Investigation of the Process Parameters in Rotary Friction Welded
Dissimilar AA7075/AA5083 Aluminum Alloy Joints on Fatigue
Initiation using FEA and ANN
ANMAR MUSAID NAYIFa, YOUNIS A. D.b, ZIAD SHAKEEB AL SARRAFc
Department of Mechanical Engineering,
College of Engineering,
Mosul University,
Mosul,
IRAQ
aORCiD: https://orcid.org/ 0009-0002-7965-8421
bORCiD: https://orcid.org/0000-0001-5145-5716
cORCiD: https://orcid.org/0000-0001-9957-4386
Abstract: - The rotary friction welding (RFW) method is one of the most widespread methods in the world for
producing bimetallic components that require high mechanical strength. Simulations play a vital role in
improving energy efficiency and reducing environmental impact, aligning with the sustainability goals of
modern industry. A neural network (NN)-based incremental learning system was developed to predict crack
growth and fatigue for AA5083 and AA7075 aluminum alloys. The results indicate the ability of this method to
accommodate the input temperatures and the S-N curve and provide reliable predictions of expected fatigue.
This method can reduce labor costs and time spent on crack propagation tests, enhancing the effectiveness of
production processes and reducing process costs. This work also reveals the ability of neural . It maynetworks
(NN) in monotonic function extrapolation like the S-N curve, which may pave the way for a wide variety of
monotonic function-predicting problems. In future studies, a neural network (NN)-based increment learning
scheme could be trained with random parts of individual SN curves and applied to predict the rest.
Additionally, the verification utilizing AISI 2205 and AISI 1020 steel has observed that neural networks may
obtain S-N curve values for another metal with less than an 8% error rate. Friction pressure increases
temperature, deformation, and stress in welding processes. Friction pressure 17 MPa increases temperature to
355 degrees Celsius, while Friction pressure 23 MPa increases deformation to 0.020 mm. A friction pressure
of 29 MPa increases equivalent stress to 110 MPa. The indication of the S-N curve shows that increasing
welding pressure increases Alternating Stress. Friction pressure also increases life, with minimum life cycles
reaching 171040 cycles at 17 MPa, 195560 cycles at 23 MPa, and 283690 cycles at 29 MPa. Comparing
research and simulation results, convergence is less than 8%, reducing error.
Key-Words: - Rotary friction welding, Fatigue, Artificial neural network (ANN), number of cycles to failure, S-
N Curve, Equivalent stress.
Received: April 3, 2024. Revised: August 11, 2024. Accepted: September 13, 2024. Published: October 31, 2024.
1 Introduction
Modeling and simulation are crucial in the
engineering sector for understanding complex
phenomena and improving designs and processes.
Fatigue fractures in welded shafts, particularly those
formed by Rotary Friction Welding (RFW), are a
significant issue in sectors like aerospace,
automotive, and oil & gas. Understanding and
forecasting the behavior of fatigue fractures in
RFW-produced welded shafts is difficult due to the
complex interaction of material characteristics,
welding settings, and loading circumstances.
Computer models can help engineers and academics
construct insights into the physical processes
involved in RFW and fatigue fracture formation and
propagation, enabling the design of methods for
mitigating and managing these fractures. A
multidisciplinary approach is essential to address
fatigue fractures in welded shafts. [1], ANSYS was
used to simulate rotary friction welding processes
for Al-alloy, dissimilar, and composite materials. A
mathematical model was developed to characterize
heat transport, frictional force, and plastic
deformation heat production. Results showed that
WSEAS TRANSACTIONS on APPLIED and THEORETICAL MECHANICS
DOI: 10.37394/232011.2024.19.11
Anmar Musaid Nayif, Younis A. D.,
Ziad Shakeeb Al Sarraf
E-ISSN: 2224-3429
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increasing welding parameters increased welding
temperature and axial shortening, with satisfactory
agreement with experimental investigations. The
study, [2], investigates the mechanical properties of
uniting Austenitic stainless steel AISI304 and Low-
carbon steel ST-37 using rotary friction welding
techniques. Results show perfect welding settings
with acceptable characteristics, with a highest
tensile strength of 596.3 MPa at 1030 RPM, [3]. The
study explores the impact of torque modulation on
rotary friction welding, a process that has been
around since WWII. It reveals that energy inputs
can cause changes in weld microstructure and
suggests potential improvements. [4], friction
welding, a non-traditional technique, primarily
focuses on Rotary Friction Welding (RFW), which
converts kinetic energy into thermal energy,
resulting in high-quality welds and high-efficiency
coefficients. [5], the role of deep learning in
improving the performance of shallow or
conventional neural networksspecifically, Back
Propagation Neural Networkshas been studied
(BPNN). The majority of the research presented in
this paper concentrated on employing deep ANN to
fulfill the recognition procedure. The accuracy and
cost function reduction with lost values between
what we already have from defined instances or
scenarios in the database is the basis for comparing
the suggested system with other studies. [6], he
study examined the thermal behavior of AA6351 T6
aluminum and AISI 304L stainless steel connections
during friction welding using a thermocouple
system. Results revealed temperature distribution at
the bonding interface affects gradients, dissipation,
heating rates, cooling, and maximum temperatures.
The study, [7], examined the impact of process
parameters on the mechanical properties of SS304
austenitic stainless steel and SS430 ferritic steel
cylindrical rods through friction welding, revealing
high tensile strength. [8], an attempt has been
successfully made to develop a model to predict the
effect of input parameters on weld bead geometry of
submerged arc welding (SAW) process with the
help of neural network technique and analysis of
various process control variables and the important
of weld bead parameters in submerged arc welding.
The study, [9], connected AISI 4140 and AISI 1050
steel, reducing raw material costs. It examined
mechanical characteristics, structural investigations,
joint strength verification, and optimal welding
settings, achieving 6% greater tensile strength. The
study, [10], examines friction welding techniques
for forming an aluminum matrix composite with
SiCp particles, utilizing optical and electron
microscopy, lap shear strength tests, and
microhardness measurements.
2 Method Statement
2.1 Materials
The AA5083 and AA7075 aluminum alloy rods that
were employed in this investigation were treated
with H112 and T6, respectively. Table 1 gives their
chemical compositions, and Figure 1 depicts the
microstructures of the two base metals, [11].
According to Figure 1(a), the AA7075's
microstructure is made up of elongated grains that
run parallel to the direction of rolling which is
typical of wrought products. Similar microstructural
characteristics are seen in AA5083 in Figure 1(b),
although in contrast to AA7075, AA5083's
microstructure shows coarser grains. Table 2
displays the mechanical characteristics of base
metals AA7075 and AA5083 as determined by
measurements with the standards for each metal
indicated in brackets. To ensure consistency, the
tensile tests were carried out in triplicate using
tensile specimens that complied with ASTM
E8/E8M-16 standards. On the cross-section of the
rods, measurements of hardness were made along
the radial direction and the center line of the parallel
section of the rods. Concerning Table 2, it is clear
that AA7075 has more strength and hardness than
AA5083, [11].
Table 1. Chemical compositions of AA5083 and AA7075 (wt. %), [11]
Material
Ti
Al
AA5083
4.45
0.03
0.45
0.19
0.07
0.08
0.02
0.01
Bal.
AA7075
2.25
5.21
0.12
0.25
0.21
0.32
1.31
0.09
Bal.
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Fig. 1: Microstructures of aluminum alloy rods: (a) AA7075 and (b) AA5083, [1]
Table 2. Mechanical properties of the base materials, [12]
Mechanical Properties
AA5083
AA7075
Tensile strength (MPa)
Yield strength (MPa)
Ductility (% elongation)
Vickers microhardness (Hv)
2.2 The Rotary Friction Welding Process
In this project, a lathe machine was modified to
create a rotating friction welding machine. The
samples were created using a milling machine in the
shape of a 50 mm long, 15 mm diameter cylindrical
rod, [11]. As indicated in Figure 2, the second rod,
AA7075, served as the rotating side (RS), and the
cylindrical rod AA5083 was positioned as the
stationary side (SS) that was moved axially.
Fig. 2: Design rod
The RFW procedures as shown in Figure 3 were
used to perform RFW processes on the dissimilar
AA7075/AA5083 aluminum alloys. As soon as the
moving surface contacted the stationary surface, the
welding process's first step began. This step is
denoted by A in Figure 3 and involved a rapid
increase in axial pressure from 0 to 17,23,29 MPa
for two seconds. Step B then involved friction at a
constant pressure of 17, 23, and 29 MPa for ten
seconds. The production of bonds was triggered by
the interruption of the rotational speed at the end of
the friction stage (step C) and the application of an
upset pressure 115 MPa for 4 s (step D). Welding
heat cycles as well as variations in burn-off length
as the response parameter were noted.
Fig. 3: Rotary friction welding process stages, [11]
2.3 Artificial Neural Network (ANN)
Neural network-based methods are of great learning
and generalization ability. Where the temperatures
that were extracted from the simulation of the
welding process were entered and entered into the
artificial intelligence program to obtain the values of
the s-n curve. As seen in Figure 5. So enormous
success has been achieved in many industry
applications, [11]. NN-based methods were
introduced to fatigue and fracture research areas in
different topics such as SN curves determination
[12], stress intensity factor prediction, the useful life
prediction Particularly, more and more NN-based
methods have shown their capabilities and
effectiveness in fatiguecrack growth rate
prediction. The neural network trained to simulate
the crack growth rate of aluminum alloy prediction
is in good agreement with the experimental data
with an error of less than 8 %, [13].
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Fig. 4: ANN diagram, [12]
2.4 Methodology
Generally, unstructured matrices are effective for
complex calculations, so for the above reason, the
unstructured tetrahedron frameworks were utilized
in the ongoing review. With just one phase from the
user, ANSYS can generate solid geometry meshes
and three-dimensional models. In this investigation,
there were cells extracted from a total of (1208270)
tetrahedron elements, and the sizing of each element
was 0.5mm see Figure 4.
An accurate network must be created to solve
the equations because the simulation process
depends on complicated algorithms to work on the
matrices present in the domain. After that, use the
mesh's dependability to find a remedy and bring the
outcomes to a stable condition. It is important to
create more than one network and mesh
dependability due to the variety of models that have
been simulated. The value of the element was
1208270 when the max temperature reached 422.9
C as in Table 3.
The fatigue simulation model was designed
according to the dimensions mentioned in Figure 6.
This sample resulted from the simulation of rotary
friction welding, where the length of the sample was
90 mm and its diameter was 12 mm.
Fig. 5: Mesh generated
Fig. 6: Fatigue Cracks domain
Table 3. Mesh independency
Case
Element
node
Max temperature C
1
694473
138678
174.82
2
845695
326866
171.90
3
056566
524326
170.74
4
208270
701776
170.67
Where the process of stabilizing the sample was
carried out on one hand, and a pressure of 20 tons
was used to obtain the fatigue state in the sample
during the simulation process. The fatigue analysis
in ANSYS Workbench estimates the number of
cycles to failure using a variety of fatigue life
estimation techniques, including the Stress-Life (S-
N) and Strain-Life (-N) approaches. The specific
material qualities, loading conditions, and method
selected all affect the equations that are utilized for
fatigue analysis. The equation used to calculate the
fatigue life (N) for a Stress-Life (S-N) approach in
ANSYS, which is frequently utilized for metals, is
frequently represented as [14]:
∧ m)+(B/()^∧ n) (1)
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Where: The cycle count before failure is N. The
amplitude of the alternating stress is. Material-
specific constants A, B, m, and n are identified by
data from fatigue tests or by material characteristics.
Similar equations are utilized in the Strain-Life (-N)
approach, which is used for materials that display
strain-based fatigue behavior, although the terms
and constants may be connected to strain rather than
stress.
2.5 Mathematical Model Adopted by
ANSYS software
The overall equilibrium equations for linear
structural static analysis are [14]:
(1)
or
(2)
where:
[K] = = total stiffness matrix
{u} = nodal displacement vector
N = number of elements
[ = element stiffness matrix (described in
Element Library) (may include
the element stress stiffness matrix (described in
Stress Stiffening))
{ = reaction load vector
{ the total applied load vector is defined by
[11]:
(3)
Where:
{ = applied nodal load vector
{ Acceleration load vector
= total mass matrix
] = element mass matrix (described in
Derivation of Structural Matrices)
} = total acceleration vector (defined in
Acceleration Effect)
{ = element thermal load vector (described
in Derivation of Structural Matrices)
{ =element pressure load vector (described
in Derivation of Structural Matrices Consider a one-
element column model that is solely loaded by its
own weight to demonstrate the load vectors.
Reaction Load Vectors and Applied Load Vectors
Although the lower applied gravity load is applied
directly to the imposed displacement and so does
not create strain, it contributes just as much to the
reaction load vector as the higher applied gravity
load. In addition, any applied loads on a particular
DOF are disregarded if the stiffness for that DOF is
0 The equation governing transient thermal
behavior, also known as the heat conduction
equation or the heat diffusion equation, describes
how temperature changes within a solid over time
due to heat conduction. This equation is a
fundamental tool in thermal analysis and can be
expressed as [10]:
(4)
Where:
Is the material density .
Is the specific heat capacity .
Is the temperature as a function of time
and spatial coordinates.
Represents the rate of change of temperature
with respect to time.
Is the thermal conductivity , which
characterizes how well the material conducts heat.
Is the gradient operator, and represents the
divergence operator, used to describe heat flow.
Represents any internal heat generation or
external heat sources .This equation states that
the change in temperature is proportional to
the heat conduction term on the right side, which is
the divergence of the thermal conductivity gradient,
and any heat sources or sinks within the material,
[15].
3 Results and Discussion
3.1 The Effect of Friction Pressure on the
Temperature of the Rotary Friction
Welding Process
The quality and properties of a weld are
significantly influenced by friction pressure and
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temperature during rotary friction welding. This
solid-state welding technique involves rotating two
parts against each other, applying pressure to
generate frictional heat, and joining materials. The
temperature profile within the welding zone is
directly influenced by friction pressure and
rotational speed. To achieve the ideal temperature
range for efficient material softening and joining,
precise control of friction pressure is necessary.
Modern rotary friction welding machines often have
automatic pressure control systems to maintain
stable temperature conditions. Engineers and
researchers study the link between friction pressure
and temperature to create precise control plans and
recommendations for dependable and high-quality
welding across various industries, [14]. From Figure
7 (Appendix) it is evident that the Friction pressure
increases the temperature generated by friction
pressure, as the temperature reached 355 degrees
Celsius at the Friction pressure of 17 MPa, and at
the Friction pressure of 23 MPa the temperature
reached 384 degrees Celsius, while at the Friction
pressure, 29 MPa the temperature reached to 413
degrees Celsius. The increase in temperature with
the increase in the speed of rotation of the axes in
rotary friction welding is primarily due to the
conversion of mechanical energy into heat through
friction at the interface between the two rotating
components. When the rotation speed increases,
several factors contribute to the rise in temperature:
Increased Frictional Work: Higher rotation
speeds lead to greater relative motion between
the materials being welded. This increased
relative motion results in more significant
frictional forces and work done at the interface.
As the materials rub against each other with
greater intensity and speed, a larger amount of
mechanical energy is converted into heat, [16].
Increased Shear Forces: The higher rotational
velocity induces stronger shear forces at the
interface. Shearing action between the materials
generates a considerable amount of frictional
heat, which further raises the temperature, [17].
More Frequent Material Mixing: The increased
speed causes more frequent and intense mixing
of material at the weld interface. This mixing
creates a localized temperature rise as the
material is exposed to greater shearing and
deformation, [18].
Reduced Heat Dissipation: At higher rotation
speeds, there is less time for heat to dissipate
away from the weld zone, which allows the
temperature to build up more quickly, [19].
Plastic Deformation: The intense mechanical
energy generated at higher rotation speeds
causes plastic deformation of the material,
making it more malleable. This plastic
deformation is necessary for the materials to
bond and form a strong metallurgical joint, [20].
Increased Strain Rate: A higher rotation speed
typically corresponds to a higher strain rate,
which is the rate at which the material is
deformed. The increased strain rate contributes
to higher temperatures because it increases the
rate at which work is done on the material, [21].
3.2 Deformation with Time of Rotary
Friction Welding Process at Different
Friction Pressure
The rotary friction welding process, also known as
spin welding, involves rotating components against
each other while applying pressure. The heat
produced by friction softens materials, making
bonding easier. The deformation properties of
welded components are significantly influenced by
friction pressure and other process variables. Higher
friction pressure results in more substantial
deformation, while lower pressure results in less
deformation and longer welding sessions. Engineers
and researchers must strike a balance between
pressure and distortion to produce dependable
welds. Different materials react differently to
deformation, with some being more malleable and
others more brittle.
Modern rotary friction welding equipment uses
automated control systems to monitor and control
deformation. Understanding friction pressure
influences deformation can help develop precise
control strategies for industries like automotive,
aerospace, and manufacturing, [22].
From Figure 8 (Appendix) and Figure 9 it is
evident that the Friction pressure increases the
deformation generated by friction pressure, as the
deformation reached 0.020 mm at the Friction
pressure of 17 MPa, and at the Friction pressure,
of 23 MPa the deformation reached 0.022 mm,
while at the Friction pressure, 29 MPa the
deformation reached to 0.025 mm .
Material Behavior: AA5083, with its higher
magnesium content, tends to be more ductile
and exhibits good formability. This can be
advantageous in friction welding, as it can
deform more readily to achieve a strong weld
joint. Work Hardening: AA5083 is known for
its work-hardening behavior. During friction
welding, it can experience significant plastic
deformation and work hardening, which can
contribute to a strong and durable weld.
AA7075, while strong, may not deform as
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readily as AA5083 and may not work harden to
the same extent. This can make it more
challenging to achieve a strong weld in certain
friction welding applications [23], [24].
Heat Generation: The heat generated during
friction welding is critical in plastic
deformation. The heat input can vary depending
on the welding parameters and the material's
thermal conductivity. AA7075, with its higher
thermal conductivity, may dissipate heat more
efficiently during the welding process, affecting
the plastic deformation.
When friction welding AA5083 and AA7075,
AA5083's greater ductility and work-hardening
characteristics can be advantageous for achieving a
strong weld. However, the success of the welding
process also depends on other factors such as
welding parameters, tool design, and the specific
requirements of the joint, [20].
3.3 Equivalent Stress Effect Of Rotary
Friction Welding Process at Different
Friction Pressure
Friction pressure significantly impacts the quality,
integrity, and performance of welded connections
during rotary friction welding. The process involves
rotating components and applying pressure to create
frictional heat, causing material flow and
deformation. Higher friction pressure leads to
greater stress levels in the welding zone. Residual
tensions may form as the welded connection cools
down. The strength and dependability of a welded
connection depend on the level and distribution of
stress within the joint. To ensure stress
concentration remains within permissible limits,
friction pressure must be properly controlled.
Modern rotary friction welding equipment often
includes advanced control and monitoring systems
to control friction pressure, resulting in reliable,
highly strengthened joints with controlled stress
levels. Understanding friction pressure's impact on
stress can lead to precise management techniques
and standards for strong and durable welds in
sectors like automotive, aerospace, and
manufacturing.
From Figure 10 (Appendix) and Figure 11
(Appendix) it is evident that the Friction pressure
increases the Stress generated by friction pressure,
as the Stress reached 110 MPa at the Friction
pressure of 17 MPa, and at the Friction pressure,
of 23 MPa the Stress reached 136 MPa, while at the
Friction pressure, 29 MPa the Stress reached to 154
MPa.
When the friction pressure is increased in rotary
friction welding, it generally implies that more force
is applied to the interface of the materials being
welded. This increased force leads to greater
frictional heating at the interface, which in turn
affects the material properties and generates
additional stress. Higher friction pressure results in
greater frictional forces at the interface between the
two materials. This can lead to increased plastic
deformation and thermal effects, contributing to
higher equivalent stresses. The response of materials
to heat and pressure can vary, affecting how they
deform and experience stress during the welding
process. Different materials have different thermal
and mechanical properties that influence equivalent
stress. It's important to note that while increasing
friction pressure may enhance the welding process
by promoting better material bonding, there is a
limit to the pressure that can be applied without
causing adverse effects such as material
degradation, excessive plastic deformation, or other
undesirable outcomes, [23].
Fig. 9: Deformation with time of rotary friction
welding process at different Friction pressure
3.4 Alternating Stress with the Number of
Cycles to Failure for Different
Welding Pressure
Through the temperatures obtained by varying the
friction pressure and entering the results into the
artificial intelligence program. S-N curve diagrams
were obtained for all cases; Figure 12 (Appendix)
shows that the increase in welding pressure
increases the amount of Alternating Stress.
Figure 13 (Appendix), it is evident that the
Friction pressure increases the Life generated by
Friction pressure, as the minimum life reached
171040 cycles at a Friction pressure of 17 MPa, and
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at the Friction pressure, of 23 MPa the minimum life
reached 195560 cycles, while at the Friction
pressure, 29 MPa the minimum life reached 283690
cycles.
4 ANN Validation for Rotary Friction
Welding of AISI 2205 and AISI
1020
Commercially available 12 mm x 6000 mm bars of
AISI 1020 low carbon steel and AISI 2205 duplex
stainless steel were employed as the materials for
the experimental experiments. To get rid of any
pollution, rust, and oxide coatings on the butt
surface, the materials were cut at a strip sawing
machine at a length of 72 mm, and the specimens
were turned at a turning lathe at a diameter of 70
mm, [24]. The steel couples' chemical makeup, as
determined by the samples they served as Each
specimen underwent tensile and fatigue testing to
determine the effects of the welding parameters on
the bond strength and the degrading processes. The
outcome of the test specimens' tensile strength
measurements. The tests indicated that the
maximum tensile stresses for the AISI 1020 steel
and the AISI 2205 duplex stainless steel employed
in them were 610,780.
Where the comparison was made with the
research and obtaining information and applying it
to the simulation process, where it was found that
the convergence reaches less than 8%, the
percentage of error between the two works, as in
Figure 14 (Appendix).
5 Conclusion
In this study, rotary friction welding was used to
connect the aluminum alloy couples AA5083 and
AA7075, which had different chemical
compositions and different manufacturing
parameters. When the friction pressure is increased
in rotary friction welding, it generally implies that
more force is applied to the interface of the
materials that are being welded. This increased force
leads to greater frictional heating at the interface,
which in turn affects the material properties and
generates additional stress. Higher friction pressure
results in greater frictional forces at the interface
between the two materials. This can lead to
increased plastic deformation and thermal effects,
contributing to higher equivalent stresses.
Therefore, the equivalent stress results show an
increase in Friction pressure. The increase in
welding pressure increases the amount of
Alternating Stress. Increased pressure can enhance
the mixing of material at the weld interface,
reducing the presence of distinct zones with
different properties. Homogeneous mixing
contributes to a more uniform stress distribution,
improving the overall fatigue performance. It's
important to note that while higher welding pressure
can generally improve fatigue performance, there is
an optimal range for pressure, and excessive
pressure may lead to other issues, such as material
damage or distortion. The specific effects of
welding parameters can vary depending on the alloy
and the welding process used, so careful
optimization is required for each application.
Additionally, the fatigue behavior is influenced by
factors like the applied load spectrum,
environmental conditions, and post-weld treatments.
Also, the validation by using AISI 1020 steel and
the AISI 2205 It has been noted that it is possible to
use neural networks to obtain S-N curve values for
other metals with an error rate of less than 8%.
Declaration of Generative AI and AI-assisted
Technologies in the Writing Process
The authors declared that the language of this
current work was carried out based on using AI and
their AI-Assisted technologies, in writing parts of
sentences through using these technologies in order
to improve the readability and language in academic
manner.
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Volume 19, 2024
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WSEAS TRANSACTIONS on APPLIED and THEORETICAL MECHANICS
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AA6063 alloy and faying surface-tapered
AISI304L alloy,
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properties of dissimilar AISI 2205AISI 1020
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APPENDIX
(a)
(b)
(c)
Fig. 7: Temperature contour of rotary friction welding process at different Friction pressure:
(a) 17 MPa, (b) 23 MPa, (c) 29 MPa
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(a)
(b)
(c)
Fig. 8: Deformation contour of rotary friction welding process at different Friction pressures:
(a) 17 MPa, (b) 23 MPa, (c) 29 MPa
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(a)
(b)
(c)
Fig. 10: Equivalent stress contour of rotary friction welding process at different Friction pressure:
(a) 17 MPa, (b) 23 MPa, (c) 29 MPa
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Fig. 11: Equivalent stress with time of rotary friction welding process at different Friction pressure
Fig. 12: Alternating Stress with Number of cycles to failure for different welding pressure
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(a)
(b)
(c)
Fig. 13: Life contour of fatigue process at different Friction pressure:
(a) 17 MPa, (b) 23 MPa, (c) 29 Mpa
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Fig. 14: Stress variation with the number of cycles to failure
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