that the fracture of the joint first started with damage to the SiC–aluminium interface, and
then the crack propagated through the rest of the base material [
36
,
49
,
50
]. Furthermore,
the increase in micro-hardness was due to the reduced grain size, as SiC particles help
in obtaining a fine grain structure and, according to the Hall–Petch effect, hardness is
inversely proportional to grain size; therefore, the micro-hardness of the weld with SiC
particles was greater than that of the conventional weld. Additionally, the hardness of
SiC particles is greater than that of grains of aluminium, which increased the combined
hardness of the composite made in the SZ during welding.

Materials 2022,15, 6427 18 of 22
Materials 2022, 15, x FOR PEER REVIEW 19 of 23
Figure 12. (a) Macrograph of the weld with SiC; (b) left side of the weld from the centre, with hook
profile; (c) right side of the weld from the centre, with hook profile; (d) SEM image of the sample
obtained with SiC; (e) EDS analysis.
Figure 12.
(
a
) Macrograph of the weld with SiC; (
b
) left side of the weld from the centre, with hook
profile; (
c
) right side of the weld from the centre, with hook profile; (
d
) SEM image of the sample
obtained with SiC; (e) EDS analysis.

Materials 2022,15, 6427 19 of 22
Materials 2022, 15, x FOR PEER REVIEW 20 of 23
Figure 13. Comparative analysis of the tensile–shear load of FSS Welds without SiC and with SiC.
Figure 14. Comparative analysis of the micro-hardness of FSS welds without SiC and with SiC.
6. Conclusions
In the present study, SiC-reinforced aluminium 6061-T6 FSSW welds were obtained.
The effects of process parameters such as guiding hole diameter, tool rotation speed, and
pre-dwelling time on output characteristics such as tensile–shear load and mi-
cro-hardness were observed. The following conclusions were obtained from the present
research:
1. The guiding hole’s diameter plays a significant role in predicting the tensile–shear
behaviour and micro-hardness of the joint.
2. The percentage contribution as analysed by the WASPAS–Taguchi method for
guiding hole diameter was 52%, followed by pre-dwelling time (23%) and tool rota-
tion speed (19%).
Figure 13. Comparative analysis of the tensile–shear load of FSS Welds without SiC and with SiC.
Materials 2022, 15, x FOR PEER REVIEW 20 of 23
Figure 13. Comparative analysis of the tensile–shear load of FSS Welds without SiC and with SiC.
Figure 14. Comparative analysis of the micro-hardness of FSS welds without SiC and with SiC.
6. Conclusions
In the present study, SiC-reinforced aluminium 6061-T6 FSSW welds were obtained.
The effects of process parameters such as guiding hole diameter, tool rotation speed, and
pre-dwelling time on output characteristics such as tensile–shear load and mi-
cro-hardness were observed. The following conclusions were obtained from the present
research:
1. The guiding hole’s diameter plays a significant role in predicting the tensile–shear
behaviour and micro-hardness of the joint.
2. The percentage contribution as analysed by the WASPAS–Taguchi method for
guiding hole diameter was 52%, followed by pre-dwelling time (23%) and tool rota-
tion speed (19%).
Figure 14. Comparative analysis of the micro-hardness of FSS welds without SiC and with SiC.
6. Conclusions
In the present study, SiC-reinforced aluminium 6061-T6 FSSW welds were obtained.
The effects of process parameters such as guiding hole diameter, tool rotation speed, and
pre-dwelling time on output characteristics such as tensile–shear load and micro-hardness
were observed. The following conclusions were obtained from the present research:
1.
The guiding hole’s diameter plays a significant role in predicting the tensile–shear
behaviour and micro-hardness of the joint.
2.
The percentage contribution as analysed by the WASPAS–Taguchi method for guid-
ing hole diameter was 52%, followed by pre-dwelling time (23%) and tool rotation
speed (19%).

Materials 2022,15, 6427 20 of 22
3.
Based on the hybrid WASPAS–Taguchi method, G
3
T
2
P
3
—i.e., 3.5 mm guiding hole
diameter, 1700 rpm tool rotation speed, and 14 s of pre-dwelling time—were the
optimal values for both tensile–shear load and micro-hardness.
4.
The FSSW with SiC particles exhibited increased tensile–shear load and micro-hardness
under optimised process parameters.
5.
An increase of 23.41% was observed in tensile–shear load, while micro-hardness
increased by 18.8%, with the incorporation of SiC particles in FSSW, when compared
with conventional FSSW, under optimal parameters.
6. Uniform distribution of SiC particles was observed in the SEM images.
7.
The increase in SiC quantity significantly increased the peak temperature of the weld,
due to the ceramic behaviour and lower conductivity of the SiC particles, which
prevented the generated heat from escaping the welded region.
Author Contributions:
Conceptualisation, N.C., S.S. (Sarbjit Singh), M.P.G., H.K.G. and S.S. (Shub-
ham Sharma); methodology, N.C., S.S. (Sarbjit Singh), M.P.G., H.K.G. and S.S. (Shubham Sharma);
formal analysis, N.C., S.S. (Sarbjit Singh), M.P.G., H.K.G. and S.S. (Shubham Sharma); investigation,
N.C., S.S. (Sarbjit Singh), M.P.G., H.K.G. and S.S. (Shubham Sharma); writing—original draft prepa-
ration, N.C., S.S. (Sarbjit Singh), M.P.G., H.K.G. and S.S. (Shubham Sharma); writing—review and
editing, S.S. (Shubham Sharma), C.L., E.M.T.E. and S.E.-K.; supervision, S.S. (Shubham Sharma), C.L.,
E.M.T.E. and S.E.-K.; project administration, M.P.G. and S.S. (Shubham Sharma); funding acquisition,
S.S. (Shubham Sharma), E.M.T.E. and S.E.-K. All authors have read and agreed to the published
version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not Applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors wish to thank Siemens’ Center of Excellence, Punjab Engineering
College (deemed to be a university), Chandigarh, for providing the research facilities.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Ai, Y.; Liu, X.; Huang, Y.; Yu, L. Numerical analysis of the influence of molten pool instability on the weld formation during the
high speed fiber laser welding. Inter. J. Heat Mass Transf. 2020,160, 120103. [CrossRef]
2.
Ai, Y.; Yu, L.; Huang, Y.; Liu, X. The investigation of molten pool dynamic behaviors during the “
∞
” shaped oscillating laser
welding of aluminum alloy. Inter. J. Ther. Sci. 2022,173, 107350. [CrossRef]
3.
Bozzi, S.; Helbert-Etter, A.L.; Baudin, T.; Klosek, V.; Kerbiguet, J.G.; Criqui, B. Influence of FSSW parameters on fracture
mechanisms of 5182 aluminium welds. J. Mater. Process. Technol. 2010,210, 1429–1435. [CrossRef]
4.
Ataya, S.; Ahmed, M.M.Z.; Mohamed, M.; El-Sayed Seleman, M.M.; Hajlaoui, K.; Latief, F.H.; Soliman, A.M.; Elshaghoul, Y.G.Y.;
Habba, M.I.A. Effective Range of FSSW Parameters for High Load-Carrying Capacity of Dissimilar Steel A283M-C/Brass CuZn40
Joints. Materials 2022,15, 1394. [CrossRef] [PubMed]
5.
Ahmed, M.M.Z.; El-Sayed Seleman, M.M.; Ahmed, E.; Reyad, H.A.; Touileb, K.; Albaijan, I. Friction Stir Spot Welding of Different
Thickness Sheets of Aluminum Alloy AA6082-T6. Materials 2022,15, 2971. [CrossRef]
6.
Tozaki, Y.; Uematsu, Y.; Tokaji, K. Effect of processing parameters on static strength of dissimilar friction stir spot welds between
different aluminium alloys. Fatigue Fract. Eng. Mater. Struct. 2007,30, 143–148. [CrossRef]
7.
Gerlich, A.; Su, P.; Yamamoto, M.; North, T.H. Effect of welding parameters on the strain rate and microstructure of friction stir
spot welded 2024 aluminum alloy. J. Mater. Sci. 2007,42, 5589–5601. [CrossRef]
8.
Lathabai, S.; Painter, M.J.; Cantin, G.M.D.; Tyagi, V.K. Friction spot joining of an extruded Al–Mg–Si alloy. Scr. Mater.
2006
,
55, 899–902. [CrossRef]
9.
Yuan, W.; Mishra, R.S.; Webb, S.; Chen, Y.L.; Carlson, B.; Herling, D.R.; Grant, G.J. Effect of tool design and process parameters on
properties of Al alloy 6016 friction stir spot welds. J. Mater. Process. Technol. 2011,211, 972–977. [CrossRef]
10.
Merzoug, M.; Mazari, M.; Berrahal, L.; Imad, A. Parametric studies of the process of friction spot stir welding of aluminium
6060-T5 alloys. Mater. Des. 2010,31, 3023–3028. [CrossRef]
11.
Garg, A.; Raturi, M.; Bhattacharya, A. Influence of additional heating in friction stir welding of dissimilar aluminum alloys with
different tool pin profiles. Inter. J. Adv. Manuf. Technol. 2019,105, 155–175. [CrossRef]

Materials 2022,15, 6427 21 of 22
12.
Shen, J.; Min, D.; Wang, D. Effects of heating process on the microstructures and tensile properties of friction stir spot welded
AZ31 magnesium alloy plates. Mater. Des. 2011,32, 5033–5037. [CrossRef]
13.
Shen, J.; Li, Y.; Zhang, T.; Peng, D.; Wang, D.; Xu, N. Preheating friction stir spot welding of Mg/Al alloys in various lap
configurations. Sci. Technol. Weld. Join. 2015,20, 1–10. [CrossRef]
14.
Sun, Y.F.; Shen, J.M.; Morisada, Y.; Fujii, H. Spot friction stir welding of low carbon steel plates preheated by high frequency
induction. Mater. Des. 2014,54, 450–457. [CrossRef]
15.
Suresh, S.; Venkatesan, K.; Natarajan, E.; Rajesh, S. Performance analysis of nano silicon carbide reinforced swept friction stir spot
weld joint in AA6061-T6 alloy. Silicon 2021,13, 3399–3412. [CrossRef]
16.
Srivastava, M.; Rathee, S. A study on the effect of incorporation of sic particles during friction stir welding of al 5059 alloy. Silicon
2021,13, 2209–2219. [CrossRef]
17.
Bhushan, R.K.; Sharma, D. Optimization of Friction Stir Welding Parameters to Maximize Hardness of AA6082/Si 3 N 4 and
AA6082/SiC Composites Joints. Silicon 2021,14, 643–661. [CrossRef]
18.
Bist, A.; Saini, J.S.; Sharma, V. Comparison of tool wear during friction stir welding of Al alloy and Al-SiC metal matrix composite.
Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. 2021,235, 1522–1533. [CrossRef]
19.
Asadollahi, M.; Khalkhali, A. Optimization of mechanical and microstructural properties of friction stir spot welded AA 6061-T6
reinforced with SiC nanoparticles. Mater. Res. Express 2018,5, 116517. [CrossRef]
20.
Singh, K.; Singh, G.; Singh, H. Influence of post welding heat treatment on the microstructure and mechanical properties of
friction stir welding joint of AZ31 Mg alloy. Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. 2021,235, 1375–1382. [CrossRef]
21.
Mahgoub, A.; Bazoune, A.; Merah, N.; Al-Badour, F.; Shuaib, A. Effect of welding parameters on the mechanical and metallurgical
properties of friction stir spot welding of copper lap joint. Arab. J. Sci. Eng. 2018,44, 1283–1292. [CrossRef]
22.
Medhi, T.; Hussain, S.A.I.; Roy, B.S.; Saha, S.C. Selection of best process parameters for friction stir welded dissimilar Al-Cu alloy:
A novel MCDM amalgamated MORSM approach. J. Braz. Soc. Mech. Sci. Eng. 2019,42, 546. [CrossRef]
23.
Acharya, K.K.; Murmu, K.K.; Bagal, D.K.; Pattanaik, A.K. Optimization of the process parameters of dissimilar welded joints in
FSSW welding process of aluminum alloy with copper alloy using Taguchi optimization technique. Int. J. Appl. Eng. Res.
2019
,
14, 54–60.
24.
Bozkurt, Y.; Bilici, M.K. Application of Taguchi approach to optimize of FSSW parameters on joint properties of dissimilar
AA2024-T3 and AA5754-H22 aluminum alloys. Mater. Des. 2013,51, 513–521. [CrossRef]
25.
Bilici, M.K. Application of Taguchi approach to optimize friction stir spot welding parameters of polypropylene. Mater. Des.
2012
,
35, 113–119. [CrossRef]
26.
Pradhan, D.K.; Sahu, B.; Bagal, D.K.; Barua, A.; Jeet, S.; Pradhan, S. Application of progressive hybrid RSM-WASPAS-grey wolf
method for parametric optimization of dissimilar metal welded joints in FSSW process. Mater. Today Proc.
2021
,50, 766–772.
[CrossRef]
27.
Ilman, M.N. Microstructure and mechanical properties of friction stir spot welded AA5052-H112 aluminum alloy. Heliyon
2021
,
7, e06009. [CrossRef]
28.
Bahrami, M.; Dehghani, K.; Givi, M.K.B. A novel approach to develop aluminum matrix nano-composite employing friction stir
welding technique. Mater. Des. 2014,53, 217–225. [CrossRef]
29.
Singh, S.; Upadhyay, S.P.; Powar, S. Developing an integrated social, economic, environmental, and technical analysis model for
sustainable development using hybrid multi-criteria decision making methods. Appl. Energy 2022,308, 118235. [CrossRef]
30.
Zolfani, S.H.; Yazdani, M.; Zavadskas, E.K. An extended stepwise weight assessment ratio analysis (SWARA) method for
improving criteria prioritization process. Soft Comput. 2018,22, 7399–7405. [CrossRef]
31.
Triantaphyllou, E.; Mann, S.H. An examination of the effectiveness of multi-dimensional decision-making methods: A decision-
making paradox. Decis. Support Syst. 1989,5, 303–312. [CrossRef]
32.
Zavadskas, E.K.; Turskis, Z.; Antucheviciene, J.; Zakarevicius, A. Optimization of weighted aggregated sum product assessment.
Elektr. Elektrotech. 2012,122, 3–6. [CrossRef]
33.
Singh, S. Study the drilling behaviour of aluminium 6061 metal matrix composites using Taguchi’s methodology. Inter. J. Mach.
Mach. Mater. 2016,18, 327–340. [CrossRef]
34.
Singh, S.; Singh, A.; Singh, I.; Dvivedi, A. Optimization of the process parameters for drilling of metal matrix composites (MMC)
using Taguchi analysis. Adv. Mater. Res. 2012,410, 249–252. [CrossRef]
35.
Moses, J.J.; Dinaharan, I.; Sekhar, S.J. Characterization of silicon carbide particulate reinforced AA6061 aluminum alloy composites
produced via stir casting. Procedia Mater. Sci. 2014,5, 106–112. [CrossRef]
36.
Doel, T.J.A.; Bowen, P. Tensile properties of particulate-reinforced metal matrix composites. Compos. Part A Appl. Sci. Manuf.
1969
,
27, 655–665. [CrossRef]
37.
Mousavi, A.A.; Zhang, C.; Masri, S.F.; Gholipour, G. Structural damage detection method based on the complete ensemble
empirical mode decomposition with adaptive noise: A model steel truss bridge case study. Struct. Health Monit.
2021
, 84049609.
[CrossRef]
38.
Hua Huang, M.A.; Huang, M.; Wei Zhang, M.A.; Pospisil, S.; Wu, A.T. Experimental Investigation on Rehabilitation of Corroded
RC Columns with BSP and HPFL under Combined Loadings. J. Struct. Eng. 2020, 146. [CrossRef]
39.
Huang, H.; Huang, M.; Zhang, W.; Yang, S. Experimental study of pre damaged columns strengthened by HPFL and BSP under
combined load cases. Struct. Infrastruct. Eng. 2020, 1–18. [CrossRef]

Materials 2022,15, 6427 22 of 22
40.
Wei, J.; Xie, Z.; Zhang, W.; Luo, X.; Yang, Y.; Chen, B. Experimental study on circular steel tube-confined reinforced UHPC
columns under axial loading. Eng. Struct. 2021,230, 111599. [CrossRef]
41.
Zhang, W.; Liu, X.; Huang, Y.; Tong, M.-N. Reliability-based analysis of the flexural strength of concrete beams reinforced with
hybrid BFRP and steel rebars. Archive. Civ. Mech. Eng. 2022,22, 171. [CrossRef]
42.
Wang, X.; Yang, Y.; Yang, R.; Liu, P. Experimental Analysis of Bearing Capacity of Basalt Fiber Reinforced Concrete Short Columns
under Axial Compression. Coatings 2022,12, 654. [CrossRef]
43.
Yuan, J.; Lei, D.; Shan, Y.; Tong, H.; Fang, X.; Zhao, J. Direct Shear Creep Characteristics of Sand Treated with Microbial-Induced
Calcite Precipitation. Int. J. Civ. Eng. 2022,20, 763–777. [CrossRef]
44.
Shan, Y.; Zhao, J.; Tong, H.; Yuan, J.; Lei, D.; Li, Y. Effects of activated carbon on liquefaction resistance of calcareous sand treated
with microbially induced calcium carbonate precipitation. Soil Dyn. Earthq. Eng. 2022,161, 107419. [CrossRef]
45.
Li, X.; Yang, X.; Yi, D.; Liu, B.; Zhu, J.; Li, J.; Wang, L. Effects of NbC content on microstructural evolution and mechanical
properties of laser cladded Fe50Mn30Co10Cr10-xNbC composite coatings. Intermetallics 2021,138, 107309. [CrossRef]
46.
Zhong, Y.; Xie, J.; Chen, Y.; Yin, L.; He, P.; Lu, W. Microstructure and mechanical properties of micro laser welding NiTiNb/Ti6Al4V
dissimilar alloys lap joints with nickel interlayer. Mater. Lett. 2022,306, 130896. [CrossRef]
47.
Wang, H.; Xie, J.; Chen, Y.; Liu, W.; Zhong, W. Effect of CoCrFeNiMn high entropy alloy interlayer on microstructure and
mechanical properties of laser-welded NiTi/304SS joint. J. Mater. Res. Technol. 2022,18, 1028–1037. [CrossRef]
48.
Liang, L.; Xu, M.; Chen, Y.; Zhang, T.; Tong, W.; Liu, H.; Li, H. Effect of welding thermal treatment on the microstructure and
mechanical properties of nickel-based superalloy fabricated by selective laser melting. Mater. Sci. Eng. A Struct. Mater. Prop.
Microstruct. Process. 2021,819, 141507. [CrossRef]
49.
Shi, L.; Xiao, X.; Wang, X.; Liang, H.; Wang, D. Mesostructural characteristics and evaluation of asphalt mixture contact chain
complex networks. Constr. Build. Mater. 2022,340, 127753. [CrossRef]
50.
Xu, H.; Wang, X.; Liu, C.; Chen, J.; Zhang, C. A 3D root system morphological and mechanical model based on L-Systems and its
application to estimate the shear strength of root-soil composites. Soil Tillage Res. 2021,212, 105074. [CrossRef]