13

CHAPTER 2

LITERATURE SURVEY

2.1 INTRODUCTION

This chapter presents a comprehensive literature survey related to

the topic of research which includes FS welding process, tool pin profile used,

parameters used for FS welding of aluminum alloys and dissimilar FS

welding of aluminum alloys and the mechanical properties of the FS welded

joints. Design of Experiments (DOE) technique, Artificial Neural Network

(ANN) and its application to different processes are discussed. Also micro

structural changes in the various zones of the FS weldments are analyzed.

2.2 FRICTION STIR WELDING (FSW) PROCESS

A very potentially revolutionary welding method was conceived at

The Welding Institute, United Kingdom in 1991. The process was named as

Friction Stir Welding. FSW is in consistent with the more conventional

methods of friction welding which have been practiced since the early 1950s.

Figures 2.1 and 2.2 shows the schematic of FSW process and the

sequence of FSW respectively which are explained as follows. A cylindrical,

shouldered tool with a profiled probe is rotated and slowly plunged into the

joint line between two pieces of sheet or plate material, which are butted

together. The parts have to be clamped onto a backing bar in a manner that

prevents the abutting joint faces from being forced apart. Frictional heat is

14

generated between the wear resistant welding tool and the material of the

work pieces. Sufficient dwell time is allowed in order to generate frictional

heat. This heat causes the material to soften without reaching the melting

point and allows traversing of the tool along the weld line. The plasticized

material is transferred from the leading edge of the tool to the trailing edge of

the tool profile and is forged by the intimate contact of the tool shoulder and

the pin profile. This produces a solid phase bond between the two pieces

(Sanderson et al 2000, Threadgill et al 2009).

Figure 2.1 Schematic diagram of friction stir welding (Mishra and Ma

2005)

The side where the direction of rotation is same as that of welding is

called the advancing side (AS), with the other side designated as the retreating

side (RS). The material movement around the pin can be complex due to

various geometrical features of the tool. During FSW process, the material

undergoes intense plastic deformation resulting in generation of fine and

equiaxed recrystallized grains.

15

Figure 2.2 Sequence of friction stir welding (Adamowski and Szkodo

2007)

The fine microstructure in friction stir welds produces good

mechanical properties. FSW is considered to be the most significant

development in metal joining in a decade and is a green technology due to its

energy efficiency, environment friendliness, and versatility (Mishra and Ma

2005).

In contrast to the traditional friction welding, which is usually

performed on small axisymmetric parts that can be rotated and pushed against

each other to form a joint, friction stir welding can be applied to various types

of joints like butt joints, lap joints, T joints, and fillet joints. Though FSW was

primarily developed to join aluminum alloys intense research has been carried

out to join other metal alloys such as magnesium, copper, brass, steel, nickel

and titanium (Nandan et al 2008).

16

2.3 FRICTION STIR WELDING PROCESS PARAMETERS

Figure 2.3 shows the Fish-bone diagram depicting the friction stir

welding factors which influence the joint properties as listed below.

1. Rotational speed

2. Welding speed

3. Axial force

4. Tool geometry

5. Tool material

6. Tool tilt angle

7. Material related properties

8. Clamping force and geometry

Figure 2.3 Factors influencing friction stir welded joint properties

(Jayaraman et al 2009a)

17

The effects of above factors on joint properties have been studied

extensively by many researchers. Each factor (scanty information is available

on the 8th factor as listed above) is able to independently influence the welding

process (Rajakumar et al 2011a).

The major FSW process parameters which influence the joint

strength and microstructure are tool rotational speed, welding speed, axial

force and tool tilt angle (Nandan et al 2008). The dimensions and especially

shape of the tool play a crucial role to obtain sound joints. The tool design

significantly alters material flow and consolidation of plasticized material

during welding (Rai et al 2011).

The rotation of the tool results in stirring and mixing of material

around the rotating pin. The rotational speed (N) is a significant process

variable since it tends to influence the transitional velocity. Higher tool

rotation generates higher temperature because of high frictional heating which

results in more intense stirring and mixing of material (Jayaraman et al

2009b). The rate of stirring of plasticized material determines the formation of

defects. Excessive stirring of plasticized material will result in tunnel defects.

Lack of stirring will result in lack of bonding. Azimzadegan and Serajzadeh

(2010) observed an increase in the width of stir zone with increased tool

rotational speed. Tool rotational speed influences the temperature in the stir

zone and subsequent grain growth (Karthikeyan et al 2010).

Peel et al (2006a) investigated the processing window of 3 mm thick

AA5083 and AA6082 joints and the tool rotational speed was found to

strongly influence the heat generation during welding than the traverse speed.

Peel et al (2006b) studied the effect of tool rotational speed and traverse speed

on the microstructure, hardness and precipitation distribution of 3 mm thick

AA5083 and AA6082 joints. Steuwer et al (2006) quantified the effect of tool

rotational speed and traverse speed on the residual stresses of 3 mm thick

18

AA5083 and AA6082 joints. He concluded that the tool rotational speed was

a useful process variable to optimize the residual stress.

The welding speed (S) prompts the translation of tool which in turn

pushes the stirred material from front to the back of the tool pin and

completes the welding. The rubbing of tool shoulder and pin with the work

piece generates frictional heat. The welding speed determines the exposure

time of this frictional heat per unit length of weld and subsequently affects the

grain growth (Sakthivel et al 2009). The rate of heating in a thermal cycle

during FSW is a strong function of the welding speed. Increase in welding

speed causes a decrease in frictional heat generation and lack of stirring

(Elangovan and Balasubramanian 2008). The welding speed also influences

the width of the stir zone.

Higher welding speeds are associated with low heat inputs, which

result in faster cooling rates of the welded joint. This can significantly reduce

the extent of metallurgical transformations taking place during welding (such

as solubilisation, re-precipitation and coarsening of precipitates) and hence,

reduces the local strength of individual regions across the weld zone in FS

welding of Al alloys (Lomolino et al 2005).

Material flow in the weld zone is influenced by the extrusion

process where the applied axial force (F) and the motion of the tool pin propel

the material after it has undergone the plastic deformation. The shoulder force

is directly responsible for the plunge depth of the tool pin into the work

(Elangovan et al 2008a). As the axial force increases, both hydrostatic

pressure beneath the shoulder and the temperature in the stir zone will

increase. The hydrostatic pressure should be essentially higher than the flow

stress of the materials of the mating surfaces (Kumar and Kailas 2008). Since

the flow stress reduces as the temperature increases, force required to make

the adequate contact between the surfaces decreases. Hence, the formation of

19

defect free solid state weld requires optimum temperature and hydrostatic

pressure. Axial force is also responsible for flash formation. An excessive

axial force results in higher amount of flash leading to defects.

The magnitude of axial force decides the coefficient of friction

between the tool and the workpieces (Colligan et al 2003). FS welds

fabricated with the operating parameters of higher rotational speed, lower

welding speed and higher axial force resulted in highest heat input, whereas

lower rotational speed, higher welding speed and lower axial force resulted in

lowest heat input (Colligan et al 2003, Lomollno et al 2005).

Zhang and Zhag (2007) investigated the effect of axial pressure in

FS welding of AA6061 aluminum alloy and reported that the maximum

temperature and plastic deformation could be increased with an increase in the

axial pressure. Due to the frictional heat generated between the tool shoulder

and the base metal, the material under the action of the rotating tool attains a

plastic state. The axial force applied through the rotating tool causes the

plasticized metal to extrude around the tool pin in the vertical direction and

get consolidated in the back side when the tool moves forward. Both the

stirring and extrusion cause the elongated grains to into smaller grains and

fracture the strengthening precipitates into very fine particles.

Oyuang and Kovacevic (2002) observed that the axial force was

directly responsible for the plunge depth of the tool pin into the work piece

and load characteristics associated with linear FS weld. When the axial force

was relatively low, there was a tunnel found at the bottom. While with higher

axial force, the weld was observed to be sound with full penetration. It

showed that sufficient axial force was required to form good weld. It was due

to the temperature during FS welding defining the amount of plasticized

metal, and the temperature was greatly dependent on the axial force.

20

The variation in the process parameters results in the variation of

temperature distribution. However, the temperature variations are limited by

the melting point of the welding material and maximum temperature ranges

from 80% to 90% of the melting point. There exists a relation between the

temperature field and process parameters. (Tang et al 1998, Zhang and Zhang

2007).

The heat input and material flow behavior decide the quality of

FSW joints. FSW eliminates fusion welding defects. But FSW can induce

other serious defects such as pin hole, worm hole, kissing bond, tunnel and

voids (Chen et al 2006, Kim et al 2006, Li et al 2011). Those defects

adversely affect the joint strength due to the reduction in load bearing area

and acting as crack initiation sites. A proper selection of process parameters is

a prerequisite to produce sound welds with full penetration.

Shigematsu et al (2003) attempted to join 3 mm thick dissimilar

AA5083 and AA6061 alloys using FSW and examined the microstructure and

the mechanical properties of the joint. The defects tend to occur on the

advancing side where an abrupt microstructural transition occurs from the

highly refined nugget zone to the TMAZ while the transition was gradual on

the relatively defect free retreating side (Nandan et al 2008). The occurrence

of kissing bond defect appeared to be alloy specific and in particular,

AA5038-H321 was known to be more susceptible to this defect (James et al

2003).

The primary function of the non-consumable rotating tool pin is to

stir the plasticized metal and move the same behind it to have good joint. Tool

design plays a critical role in FSW process. Tool design influences the

material flow and in turn governs the traverse rate at which FSW can be

carried out. The factors attributed to tool design are tool material, tilt angle,

shoulder diameter and pin length, pin diameter and pin profile. The proper

21

selection of these factors to design the tool is vital to produce sound welds

without defects (Rai et al 2011). Several researchers attempted to study the

effect of tool factors on the quality of FSW joint.

The tool material must be harder than the plate material to be

welded. The heat generation between the tool shoulder and the surface of the

plates depends on the coefficient of friction. If the coefficient of friction is

higher, the heat generation will be higher. The hardness of the tool influences

the coefficient of friction. The chemical composition of the tool material

significantly affects the hardness of the tool (Padmanaban and

Balasubramanian 2009). A less hard tool will produce less heat generation

and induce defects and vice versa (Rajakumar et al 2011a). Table 2.1 shows

various tool materials commonly used for FSW of different monolithic alloys.

Table 2.1 Tool materials used for FSW of monolithic alloys

S.No Work piece material Tool material

1 Aluminum and alloys High Speed Steel, High carbon steel, Tool steel, Mild steel, stainless steel.

2 Magnesium and alloys High Speed Steel, High carbon steel, Tool steel, Mild steel, stainless steel

3 Copper and alloys Hot work steel, Ni-based super alloys, Tungsten.

4 Steels, Nickel and Titanium and its alloys

Polycrystalline Cubic Boron Nitride (PCBN), Polycrystalline Diamond (PCD),Tungsten-25% rhenium alloy, Tungsten Carbide (WC), Tungsten, Mo based alloys.

22

In general, FS we1ding tool pin is slightly shorter than the thickness

of the workpiece and its diameter is typically equal to or slightly larger than

the thickness of the workpieces (Ulysse 2002, Cavaliere et al 2006).

The tool pin profile generally has plain cylindrical, conical, threaded

and flat surfaces. Different pin profiles of FSW tools are shown in Figure 2.4.

Pin profiles with flat faces (square and triangular) are associated with

eccentricity. This eccentricity allows incompressible material flow to pass

around the pin profile. Eccentricity of the rotating object is related to dynamic

orbit. The relationship between the static volume and dynamic volume

decides the path for the flow of plasticized material from the leading edge to

the trailing edge of the rotating tool. Tool pin profiles having flat surfaces

produce a pulsating stirring action in the flowing material which improves the

joint strength (Elangovan et al 2008a).

Figure 2.4 Types of tool pin profiles of FSW tools (Elangovan et al 2009)

The pin diameter decides the volume of material that is being

plasticized. If the pin diameter is larger, the volume of material stirred will be

higher and vice versa. The smaller pin diameter will cause higher heat

supplied to a smaller volume of material. This will lead to turbulent material

23

flow and grain coarsening in the weld region. On the other hand, higher pin

diameter will cause lower heat supplied to a larger volume of material. This

will lead to insufficient material flow and inadequate plasticization

(Rajakumar et al 2011b).

Angle of spindle or tool tilt angle with respect to the work piece

surface is another important aspect of tool design. A suitable tilt of the spindle

towards the trailing direction will ensure the shoulder of the tool to hold the

stirred material and move material efficiently from the front to the back of the

pin (Arici and Selale 2007). The length of the pin decides the depth of

penetration of the weldment. If the pin length is too shorter compared to the

thickness of the plates to be welded, the weldment will not have full

penetration and form inner channel or surface groove. If the pin length is too

close to the thickness of the plates to be welded, the vertical flow of material

will be excessive and form flash resulting in a concave weld (Mishra and Ma

2005).

Figure 2.5 Selection of tool designed at TWI

24

Tool design influences heat generation, plastic flow, the power

required and the uniformity of the welded joint. The shoulder generates most

of the heat and prevents the plasticized material from escaping from the work-

piece, while both the shoulder and the tool-pin affect the material flow. In

recent years several new features have been introduced in the design of tools.

Several tools designed at TWI are show in Figure 2.5.

The whorl and MX-tri-flute have smaller pin volumes than the tool

with cylindrical pins. The tapered threads in the whorl design induce a

vertical component of velocity that facilitates plastic flow. The flute in the

MX-tri-flute also increases the interfacial area between tool and the work-

piece, leading to increased heat generation rates, softening and flow of

material. Consequently, more intense stirring reduces the traversing force, the

forward tool motion and the welding torque. Although cylindrical, Whorl and

Tri-flute designs are suitable for butt welding; they are not useful for lap

welding, where excessive thinning of the upper plate can occur together with

the trapping of adherent oxide between the overlapping surfaces. Flared-Tri-

flute and A-skew tools were developed to ensure fragmentation of the

interfacial oxide layer and a wider than is usual for butt welding. The Flared-

Tri-flute tool is similar to MX-Tri-flute with an expanded flute, while A-skew

TM tool is threaded tapered tool with its axis inclined to that of the machine

spindle. Both of these tools increase the swept volume relative to that of the

pin, thus expanding the stir region and resulting in a wider weld and

successful lap joints. Motion due to rotation and translation of the tool

induces asymmetry in the material flow and heating across the tool pin. It has

been demonstrated that during FSW, material flows primarily on the

retreating side. To overcome this problem, TWI devised a new tool, Re-stir,

which applies periodic reversal of tool rotation. The two process parameters,

tool rotational speed and pin profile contributes remarkably to the generation

of frictional heat during welding (Simar et al 2012).

25

Figure 2.6 Different types of tool pin profiles of FSW tools (Aval et al

2011a)

Aval et al (2011a) estimated the effect of tool geometry on 5 mm

thick AA5086-O and AA6061-T6 joints. They used three tool geometries as

shown in Figure 2.6 and found that the tool with a concave shoulder and a

conical probe (Figure 2.6a) with three grooves provided more homogeneous

stir zones compared to other tools due to higher heat input.

The shoulder provides confinement for the heated volume of

material. The tool shoulder diameter (D) is having a proportional relationship

with the heat generation due to friction. If the shoulder diameter is larger, heat

generation due to friction will be higher due to large contact area and vice

versa (Arora et al 2011, Mehta et al 2011).

26

Figure 2.7 Different shoulder profiles used for FS welding (Scialpi et al

2007)

Scialpi et al (2007) designed FS welding tools with different

shoulder profiles viz. scroll and fillet, cavity and fillet, and only fillet for

aluminum alloy 6082 as shown in Figure 2.7. The investigation results

showed that, for thin sheets, the best joint had been welded by the shoulder

with fillet and cavity.

Leal et al (2008) elaborated the influence of tool shoulder geometry

on material flow in 1 mm thick AA5182-H111 and AA6016-T4 joints. A tool

shoulder with a conical cavity was reported to yield an onion ring structure.

Aval et al (2011b) used finite element software to predict the

thermo-mechanical behaviors of 5 mm thick AA5086-O and AA6061-T6

joints and compared the simulation results with the observed microstructures.

Aval et al (2011c) evaluated the thermo mechanical behavior and

microstructural events of 5 mm thick AA5086-O and AA6061-T6 joints and

observed that the temperature field was distributed asymmetrically resulting

in larger thermally affected region in the AA6061 side.

Leitao et al (2009a) analyzed the tensile behavior of 1 mm thick

AA5182-H111 and AA6016-T4 joints and observed that the grain size in the

TMAZ and precipitate distribution influenced the tensile behavior. Leitao

et al(2009b) assessed the formability of 1 mm thick AA5182-H111 and

27

AA6016-T4 joints by deep drawing cylindrical cups and noticed that the

mismatch in mechanical properties between the weld and the base materials

determined the formability limits.

Park et al (2010) investigated the effect of material locations on the

properties of 2 mm thick AA5052-H32 and AA6061-T6 joints and a proper

mixing of dissimilar aluminum alloys was observed when AA5052-H32 was

kept in the advancing side.

Dilip et al (2010) examined that in friction stir welding of dissimilar

aluminum alloys, the material placed on the advancing side dominates the

nugget region. By placing the stronger of the two base materials on the

advancing side, one can achieve higher joint efficiencies.

2.4 MECHANICAL PROPERTIES

Aluminum alloys strengthened by precipitates showed a decrease of

mechanical properties in the weld zone because of the dissolution and growth

of strengthening precipitates during the welding thermal cycle (Su et al 2003,

Cabibbo et al 2003, Fonda and Bingert 2004). The weld joints produced by

FS welding had been reported to have joint efficiencies of 80-100% (Liu et al

2003, Lee et al 2003a).

Elangovan and Balasubraraniam (2008) investigated the effect of

different tool pin profiles on tensile strength of FS welded AA2219 plates.

Joints fabricated by square tool profile exhibited superior tensile properties

with joint efficiency of 61 % compared to the other tool profiles, irrespective

of welding parameters. Though the tensile strength and hardness values were

lower than the base metal, the joint efficiency was acceptable one when

compared to that of conventional fusion welding process with lower joint

efficiency not exceeding 50 %. The tensile strength of Gas Metal Arc Welded

28

(GMAW), Gas Tungsten Arc Welded (GTAW) and FS welded Al AA6061-

T6 were reported as 49 %, 63 % and 74 % of the parent metal respectively

(Lakshminarayanan et al 2009a).

The tensile strength of FS welds of Al alloy AA5083-H321 was

observed with lower value than the parent material due to thermal softening

(Dickerson and Przydatek 2003). Cavaliere et al (2005) examined the tensile

strength of dissimilar FS welded Al alloys 2024-T3 and 7075-T6 and it was

reported that the UTS of the dissimilar joints, were lower than both the base

metals 2024 and 7075. The UTS of the base metal 2024-T3, 7075-T6 and

dissimilar joint were 490 MPa, 572 MPa and 424 MPa respectively.

The range of maximum temperature measured in dissimilar FS

welding of AA2024-T35l and AA6056-T4 was found to be from 300°C to

400°C. No or very limited chemical mixing of the materials in the stir zone

could be observed with the experimental techniques used. Only an intimate

physical contact been base metals was observed. Reasonable joint efficiencies

in terms of UTS (around 56.0% of the 2024-T35l and 90% of the 6056-T4

alloys) was reported. Microscopic investigation as well as the evaluation of

local mechanical properties suggested that mechanical mixing was the major

material flow mechanism in the formation of the stirred zone (Amancio-Filho

et al 2008).

The UTS of the dissimilar FS welded joints of aluminum alloys

A356 and AA6061 had the value near to that of A356. Regardless of the

welding parameters the tensile fracture occurred at the aluminum alloys A356

base metals which had lower strength than AA6061 (Lee et al 2003a).

Lim et al (2004) concluded that the decrease in tensile elongation of

friction-stir welded aluminum alloys 6061-T651 with decreasing welding

speed or increasing rotating speed was due to the increasing tendency for the

29

plastic flow per unit time and consequently the intense clustering of Mg2Si

precipitates.

The tensile strength and tensile elongation of FS welded 5083-H32

were 301 MPa and 17% compared to that of base metal which were 306 MPa

and 22% respectively (Hong et al 2007).

Cavaliere et al (2005) measured the elongation of dissimilar FS

welded aluminum alloys 2024-T3 and 7075-T6 and it was reported that the

ductility of the dissimilar joints were lower than both the base metals 2024

and 7075. The reported tensile elongation of the base metal 2024-T3, 7075-T6

and dissimilar joint were 17%, 11% and 6% respectively.

The poor ductility observed in dissimilar FS welding of AA2624-T3

and AA6056-T4 could be explained in terms of the stress concentration

caused by the large difference in strength between base materials, leading to

confined plasticity and then, to failure. The TMAZ of AA5056-T4 (Weaker

region within weld seam) was the location where a crack could initiate and

propagate (Amancio-Filho et al 2008).

2.5 DRY SLIDING WEAR BEHAVIOR OF JOINTS

Dissimilar joints used in many places where components slide each

other. The sliding action results in wear of the components. Therefore testing

the wear rate of the dissimilar joints is essential before converting into an

application.

Pin-on disc wear apparatus has been extensively used by researchers

across the globe to test the wear rate of the material. Figure 2.8 shows the

typical pin-on-disc test setup. The pin which is made of the dissimilar joints to

30

be tested is slid against the hardened steel disc. When no lubricant is used in

the test it is known as dry sliding wear.

Figure 2.8 Schematic diagram of Pin-On-Disc test setup (Rao et al 2009)

2.6 DESIGN OF EXPERIMENTS

Experimental design methods play an important role in process

development and process trouble shooting to improve performance.

Experimental design is a powerful problem-solving technique that assists

industrial engineers for tackling process quality problems effectively and

economically. Experimental design consists of purposeful change of the

inputs (factors) of a process to observe the corresponding change in the output

(responses). Thus, experimental design is a scientific approach that allows the

researcher to understand clearly a process and know how the inputs affect the

response (Montgomery 2001). It is important to identify the factors that affect

31

the output of the process, and it is necessary to optimize these factors to

obtain the desired output. Improved performance characteristics result from

the identification of the critical factor levels that optimize the mean response

and minimize the response variability. These improved performances also

lead to the reduction of scrap and the need to rework, which greatly reduces

costs. Various types of design of experiments such as full factorial design;

Placket-Burman design, Box-Behnken design, Taguchi design, and Central

Composite Design (CCD) are available. In the present research work, the

CCD has been used.

Table 2.2 shows the list of applications of CCD to welding process.

CCD has been applied successfully for variety of welding processes including

FCAW, LBW, GTAW, FSW and Diffusion Bonding.

Some investigators employed CCD to analyze FSW of different

aluminum alloys and composites. The investigators developed precise

mathematical models to predict the responses and the effect of different

process parameters (four or five or six) on the response were evaluated using

the developed mathematical models. The mathematical models were

developed using several statistical software packages such as Quality

America, SYSTAT, MINITAB, Design Expert, STATISTICA, SPSS and

SAS. Some investigators optimized the developed models to either maximize

or minimize the response(s) using Microsoft Excel solver or in the statistical

software package itself.

Elangovan et al (2008b and 2009) carried out FSW of AA2219 and

AA6061 according to four factor CCD design and developed mathematical

models to predict the influence of FSW parameters on the tensile strength of

the joints. The four factors considered were tool rotational speed, welding

speed, axial force and tool pin profile. All those factors significantly affected

the joint strength.

32

Table 2.2 List of applications of CCD to welding process

ReferenceWelding Process Predicted response

Kannan and Murugan 2006a FCAW Bead geometry Palani and Murugan 2006 FCAW Bead geometry Manonmani et al 2007 LBW Bead geometry Balasubramanian et al 2007 GTAW Corrosion rateBalasubramanian et al 2008a GTAW Grain size & Hardness

Balasubramanian et al 2008b GTAW Impact toughness Balasubramanian et al 2008c GTAW Corrosion rateBabu et al 2008 GTAW Tensile strength & Grain

sizeGiridharan and Murugan 2009 GTAW Bead geometry Mahendran et al 2010a Diffusion

Bonding Bonding strength & Layer thickness

Mahendran et al 2010b DiffusionBonding

Bonding strength & Layer thickness

Lakshminarayanan et al 2008 PTAW DilutionElangovan et al 2009 FSW Tensile strength Karthikeyan and Balasubramanian 2010

FSSW Shear strength

Shanmugasundaram and Murugan 2010

FSW Tensile behavior

Rajakumar et al 2010 FSW Grain sizeRajakumar et al 2011a FSW Tensile strength &

Corrosion rateGopalakrishnan and Murugan 2011

FSW Tensile strength

Dinakaran and Murugan 2011 FSW Tensile strength and wear rate

33

Shanmugasundaram and Murugan (2010) employed a similar

procedure to study the effect of FSW parameters on tensile strength and

elongation of dissimilar AA2024 - AA5083 joints.

Rajakumar et al (2010 and 2011b) and Rajakumar and

Balasubramanian (2012) respectively conducted FSW of AA6061, AA6061

and AA1100 according to six factor CCD design and developed mathematical

models to predict the influence of FSW parameters on tensile strength, grain

size, hardness and corrosion rate of the joints. The six factors considered were

tool rotational speed, welding speed, axial force, shoulder diameter, pin

diameter and tool hardness. Rajakumar et al (2011b) optimized the developed

models using the software Expert to maximize the tensile strength of the

joints.

Dinakaran and Murugan (2011) carried out FSW of AA6061 with 0-

10 wt. % ZrB2 in-situ Composite Butt joints according to four factor CCD

design and developed a mathematical model to predict the influence of FSW

parameters on sliding wear behavior of the joints. The four factors considered

were tool rotational speed, welding speed, axial force, and ZrB2 content.

They observed that all those FSW parameters have significant effect on the

wear behavior of the joint.

Gopalakrishnan and Murugan (2011) carried out FSW of AA6061

with 3-7wt. % TiC MMC according to five factor CCD design and developed

a mathematical model to predict the influence of FSW parameters on tensile

strength of the joints. The five factors considered were tool rotational speed,

welding speed, axial force, tool pin profile and TiC content. They observed

that all those FSW parameters except tool rotational speed influenced the joint

strength.

34

2.7 ARTIFICIAL NEURAL NETWORK MODELING

Artificial neural networks are distributed information processing

systems that simulate the biological learning processes. It basically consists of

a number of interconnected processing elements, commonly referred to as

neurons. The neurons are logically arranged in two or more layers and interact

with each other via weighted connections. These scalar weights determine the

nature and strength of the influence between the interconnected neurons.

Neural networks essentially learn through the adoption of their connection

weights. Common configuration of neural networks is full interconnection

and feed-forward. Each neuron is connected to all the neurons in the next

layer. The input layer takes the data to the neural network, and the output

layer holds the response of the network to the input (Mounayri et al 2010).

The basic objective of neural networks can be defined as the search

for efficient learning procedures, which may be applied to networks of many

simple processing units, representing neurons, in order that complex internal

representation of the environment may be constructed and recalled through

the process of learning and relating experiences.

A neural network learns from a set of training patterns. Through

training, they generalize the features within the training patterns and stores

those generalized features internally in its architecture. The learning takes

place mainly through the readjustment of the weights.

Artificial neural networks can be classified as either ‘supervised’ or

‘unsupervised’ based on the learning type. Supervised learning requires

pairing of each input value with the target value representing the desired

output and a ‘teacher’ who provides error information. In unsupervised

learning the training set consists of input vectors only. The unsupervised

35

learning procedures construct internal models that capture regularities in the

input values without receiving additional information.

Description of artificial neurons, the characteristic features of neural

networks and the network architecture are discussed below:

The following are the characteristics of Neural Networks

1) Neural networks exhibit mapping capabilities. They can map

input patterns to their associated output patterns.

2) They learn by examples. Neural network architectures can be

trained with known examples of a problem before they are

tested for their inference capability.

3) The neural networks possess the capability to generalize. Thus

they can predict new outcomes from past trends.

4) They are robust systems and are fault tolerant. They can recall

full patterns from incomplete, partial or noisy patterns.

5) The neural networks can process information in parallel, at

high speed, and in a distributed manner (Rajasekaran and

Vijayalakshmi 2003).

2.8 NETWORK ARCHITECTURES

In general, there are three fundamentally different classes of

network architectures, which are classified according to their learning

mechanisms:

36

2.8.1 Single layer Feed Forward Networks

This type of network comprises of two layers, namely the input

layer and the output layer. The input layer neurons receive the input signals

and the output layer neurons receive the output signals. The synaptic links

carrying the weights connect every input neuron to the output neuron. Such a

network is feed forward type. The input layer merely transmits the signal to

the output layer. Hence, the name single layer feed forward network.

2.8.2 Multilayer Feed Forward Networks

This network is made up of multiple layers. This architecture

besides possessing an input and an output layer also has one or more

intermediary layers called hidden layers. The hidden layer aids in performing

useful intermediary computations before directing the input to the output

layer. The input layer neurons are linked to the hidden layer neurons and the

weights on these links are referred to as input-hidden layer weights. The

hidden layer neurons are linked to the output layer neurons and the

corresponding weights are referred to as hidden – output layer weights.

2.8.3 Recurrent Network

A recurrent network distinguishes itself from a feed forward neural

network that is, it has at least one feedback loop. A recurrent network may

consist of a single layer of neurons with each neuron feeding its output signal

back to the inputs of all the other neurons. There could also be neurons with

self feedback links. The output of a neuron is fed back into itself as input.

Artificial neural networks have been used for many years in different forms,

but recently it is being applied to solve different types of problems. It is

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mainly due to its excellent performance in the modeling of nonlinear

relationships involving a multitude of variable in place of conventional

techniques. In the fields of materials joining, computer aided artificial neural

network (ANN) modeling has gained increased importance. Few of the works

are depicted below:

Prediction of LASER butt joint welding parameters using back

propagation and learning vector quantization networks had been attempted by

Jeng et al (1990). Non linearity and input - output mapping are the two most

important benefits in the use of neural networks. Hence neural networks have

been adopted to model the input - output relationship of non-linear and

interconnected systems like welding applications.

Chang and Na (2001) have developed a combined model of finite

element analysis and neural network which can be effectively applied for the

prediction of LASER spot weld bead shapes of stainless steel welded with gap

and without gap.

Nagesh and Datta (2006) applied the back propogation neural

network to predict weld bead geometry and penetration in sheiled metal arc

welding. They reported those neural networks are powerful tool for the

analysis and modeling. Kannan and Murugan (2006b) developed back

propagation neural network model for weld bead geometry and dilution in the

FCAW.

Dutta et al (2007) modeled the gas tungsten arc welding process

using conventional regression analysis and neural network-based approaches

and found that the performance of ANN was better compared with regression

analysis.

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Ates (2007) presented the use of artificial neural network for

prediction of gas metal arc welding parameters. Okuyucu et al (2007) showed

the possibility of the use of neural networks for the calculation of the

mechanical properties of friction stir welded (FSW) aluminum plates

incorporating process parameters such as rotational speed and welding speed.

Lakshminarayanan and Balasubramanian (2009b) used BP

algorithm with a single hidden layer improved with numerical optimization

techniques for predicting the tensile strength of FS welding of AA7039. The

predictive ANN model is found to be capable of better predictions of tensile

strength within the range that they had been trained. He concluded that the

ANN model is much more robust and accurate in estimating the values of

tensile strength when compared with the response surface model.

2.9 METALLURGICAL ANALYSIS

Focusing attention on FS welding, the structure of full Weld Zone

(WZ), due to severe mechanical stresses experienced by material, shows an

interesting variety of zones. The sight of weld bead, across the section, shows

a region of deep deformation that is often referred to as the WZ (Norman et al

1999, Norman et al 2000, Krishnan 2002a, Krishnan 2002b, Lockwood et al

2002) this zone shows a very fine equiaxed grain structure due to the dynamic

recrystallization process (Jata and Semiatin 2000, Lockwood et al 2002,

Charit et al 2002, Rhodes et al 2003) and is characterized by the presence of

so-called onion rings. The thin zone subjected directly to the action of tool

shoulder is referred to as flow arm. Adjacent to the nugget zone is a zone that

though has been not directly subjected to pin or shoulder action, due to

internal shear stresses, has experienced a severe thermo-mechanical alteration,

this zone is referred to as Heat Affected Zone (HAZ). The sight of the top

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surface of the weld bead, shows semicircular rings referred as banded

microstructure, the distance between such rings depends on tool advance per

revolution: band spacing will increase as this ratio grows, resulting in a less

homogenous structure within the weld (Sutton et al 2002).

The FS welding of non-heat treatable alloys caused microstructural

damage in the heat affected zone. This resulted in recovery, recrystallisation

and grain growth, therefore, the loss in strength was not as severe as that

found in the heat treatable alloys where strengthening precipitates may

dissolve or coarsen. The weakest part of a wild in non-heat treatable

aluminum alloys was generally the weld metal, in contrast to most heat

treatable - alloys where the HAZ had the lowest strength (Cross et al 1993).

Cavaliere et al (2005) analyzed the microstructure of various zones

of dissimilar FS welded aluminum plates of 2024-T3 and 7075-T6 with

2.5mm thickness. It was reported that the weld nugget had very fine grains

due to its higher temperature and severe plastic deformation was not so severe

to cause recrystallization. By moving away from the weld centre and towards

7075 side, the grains dimensions were increased and the grains became less

equiaxed than those closer to the weld centre. At a distance of 4mm from the

weld centre many of the prior grains of the parent material started to appear

which was corresponding to the HAZ, as the hardness was low compared to

the base metal. The hardness dropped here because the precipitates were

coarsened. Similar microstructural observations were made in 2004 side of

the weld. SEM microstructure of the fractured surface was comprised of voids

of various sizes.

Cavaliere et al (2006) observed fine recrystalized grains at Weld

Nugget (WN) of dissimilar FS welded aluminum alloy AA2024-T3 and

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AA 7075-T6. Also the fracture surface of the welded specimens tested under

tension was observed with a broad population of microscopic voids of

different size and shape, and at room temperature the material showed ductile

fracture.

Aval et al (2011b) observed the macrostructure of the cross-sections

of the dissimilar welded samples as shown in Figure 2.9 where no typical

FSW onion rings can be seen in the stir zone of the welds. The AA5086-O

alloy is on the left, and the AA 6061-T6 alloy is on the right side.

Figure 2.9 Macrostructures of welded samples (Aval et al 2011b)

Ouyang et al (2006) observed material flow pattern and alternative

lamellae in a dissimilar 6061 aluminum alloy with copper is shown in

Figure 2.10. The weld consists of mixing of copper and aluminum alloy

mainly of several intermetallic compounds such as CuAl2, CuAl, and Cu9Al4

together with small amounts of Al and a face-centered cubic solid solution of

Al in Cu distributed at the bottom of the weld nugget.

Figure 2.11 shows the microstructures of the weld zone in case

AA356 alloy was fixed at the retreating side. The advancing side of the stir

zone (Figure 2.11a) showed the fine and recrystallized grain structure of

AA6061 alloys. Its grain size was much smaller than that of the AA6061 alloy

base metal. Onion ring patterns (Figure 2.11b) were continuously observed in

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the retreating side of the stir zone and randomly observed in the advancing

side of the stir zone. Onion ring patterns were composed of the lamellar like

structure of stacked AA356 and AA6061 alloy in turn and show the same

width of 20 - 25 µm, respectively. The upper region of the stir zone

(Figure 2.11c) had a slightly elongated and recrystallized 6061 Al alloys and

thinly scattered Si particles. The exact central region (Figure 2.11d) of the stir

zone and other regions where the onion ring patterns was not observed

showed the homogeneously dispersed Si particles comparing that of A356

base metal which the eutectic Si particles are partially distributed.

Figure 2.10 Material flow behaviors of dissimilar joints (Ouyang et al

2006)

Ouyang and Kovacevic (2002) examined the material flow behavior

in friction stir butting welding of 2024Al to 6061Al plates of 12.7 mm thick.

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Three different regions were revealed in the welded zone. The first was the

mechanically mixed region characterized by the relatively uniformly

dispersed particles of different alloy constituents. The second was the stirring-

induced plastic flow region consisting of alternative vortex-like lamellae of

the two aluminum alloys. The third was the unmixed region consisting of fine

equiaxed grains of the 6061Al alloy. They reported that in the welds the

contact between different layers is intimate, but the mixing is far from

complete. However, the bonding between the two aluminum alloys was

complete. Further, they attributed the vortex-like structure and alternative

lamellae to the stirring action of the threaded tool, in situ extrusion and

traverse motion along the welding direction.

Figure 2.11 Microstructures of the stir zone of dissimialar joints AA6061 with A356 (Lee et al 2003b)

43

Figure 2.12 shows the macrostructure of dissimilar FS welding of

AA6082 with AA2024. The cross-section typical feature of the nugget zones

of dissimilar aluminum FSW joints is shown in Figure 2.13. The nugget zones

which appeared to be composed of different regions of both the alloys were

severely plastically deformed. Such complex deformation produces the vortex

structure composed of alternative lamellae of 2024 and 6082 aluminum alloys

(Cavaliere et al 2009).

Figure 2.12 Macrographs of the dissimilar joints AA6082 with AA2024

(Cavaliere et al 2009)

Figure 2.13 Microstructure of the joints AA6082 with AA2024

(Cavaliere et al 2009)

Yong et al (2010) studied dissimilar friction stir welding between

AA5052 and AZ31 magnesium alloy and reported that at the top of the stir

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zone, AA5052 and AZ31 alloys are simply bonded, while onion ring structure

which consisted of aluminum bands and magnesium bands is formed at the

bottom of the stir zone.

2.10 MICROHARDNESS SURVEY

A number of investigations demonstrated that the change in

hardness in the friction stir welds is different for precipitation-hardened and

solid-solution- hardened aluminum alloys. FSW creates a softened region

around the weld center in a number of precipitation-hardened aluminum

alloys (Benavides et al 1999, Sato et al 2001 Mishra and Ma 2005, Murr

2010). It was suggested that such a softening is caused by coarsening and

dissolution of strengthening precipitates during the thermal cycle of the FSW.

Sato et al (1999) have examined the hardness profiles associated with the

microstructure in an FSW AA6063-T5. They reported that hardness profile

was strongly affected by precipitate distribution rather than grain size in the

weld.

For the solid-solution-hardened aluminum alloys, generally, FSW

does not result in softening in the welds (Li et al 2000, Sato et al 2001,

Svesson et al 2000, Mishra and Ma 2005). For AA5083-O containing small

particles, the hardness profile was roughly uniform in the weld, whereas for

AA1080-O without any second phase particles, the hardness in the nugget

zone was slightly higher than that in the base material, and the maximum

hardness was located in the Thermo Mechanically Affected Zone (TMAZ)

(Sato et al 2001). Microstructural factors governing the hardness in the FSW

welds of the solid-solution-hardened aluminum alloys were suggested by

various investigators. In an investigation on the microstructure and properties

of FSW AA5083-O, (Svesson et al 2000) reported that the nugget zone had

fine equiaxed grains. They suggested that the hardness profile mainly

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depended on dislocation density, because the dominant hardening mechanism

for AA5083 is strain hardening. On the other hand, (Sato et al 2001) reported

that FSW created the fine recrystallized grains in the nugget zone and

recovered grains in the TMAZ in AA5083-O with the nugget zone and the

TMAZ having slightly higher dislocation densities than the base material.

They concluded that the hardness profile could not be explained by the Hall–

Petch relationship, but rather by orowan strengthening, namely, the hardness

profile in the FSW AA5083 was dominantly governed by the dispersion

strengthening due to distribution of small particles. In this case, the inter

particle spacing is likely to be much lower than the grain size.

The Vickers hardness of Gas Metal Arc Welded (GMAW), Gas

Tungsten Arc Welded (GTAW) and FS welded AA6061-T6 were reported as

58,70 and 85 respectively compared to that of parent metal which was 105

VHN (Lakshminarayanan et al 2009a). The loss of hardness and strength

found in the HAZ of heat-treated alloys and strain hardened alloys was due to

over-ageing or lowering the dislocation density. It was indicated that

microhardness increased in the WN of AA5083-O, where as it decreased in

the WN of AA5083-H321 when compared to that of base metal (Threadgill

1997).

The hardness measured along the transverse direction of the FS

welds of AA5083-H321 exhibited a lowered hardness in WN, TMAZ and

HAZ compared to the base metal (Threadgill 1997, James et al 2003). The

decrease in strength and hardness in this zone was generally attributed to the

softening of the strain hardened structure (i.e. annealing) or coarsening of the

precipitates. The increased vales of hardness found at the centre of the weld

were probably related to the dynamic recrystallized grains characterized by

fine and equaxied grain structure which was formed by the plastic

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deformation and the simultaneously occurred frictional heat as reported by the

other authors (Czechowski 2005). As the outer edge of the shoulder have the

greatest velocity which led to the greatest temperature rise at this point, and

therefore degree of annealing that occurred would be maximum. So that

TMAZ was observed with minimum hardness. From the minimum hardness

values there was a gradual increase in the hardness as the distance from the

tool shoulder increases until the hardness of the parent plate was attained, and

this region represented the HAZ (Threadgill 1997).

Cavaliere et al (2005) studied the mechanical properties of

dissimilar FS welding of AA2024-T3 and AA7075-T6 when the welding was

done perpendicular to the rolling direction of both the base metal plates. The

Vickers hardness profile to the weld zone was measured on a cross section

and perpendicular to the welding direction using a Vickers indenter with a

200 kg load for 15 sec. The authors reported a steep drop in the hardness in

the WN and TMAZ while compared to that of both the base metals.

Cavaliere et al (2006) investigated the hardness of dissimilar FS

welding of AA alloys 2024-T3 and 7075-T6, and it was found that the nugget

zone had slightly higher hardness compared to the TMAZ. While moving

away from WN towards 7075 side minimum hardness was found at the

TMAZ, but no recrystallization apparently occurred because of the low

temperature filed originated by the FS process. In HAZ, where the hardness

was low with respect to the base metal and higher than TMAZ, precipitates

were coarsened (Jata et al 2000). Similar behavior had been observed in the

FS weld zone of the 2024 alloy side of the weld.

Dilip et al (2010) observed hardness on the advancing side of the

dissimilar FS welded AA2219 with AA5083, there was a significant drop in

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hardness from the AA2219 unaffected base material to the weld nugget

boundary. The weld nugget hardness was considerably lower than that of the

AA2219 base material. On the retreating side, only slight drop in hardness

was observed from the AA5083 unaffected base material to the weld nugget

boundary. The WN showed higher hardness compared to the AA5083 base

material. Also AA2219 base material showed significantly higher hardness

compared to 5083 base material.

The HAZ was observed with a decrease in microhardness, in

dissimilar FS welding of AA2024-T351 and AA6056-T4. It was due to the

accelerated ageing and recovering process caused by the weld thermal cycle

(Threadgill 1997, Amacio - Filho et al 2008).

Hardness in the TMAZ and WN regions were slightly decreased in

comparison with the base metals. The higher hardness observed in the WN

and TMAZ than that in the HAZ can be attributed to the increase in the

amount of dissolved precipitates that increased the contents of alloying

elements (in-solution) available for the precipitation hardening during natural

aging (Khodir et al 2008).

The minimum hardness observed in HAZ of AA6061 alloy side.

Fluctuating hardness observed in the Stirring-induced Plastic Flow Region

(SPFR) of the nugget zone that is related to the alternative lamellae of the two

aluminum alloys. The maximum hardness observed in the Mechanically

Mixed Region (MMR) of the nugget zone, in which the relatively uniform

mixing of two Al-alloys is reached. No distinct variations in hardness are

found in both the TMAZ and HAZ of the AA2024 alloy side as contrasted to

the parent metal of the AA2024 (Ouyang et al 2002).

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Scialpi et al (2008) noticed that the minimum microhardness on

AA6082 alloy side at a distance of about 3 mm from the joint centre line of

dissimilar FS welded aluminum alloy AA2024-T3 and AA6082-T6 sheets

and its value was very close to the AA6082 joint. The hardness in the nugget

area of dissimilar FS welding of AA6061-T6 with AA6082-T6 is always

higher than the values in the transition between the TMAZ and the HAZ. Also

the average hardness of the WN was found to be significantly lower than the

hardness of the base alloy (Moreira et al 2009).