8

CHAPTER 2

LITERATURE SURVEY

2.1 INTRODUCTION

Extensive survey of available literature was carried. A summary of

the literature review is presented under the following headings:

1.� Aluminium matrix composites

2.� Hybrid metal matrix composites

3.� Fabrication techniques

4.� Microstructural investigations

5.� Design of experiments

6.� Artificial Neural Networks

7.� Wear testing

8.� Property evaluation

9.� Heat treatment of composites

10.� Machining characteristics of composites

2.2 ALUMINIUM MATRIX COMPOSITES

Aluminium Matrix Composites (AMCs) refer to the class of light

weight high performance aluminium centric material systems. In AMCs one

of the constituents is aluminium / aluminium alloy, which forms a continuous

phase and is termed as matrix. The other constituent is embedded in this

9

aluminium / aluminium alloy matrix and serves as reinforcement, which is

usually non6metallic material (common ceramics such as SiC and Al2O3).

Major advantages of AMCs, compared to the unreinforced materials are

greater strength, improved stiffness, reduced density, better high temperature

properties, enhanced abrasion and wear resistance as well as improved

damping capabilities (Surappa 2003c). On account of the excellent physical,

mechanical properties of AMCs, they are applied widely in aircraft

technology, electronic engineering and automotive industries. Of all the

commercial aluminium alloys, 6061 is quite popular choice as a matrix

material to prepare metal matrix composites. Several researchers have

investigated aluminium matrix composites. Various types of matrices and

reinforcement materials used and outcome of the research work done by the

previous researchers on aluminium matrix composites are given in Table 2.1.

Table 2.1 Investigations on aluminium matrix composites by previous

researchers

Sl.No Investigators Matrix Dispersoids Remarks

1 Mahagundappa

Benal and

Shivanand (2007)

Al 6061 9wt.% SiC

particulates

and 0, 1, 3,

5wt.% E6glass

fibre

Wear rate

increased with

increase in the

sliding distance

and decrease with

increasing the

weight percentage

of reinforcing

materials.

2 Basavarajappa et al

(2007)

Al 2219 15wt.%SiCp

and

3wt.%graphite

particles

The graphitic

composite

exhibited less

degree of

subsurface

deformation.

10

Table 2.1 (Continued)

Sl.No Investigators Matrix Dispersoids Remarks

3 Rodriguez et al

(2007)

Al 8090 15vol.%SiCp Composite

exhibited higher

wear resistance.

Friction coefficient

is always higher in

the composite.

4 Du Jun et al (2007) SAE321

alloy

Al2O3 and

carbon short

fibre

Heat6treatment was

beneficial to the

improvement of

dry sliding friction

and wear property

of the composites.

5 Ramachandra and

Radhakrishna

(2007)

Al (12wt.%

Si) alloy

15wt.% of

flyash

particulates

Wear resistance of

composite

increased with

increase in flyash

content, but

decreases with

increase in normal

load, and track

velocity.

6 Ramesh and Mir

Safiulla (2007)

Al 6061 SiC, Al2O3 Composites

possess higher

microhardness and

lower wear rates.

7 Olivier Beffort et al

(2007)

Pure

aluminium

SiC particles Hardness of

composite was

increased. Addition

of the elements

Mg, Zn and Cu

does not

significantly

improve the

bending strength of

as6cast composite.

8 Uyyuru et al (2006) Al6Si alloy SiC particles Applied load is

most important

parameter on wear

performance.

11

Table 2.1 (Continued)

Sl.No Investigators Matrix Dispersoids Remarks

9 Vedani et al (2006) Al 6061 20wt.% Al2O3

particles

Improvement in

ductility as well as

strength of

composite.

10 Kilickap et al

(2005)

Pure

Aluminium

20wt.% SiC

particles with

an average

size of 24 Hm

Higher cutting

speeds and lower

feed rates produced

better surface

quality of

composite.

11 Ramesh et al

(2005)

Al 6061 TiO2 Wear coefficient

decreased at higher

loads and larger

sliding distances.

12 Mandal et al (2004) Pure

aluminium

Short steel

fiber

Wear resistance

and coefficient of

friction of

composites

improved.

13 Narender Singh et

al (2004)

Pure

aluminium

10% SiCp For larger current

settings in EDM

MRR and TWR

found to be higher.

14 Cambronero et al

(2003)

AA 7015 B4C, TiB2 and

Si3N4

Hardness is

increased by the

ceramic addition

and better wear

behaviour of

composite.

15 Korkut (2003) Al 2024 Al2O3

particulates

(15 vol.%)

Under severe wear

condition Al2O3

particulates were

broken and

particulates

affected wear

behavior badly.

16 Sug Won Kim et al

(2003)

Al–Si–Cu–

Mg–(Ni)

alloy

SiCp Wear decreased

with increase of the

sliding speed.

12

Table 2.1 (Continued)

Sl.No Investigators Matrix Dispersoids Remarks

17 Bekheet et al

(2002)

Al 2024 SiC particles The presence of

SiC particles

refined the

structure of the

matrix. The peak

hardness of

composites is

slightly higher than

that for Al 2024

alloy.

18 Sharma (2001) Al 6061 Garnet

particles (90–

150 Hm)

Wear resistance

was superior to that

of unreinforced

matrix alloy. It

increases with

increasing wt.% of

garnet. The average

coefficient of

friction of the

composite is lesser

than that of matrix

alloy.

19 Tiancheng Zhang

and Li (2001)

Pure

aluminium

1wt.%Y2O3

powder

Improved dry

sliding wear

properties of

composites.

20 Rong Chen et al

(2000)

A356

aluminum

alloy

15 vol.%

Silicon

carbide

particles

Volume loss of the

composite is

increased with the

increase of the

load.

21 Lin et al (1998a) Al 6061 6wt.%graphite

particles

Weight loss is

reduced with

increasing content

of graphite

particulates.

22 Szu Ying Yu et al

(1997)

Al 6061 SiC particles Wear resistance of

the composite was

increased.

13

Table 2.1 (Continued)

Sl.No Investigators Matrix Dispersoids Remarks

23

Hung et al (1996) A16Li alloys 10620 wt.% of

SiC

particulates

Tool wear was

reduced during

machining.

24 Wilson and Alpas

(1996)

A356 alloy Graphite

particles

Wear of the

composite reduced.

25 Qunji Xue and

Mingwu Bai (1996)

Al 2024 Molybdenum

powder (15

vol.%)

With increasing

load, the wear rate

increases quickly

and the wear

mechanism is

ploughing with

delamination.

26 Shyong and Derby

(1995)

Al 2014, Al

6061

SiC particles

(3 – 20Hm)

Maximum wear

resistance was

achieved when the

composites were in

the peak6aged

condition.

27 Hall Jody et al

(1994)

Al 2124 SiC particles Increased particle

fracture with

increased SiCp size

and volume

fraction.

28 Doel et al (1993) Al 7075 SiC particles As particle size is

increased from 13

to 60Hm toughness

of composites was

increased.

29 Alpas and Zhang

(1992)

A356 alloy 10620 vol.%

of SiC

particles

Sliding wear

resistance of

aluminium6silicon

alloys varies with

the applied

pressure.

30 Prasad and Mc

Connell (1991)

Al6Si alloy 20 vol.% of

SiC particles.

Wear resistance

was improved.

14

2.3 HYBRID METAL MATRIX COMPOSITES

When two or more than two reinforcements are added to the matrix

then the resulting composite is called hybrid composite. The addition of

reinforcement materials increases the mechanical properties such as hardness,

and tensile strength of composite. The reinforcement for a matrix alloy is

selected based on the property requirement for the composite, wettability of

the reinforcement and the matrix compatibility. The performance of Al6MMC

can be influenced by chemical reactions occurring between the aluminium

and the reinforcing element. Heat treatment can increase the abrasive wear

resistance of Al6MMC’s. Aluminium matrix composites containing solid

lubricants such as graphite and MOS2 showed better friction and wear

performance. Large hard particles increase more intensively the wear

resistance than the smaller particles. The abrasive wear resistance of the

aluminium composites depends on the size of dispersoids as well as the size

of abrasive particles (Mihaly Kozma 2003, Pedro Fernandez at al 2006, Urena

et al 2004). Several investigations have been carried out on composites

reinforced with two or more reinforcements.

Wilson and Alpas (1996a) investigated the effect of temperature on

the wear performance of Al matrix composites reinforced with 20 vol.% SiC

and 10% vol.% graphite particles. The graphite particles had been coated with

the thin layer of nickel to enhance wetting with the molten aluminium alloy.

Experimental results revealed that the addition of hard ceramic particulates to

Al alloy matrices improved their resistance to seizure at elevated

temperatures.

Tedguo and Tsao (2000) investigated the tribiological behavior of

self lubricated Al hybrid composites reinforced with SiC and graphite

particles. They observed that as the amount of graphite particles increased, the

hardness of the composite decreased. The seizure phenomenon did not occur

15

with the hybrid composites. Friction coefficient decreased as the percentage

of graphite addition increased. Wear rate of the composite decreased due to

the addition of SiC particles.

Basavarajappa et al (2007a) investigated the influence of sliding

speed on the dry sliding wear behaviour of graphite particle and SiC

reinforced hybrid aluminium composites. They reported that the addition of

SiC particles increased the wear resistance of the composite and the addition

of graphite reinforcement further increases the wear resistance at all sliding

speeds and effectively avoids the occurrence of severe wear. The degree of

subsurface deformation in graphite composite was less than that of the

graphite free composite.

2.4� FABRICATION TECHNIQUES

The most widely applied methods for the production of composites

are based on casting techniques such as squeeze casting of porous ceramic

performs with liquid metal alloys, stir casting and powder metallurgy methods

(Kaezmar et al 2000a). Several research works have been carried out for the

production of composites. They have applied various methods to fabricate the

composites. Among these methods stir casting method has been found to be

best suited for fabricating aluminium composites. Because this method is

highly versatile, most economical and easy to use.

Mandal et al (2004a) fabricated aluminium matrix composites using

stir casting route. They preheated the reinforcement to 475K and added to the

center of the vortex formed by stirring. The preheating temperature of the die

was maintained at 825K. They observed that, volumetric wear during wear

test increased with increasing applied load.

16

Chaudhury et al (2004) produced Al62Mg611 TiO2 (rutile)

composite using vortex method. They used electric resistance furnace for

melting aluminium and the temperature was maintained at 1073K. The

reinforcement was preheated to 475K. The melt was stirred with a stirrer at a

rotational speed of 200rpm. They found that, the addition of rutile particles

tend to increase the hardness of composites.

Sahin (2005) fabricated the composites by stir casting route. A

graphite mixer was fixed on the mandrel of the drilling machine for mixing

process. The speed of the stirrer was maintained at 670rpm. It was found that

the distribution of SiC particles in the composite was uniform.

Senthil Kumar et al (2008) fabricated Al6SiCp composites using stir

casting route. They investigated the influence of electro chemical process

parameters on the metal removal rate and surface roughness. They observed

that the metal removal rate increased with increase in applied voltage,

electrolyte flow rate and tool feed rate. The surface roughness of composite

increased with increase in applied voltage.

Ramachandra and Radhakrishna (2007a) fabricated aluminium

composites reinforced with fly ash particles using liquid metallurgy route.

They added flux to the melt in order to minimize the oxidation of molten

metal. The super heated molten metal was degassed at a temperature of

1025K. The reinforcement particles were preheated to 875K.

Muthukrishnan et al (2008) adopted stir casting route for

fabricating Al6SiC composites with the addition of hexachloroethane tablets

to the molten aluminium for effective degassing. The preheating temperature

of SiC particles was maintained at 875K. They also added magnesium chips

in order to make up for its loss during melting as well as to improve wetting.

17

Mak et al (2006) investigated the effect of particle size on the

particulate distribution in aluminium particulate metal matrix composites

fabricated using casting technique. They preheated reinforcing particles to

575K for about 45min. The mixing time was maintained at 15min. Argon gas

was supplied to the mixing chamber and its pressure was increased to 10 bar

during mixing process. The particle size was varied between 150µm to

350µm. They reported that, particle size significantly affects the particle

distribution.

Ipek (2005) fabricated SiC reinforced 4147 Al matrix composites

using liquid metallurgy route. Melt was heated upto 910K and stirring was

carried out approximately 400rpm speed for 30min under CO2 gas atmosphere

to avoid oxidation.

Weijie Lu et al (2001) produced composite using casting method.

The microstructure of the composite showed a homogeneous distribution of

reinforcements. The strengthening observed in the composite was due to load

bearing by reinforcements, refinement of matrix alloy’s grain size and

intrinsic strengthening due to high dislocation density in the matrix alloy.

Prasanna Kumar et al (2006) investigated the wear behaviour of

aluminium composites reinforced with garnet particles. A liquid metallurgy

technique was used to fabricate the composite. Preheated reinforcing particles

were added to the aluminium melt at a temperature of 840K. The stirring was

continued at a speed of 400rpm for about 5min and then the molten mixture

was poured into cast iron permanent mould.

Ranganath et al (2001) fabricated composites reinforced with

garnet particles using liquid metallurgy route. They reported that the matrix

was first superheated above its melting temperature and stirring was initiated

to homogenize the temperature. The temperature was then lowered gradually

18

until the alloy reached a semi solid state. At this temperature, the

reinforcement particles were added and stirring were continued until particle

and matrix wetting occurred. The size of the garnet particles was varied

between 30650µm.

2.5� MICROSTRUCTURAL INVESTIGATIONS

The main objective of the microstructure study is to find the

distribution of reinforcing particles with in the matrix as well as

reinforcement of structure if any. This investigation can be carried out by

using either optical microscope or Scanning Electron Micoscope (SEM). This

study is also used to find out different types of wear mechanisms.

Ipek (2005a) studied the microstructure of Al 4147 composites and

also worn out surfaces using Scanning Electron Microscope (SEM). He

observed the presence of the mild wear and severe wear region in the

specimens after wear tests.

Ramesh et al (2005a) observed the microstructure of both the

unreinforced alloy Al 6061 and its composites. Micrographs clearly revealed

minimal micro porosities in the castings. Uniform distribution of silicon

carbide reinforcement particles with in the matrix was observed using

Scanning Electron Microscope.

Gurcan and Baker (1995) studied the microstructures of the pin

surface after wear test using SEM. The microstructure showed micro cutting,

microploughing and microchipping with long, continues grooving on

composite pin surface.

Prasad (2004) studied the wear surfaces, sub surface regions and

debris using SEM. He attributed the wear to delamination and formation of

wear grooves.

19

Mahadevan et al (2006) investigated the micro structures of AA

60616SiCp composites. A homogeneous distribution of the SiC particulates in

the matrix was observed which was attributed to the good wettability of Al

matrix for SiC particulates.

2.6� DESIGN OF EXPERIMENTS

Design of experiments (DOE) is a technique for studying any

situation that involves a response that varies as a function of one or more

independent variables. This approach helps to understand, how the change in

the levels of application of a group of parameters affects the response.

Various techniques are available from the statistical theory of experimental

design (Davies 1978, Cochran and Cox 1987, Douglus Montgomery 2003,

Harris and Smith 1983, Murugan and Gunaraj, 2005), which is well suited for

engineering investigations. The main statistical based approaches that have

been investigated in past work are discussed below.

The first approach, Taguchi method, offered several innovations

including a widely used procedure for addressing the impact of “noise

factors” which can be controlled during experimentation but not during

standard operations. Yet, despite the many advantages of Taguchi methods,

there are some limitations:

•� The total number of experimental runs using product arrays can

make experimental costs substantially higher than when

classical DOEs are used because the total number of runs is

often higher for a given number of factors.

•� The standard Taguchi modeling methods do not permit

estimation of interactions between control factors, potentially

resulting in poor engineering choices (Phadke 1989, Myers and

Montgomery 1995).

20

Charles et al (2006) developed a mathematical model for machining

of hybrid aluminium composites reinforced with SiC and fly ash particles

using five level factorial design concept. They used Analysis of Variance

(ANOVA) technique to calculate the regression coefficients as well as to

check the significance of the developed model. They observed that surface

roughness increased with increasing the vol.% of SiC particles and current but

decreased with increase in pulse duration.

Narender Singh et al (2004a) applied three factor three level design

of experiment method for developing a model for machining of Al610% SiCp

composites using Electric Discharge Machining (EDM). They reported that

the model was adequate and added that the flushing pressure of the dielectric

has considerable effect on the metal removal rate and tool wear rate. The

cooling rate of the tool increased with an increase in the flushing pressure.

Sahin (2003) developed mathematical model for the wear rate of

composite using a linear factorial design approach. He reported that this

model was used to study the direct and interaction effects of variables on the

response. He concluded that the wear rate of the composite increased with

both increasing applied load and sliding distance.

Simul Banerjee et al (2008) used a face centered central composite

design approach to develop a mathematical model for material removal rate

and surface roughness. A total eighteen experiments were conducted based on

the design matrix and the model was found to be adequate. According to the

model developed, the surface roughness increased when the pulse on time was

increased.

Hong and Chung (1995) conducted experiments using Taguchi

method. They reported the direct effects of controlled factors on the response.

21

They also stated that the tensile strength of the composite decreased with

higher extrusion ratio (25:1) due to the reduced aspect ratio of SiC whiskers.

Huda et al (1994) developed a model for estimating the hardness of

aluminum alloy reinforced with alumina particles. A design consisting of

twelve experiments was used to develop the model. They also applied

response surface methodology to study the effect of manufacturing

parameters on the hardness of the composite. They found that higher hardness

was obtained with a combination of high temperature and high volume

fraction of reinforcement.

2.7� ARTIFICIAL NEURAL NETWORKS

In recent years, Neural Networks have become a very powerful tool

in modeling inter6relationships between input and output parameters of many

complicated systems. Various studies on the prediction of mechanical

properties of composites using ANN have been carried out.

Mustafa Taskin et al (2007) applied Artificial Neural Networks

(ANN) for modeling of wear resistance of aluminum composites. They used

back propagating Multi Layer Perceptron (MLP) Artificial Neural Network

for training the experimental results. They found that Artificial Neural

Network could be successfully used for modeling of both adhesive wear

behavior as well as the weight loss of composites.

XuLiujie et al (2007) predicted the value of hardness and abrasive

wear resistance using neural network. A total of 25 data were selected for

training of the network. They reported that a well trained two hidden layer

network had smaller training errors and much better generalization

performance in comparison to one hidden layer network.

22

Singh et al (2006) applied back propagation neural network to

predict the flank wear of high speed drills. From the data sets obtained from a

total of 49 experiments, 34 were selected at random for training the network

and remaining 15 were used for testing. The normalized data sets were used

for training the network. They observed that, the neural network is able to

effectively learn the pattern of wear, and thus applicable to predict drill wear

during composite machining.

Necat Altinkok and Rasit Koker (2005) predicted tensile strength,

density and porosity of particle reinforced aluminium composites using neural

network. A back propagation algorithm with one hidden layer was employed

for training the network. They reported that the training process was

completed with 520 iterations. The neural network prediction was in good

agreement with experimental results.

Yezdanmehr et al (2009) developed an Artificial Neural Network

model to predict the yield and tensile strength of composites. They found the

predicted results had a very good agreement with the experimental values.

ANNs ability to minimize complex input6output relationships, has made it a

very useful tool in the field of the mechanical property prediction.

Mehmet Sirac Ozerdem and Sedat Kolukisa (2009) applied neural

network to predict tensile strength and elongation of composites. They used a

multilayer perceptron (MLP) architecture with back propagation algorithm in

the network. The model was trained using the prepared training set. The test

data were used to check system accuracy after training. They found that, the

neural network successfully predicted the tensile strength and elongation of

the composite specimens.

Paulo Davim et al (2008) predicted the surface roughness of Al

composite using Artificial Neural Network. They conducted experiments

23

using a L27orthogonal array with three levels for each factor. The network

was trained using Error Back6Propagation Training Algorithm (EBPTA).

They reported that, the performance of ANN prediction model though

adequate, can be improved by defining more number of levels for input

process parameters.

Mohammed Hayajneh et al (2009) predicted the wear loss of

aluminium composites using Artificial Neural Network. They coded the

experimental results prior to training in a feed forward back propagation

artificial neural network. A satisfactory agreement between the experimental

and ANN was obtained when the model was tested.

Hafizpour et al (2009) developed neural network model to predict

the densification of composite powders. They used back propagation (BP)

learning algorithm with two hidden layers to train the experimental data. The

neural network model gave better values when tested.

Abdullah Kurt (2009) developed a neural network model to predict

the cutting tool stresses. A back propagation algorithm was developed for

training the network. The best fitting set was obtained with ten neurons in the

hidden layer in the model.

Rapetto et al (2009) developed neural network model to determine

the relationship between the roughness parameter. They found that, the neural

network was able to prove the correlation between the roughness parameters.

Raghuprased et al (2009) predicted compressive strength using

neural network. They found that the proposed neural network model gave

good prediction of the values.

24

Rashed and Mahmoud (2009) predicted the wear behavior of

aluminium composites using neural network. They used multilayer perceptron

(MLP) network. They found that considerable savings in terms of cost and

time could be obtained from using ANN models. ANN approach is a

successful analytical tool that can be used to predict the wear behavior of new

materials including composites.

Marai Alshihri et al (2008) developed neural network model for

predicting compressive strength of structural light weight concrete. They

reported that the neural network models are efficient tools for estimating the

compressive strength give significant reduction in cost and time.

Adel Mahamood Hassan et al (2009) predicted density, porosity

and hardness of aluminium composites using neural networks. They reported

that by using ANN outputs, satisfactory results can be obtained rather than

actual experimentation and measurement. This leads to reduce testing time

and cost.

Necat Altinkok and Rasit Koker (2004) predicted the bending

strength and hardening behaviour of aluminium matrix composites. They used

one hidden layer network and found that the neural network was successful in

the prediction of bending strength, hardening behaviour as well as porosity for

any given SiC particles size range in the produced AMCs.

2.8� WEAR TESTING

Wear is a material removal from a component by mechanical attack

of solids. Adhesive wear occurs when two solid surfaces slide over one

another under pressure. Abrasive wear occurs when material is removed from

a surface during contact with hard particles. The particles either may be

present at the surface of a second material or may exist as loose particles

25

between two surfaces. Many factors have to be considered while attempting to

improve the wear resistance of materials. Designing components so that loads

are small and surfaces are smooth along with continual lubrication can help

prevent adhesive wear. Materials with a high hardness, good toughness and

high hot strength are most resistant to abrasive wear (Donald Askeland and

Pradeep Phule 2002). Various researchers have carried out experiments for

testing the wear property of aluminium matrix composites.

Gurcan and Baker (1995a) investigated the wear resistance of four

different Al 6061 metal matrix composites reinforced with SiC particles. The

results from their investigation revealed that the composites containing only

Saffil had inferior wear resistance to those containing the same volume

fraction of SiC particles. The greatest wear resistance was observed in the

composite containing 20 wt.% of SiC particles.

Ramesh et al (2005b) predicted the wear coefficient of A1 6061

composites using Archard’s and Yang’s theoretical models. Their results

showed that, the predicted values of the wear coefficient from the Archard’s

model agreed more closely with the experimental values in comparison with

Yang’s model. They also reported that the composites exhibited higher

hardness and lower wear coefficients. It was observed that increased loads

and sliding distances resulted in higher volumetric wear loss but lower wear

coefficient for both unreinforced alloy and its composites.

Seyed Reihani (2006) studied the wear characteristics of Al

6061composites reinforced with 30 vol.% SiC particles with an average size

of 22µm. The weight loss of the composite was almost one6third of the

unreinforced alloy. Better mechanical properties were obtained by decreasing

the particle size in the composites.

26

Sharma (2001a) investigated the sliding wear behavior of Al 6061

composites reinforced with garnet particles. A pin6on6disc wear testing

machine was used to carry out tribiological tests on both composites and

matrix alloys over a load range (10 – 50 N) and sliding velocities (1.25 – 3.05

m/s) for various sliding distances (0.3 – 3 Km). The results revealed that, the

wear resistance of Al 6061 composites are superior to that of unreinforced

alloy. The wear resistance of the composite increases with increasing wt.% of

garnet particles. The average coefficient of friction of the composite was

lesser than that of matrix alloy.

Jen Fin Lin et al (1996) reported that the tribiological performance

of Al 6061 composites reinforced with graphite particles. The experimental

results revealed the occurrence of surface seizure, which was dependent upon

the value of the oil temperature rise.

Sanchez Santana et al (2006) reported the wear properties of

surface treated Al 6061 composites. Surface treatment was done by LASER

shock processing. The results showed that LASER shock processing reduced

the wear rate of the alloy due to compressive residual stress field induced.

Wear mechanisms observed were adhesion and abrasion. When the wear

depth increased, the wear mechanism was attributed to delamination.

Kiourtsidis et al (2002) conducted wear testing on heat treated

aluminium composites reinforced with silicon carbide particles. They

observed that, the wear resistant of the composite were improved with

increasing SiCp content in both the peak aged and over aged condition.

Serdar Osman Yilmaz and SonerBuytoz (2007) investigated the

sliding wear behavior of cast Al 6061 composites reinforced with Al2O3

particles. They reported that, the increasing Al2O3 volume percentage

decreased both thermal conductivity and friction coefficient. The wear rate of

27

the composites was not affected by the volume percentage of porosity.

However, the hardness of the composites decreases with the increase in

porosity volume percentage.

Ramesh and Mirsafiulla (2007a) produced cast Al 6061 with SiC

particles. They reported that, extruded composites possessed higher

microhardness and lower wear rate under different loads and sliding velocities

in comparison to cast composites.

Yilmaz and Buytoz (2001) conducted wear test on alumina

reinforced aluminium matrix composites using pin6on6disc apparatus. Applied

load and sliding distance were varied during testing. They found that, the

wear rate of the composites decreased with decreasing applied load and

sliding distance.

Natarajan et al (2006) studied the wear behaviour of aluminium

metal matrix composites sliding against semi6metallic brake shoe material.

They observed that wear of the lining material was more when sliding against

metal matrix composite disc because of the ploughing of the lining material

by the silicon carbide particles.

Alpas and Zhang (1992a) investigated dry sliding wear of the SiC

particulate reinforced aluminium composites. They used block6on6ring

apparatus for conducting wear tests. They found that SiC reinforcement

proved to be very effective in suppressing the severe wear.

Yang (1999) developed a new moving pin technique for pin6on6disc

wear testing. He modified CNC lathe for conducting the wear testing.

Tungsten carbide inserts were used as pins and three types of disc material

were employed. He found that, the results obtained from the new moving pin

testing technique were more consistent.

28

Jang et al (2004) investigated the friction and wear performance of

different metallic fibers. Tests were conducted using a small scale friction

tester at two different temperatures. They observed that the greatest amount of

wear occurred in the friction material containing steel fibers, followed by

the copper fiber friction material and least wear with the Al fiber friction

material.

Straffelini et al (2004) investigated the dry sliding wear behavior of

aluminium matrix composites against friction material. The tests were carried

out using block6on6ring wear test apparatus. They observed that for applied

loads lower than 200N, wear was due to abrasion and adhesion. For applied

loads higher than 200N, the interface temperature becomes high and this

caused a degradation of the organic binder in the friction material.

Cambronero et al (2003a) investigated the wear properties of

aluminium alloy reinforced with boron carbide, titanium boride and silicon

nitride ceramics. They conducted wear test using pin6on6disc apparatus. They

found that the plastic deformation of the composite decreased with the

addition of ceramic particles.

Korkut (2003a) conducted dry sliding wear tests on aluminium

composites using pin6on6ring wear test rig. 8mm diameter and 12mm length

pins were used for wear testing with loads varying from 40650N. The sliding

speed was varied from 0.1562.0 m/s. It was observed that the coefficient of

friction increased with increasing load.

Das (2004a) studied the sliding wear and abrasive wear behaviour

of Al6SiC composites using pin6on6disc wear testing machine. The sliding

distance was varied from 500m to 5000m. Results showed that the wear rate

increased almost linearly with the sliding distance. Composites exhibited

improved wear resistance compared to unreinforced alloy.

29

Wilson and Alpas (1997) investigated the wear characteristics of

aluminium composites under dry sliding conditions using a block on ring

apparatus. The tests were conducted with in a load range of 206400N and a

sliding velocity range of 0.2 to 5.0m/s. They found that the mild wear regime

for the composite was extended to a higher range of sliding speeds and loads.

2.9� PROPERTY EVALUATION

Various mechanical properties such as hardness, tensile strength,

impact strength and toughness of different types of composites have been

investigated by many researchers. Hardness is the resistance that a material

offers to scratch or indentation (or plastic deformation). In the indentation

hardness test, an indenter of specified geometry is allowed to penetrate the

test specimen using a standard load under static conditions. The resistance to

penetration or indentation gives the hardness. Indentation hardness test is very

common for metals as they can deform plastically when indented (Bhargava

2004, Yulong Li et al 2004, Srivatsan et al 2003). The investigations on

hardness in past work are discussed below.

Ramesh and Mirsafiulla (2007b) studied the micro hardness of A1

6061 composites reinforced with SiC, Al2O3 particles. They found that the

extruded composites possess higher micro hardness and lower wear rate for

all loads and sliding velocities investigated.

Tedguo and Tsao (2000a) reported a decrease in the hardness of the

composite with increasing graphite percent and attributed this to the weaker

graphite phase.

Ceschini et al (2006) investigated the tensile properties and low

cycle fatigue behavior of 6061 aluminium alloy composites reinforced with

20 vol. % of Al2O3 particles. The tensile tests showed an increase in the

30

elastic modulus as well as tensile strength and a decrease of the elongation to

failure in the MMCs, with respect to the unreinforced alloys. The tensile

ductility was strongly affected by the material inhomogeneity, particle size

and distribution. Low cycle fatigue tests showed no evidence of isotropic

hardening or softening of composites.

Huda et al (1994a) developed hardness model for MMCs reinforced

with Al2O3. They found that better hardness is obtained at high volume

fraction of reinforcement particles.

Mahagundappa Benal and Shivanand (2007a) have reported that the

hardness of Al 6061 composites reinforced with SiC particles increased with

increasing ageing duration as well as the amount of reinforcing particles.

Bekheet et al (2002a) reported that the hardness of Al 2024

composite reinforced with SiC particulates. The time required to attain the

peak hardness is very much influenced by the presence and amount of SiCp.

2.10 HEAT TREATMENT OF COMPOSITES

The properties of the aluminium matrix composites can be

enhanced by adopting suitable heat treatment. Several investigations have

been carried out on heat treatment response of aluminium composites.

Mahagundappa Benal and Shivanand (2007b) studied the influence

of heat treatment on the wear resistance of the hybrid composites. They

observed that, among the heat treated hybrid composite specimens aged at 5h

exhibited better wear resistance. Specimen aged at 7h exhibited lowest wear

resistance in all the cases. When the ageing duration increased, the hardness

also increased and there was an increase in wear resistance.

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Prabhuswamy et al (2007) reported that the addition of SiC

particles as reinforcement in Al 6061 alloy improves its hardness, tensile

strength and wear resistance. They also stated that heat treatment had a

significant influence on microhardness and adhesive wear of both

unreinforced alloy and its composites. Microhardness of composites increased

significantly with increased content of SiC particles.

Xia et al (1997) studied the mechanical properties of 6061

aluminium matrix composites reinforced with Al2O3 particles. They found

that heat treated composites showed higher stiffness and strength. During

biaxial cyclic tests, a significantly loss in elastic modulus was observed.

Varma et al (1999) studied the corrosive wear behavior of Al 6061

composites prepared by stir casting route. The composite samples were

subjected to impact scratching during a corrosive wear process. They

observed that due to larger grain size at longer solutionizing time, the area of

the scratch increased. They also stated that the dominating corrosion

mechanism in the composites was pitting corrosion.

Bekheet et al (2002b) investigated the effect of ageing on the

hardness of aluminium composites reinforced with SiC particulates. They

found that the peak hardness of the composites was slightly higher than that

of the unreinforced alloy.

Doel et al (1993a) investigated the tensile properties of aluminium

composites reinforced with silicon carbide particles. Heat treatment on the

specimens was carried out for under aged, peak aged and over aged

conditions. They found that in the peak aged condition the composites

reinforced with fine particulates have a slightly lower yield stress and tensile

strength.

32

Mahadevan et al (2006a) investigated the influence of heat

treatment parameters on the hardness of Al 60616SiCp composites. They

observed that, solutionizing time and ageing temperature has a slightly higher

influence on hardness than ageing time.

Dujun et al (2007a) studied the effect of heat treatment on wear

properties of hybrid composites. They found that the strength and hardness of

metal matrix composite increased due to the refinement of microstructure.

They also stated that heat treatment was beneficial to the improvement of

wear property of the composites. Heat treatment improved the resistance to

delamination in composites during wear testing.

Park et al (2001) reported the effect of heat treatment on aluminium

metal matrix composites. They found that hardness and elastic modulus of the

composite increased with increasing ageing time. The tensile strength of the

composite was slightly lower than that of the unreinforced alloy at an ageing

duration of 8h.

Martin et al (1999) studied the effect of temperature on the wear

behavior of particulate reinforced aluminium based composites. The results

revealed that the wear mechanism seems to be associated with the plastic

deformation of the matrix phase at higher temperatures.

Farooq Bashir et al (2008) investigated the hardness characteristics

of heat treated copper based composites. They stated that solution treatment

of the specimens followed by quenching in water increased the hardness of

the composite due to the residual stresses generated by fast cooling.

Stone and Tsakiropoulos (1998) have reported the tensile properties

of heat treated Al64wt.% Cu metal matrix composites. The results revealed

33

that, the strength of the composites increased with decreasing size of the SiC

particulates.

Stone and Tsakiropoulos (1994) reported the heat treatment of

aluminium composites reinforced with SiC particles. The heat treatment cycle

comprised of solution treatment for 1h at 803K followed by a cold water

quench and subsequent ageing at 463K for varying times between 30min to

8h followed by a cold water quench. The results revealed that when the

reinforcement becomes more uniformly distributed throughout the matrix, the

cracking tendency at the edges became less severe during heat treatment. The

wear rate decreased with increase in ageing duration.

Slipenyuk et al (2004) investigated the effect of heat treatment on

the mechanical properties of Al6SiCp composites. The specimens were

solution treated for 1h at 803K followed by water quenching to room

temperature. It was observed ultimate strength was increased as a result of

heat treatment.

Rong Chen et al (2000a) investigated the fretting wear behaviour of

A356 aluminium alloy reinforced by 15vol.%,10µm SiC particles under T6

conditions. They found that the composites with heat treatment showed a low

coefficient of friction during the initial fretting stages. The hardness and yield

strength of composites showed an increase after heat treatment.

Sug Won Kim et al (2003a) investigated the effect of heat treatment

on the wear resistance of Al/SiCp composites. The specimens were treated in

a solution for 10h at 773K and then aged at 430K for various periods of time.

They found that the composites exhibited higher hardness. Aluminium

composites reinforced with 10µm SiCp was found to have the lowest wear

loss.

34

Sawla and Das (2004) investigated the two body abrasive wear of

aluminium composites with heat treatment. The hardness of the composites

was improved by as much as 33% due to combined effect of heat treatment

and reinforcement of 15wt. % SiC particles. The wear constant decreased

with load. Heat treated alloy and composite showed increased hardness.

Olivier Beffort et al (2007a) studied the mechanical properties of

heat treated composites. The composite specimens were subjected to solution

treatment and artificial ageing. They found that, the dominant failure

mechanism in silicon carbide reinforced composite was SiC intra6particulate

fracture.

2.11 MACHINING CHARACTERISTICS OF COMPOSITES

Machinability of composites depends upon a number of factors like

particle size, shape and type of reinforcement, distribution of reinforcement

material as well as machining parameters. Various investigators have carried

out studies on the machining characteristics of composites using different

machines like CNC, EDM and ECM (Mujahid and Friska 2005, Ibrahim

Ciftci et al 2004). A brief summery on machinability studies of aluminium

composites in the past are discussed below.

Patel et al (2009) optimized the process parameters with EDM for

alumina reinforced aluminium composites. Experiments were conducted with

discharge current, pulse6on6time, duty cycle and gap voltage as typical

process parameters. The discharge current was found to be the most

significant factor influencing metal removal rate. An increase in duty cycle

increases removal rate while surface roughness decreases.

Ramulu et al (2002) studied the drilling characteristics of alumina

reinforced Al 6061 metal matrix composites. They reported that when drilling

35

of composites caused extremely rapid flank wear in the drilling tools. Among

the drilling tools studied, Polycrystalline diamond (PCD) drills possessed the

highest resistance to abrasion. High speed steel (HSS) drills were unsuitable

for drilling MMCs because of very high tool wear, poor drilled – hole quality

and higher drilling forces induced. PCD drills induced lowest drilling forces.

Yusuf Keskin et al (2006) investigated the effects of machining

parameters on surface roughness in EDM. Surface roughness showed an

increasing trend with increase in the discharge duration. The interaction

between spark time and power to surface roughness was found to be

statistically significant.

Narcls Pellicer et al (2009) studied the influence of process

parameters on surface quality in EDM of AISIH13 steel. They found that,

metal removal rate and surface roughness increased with discharge current.

Pulse6off variation affects metal removal rate, but its behaviour is not linear

due to the interactions with other parameters like voltage, current.

El6Taweel (2009) investigated the relationship between process

parameters in electro discharge machining of CK45 steel with Al6Cu6Si6TiC

composite electrode tool. They used titanium carbide percent, peak current,

dielectric flushing pressure and pulse6on time as input process parameters.

The optimal process parameter settings obtained were TiC percent of 18%,

peak current 6A, flushing pressure 1.2 MPa, and pulse6on6time 182 µs, for

achieving maximum metal removal rate and minimum tool wear rate.

Ponappa et al (2009) investigated the effect of process parameters

such as pulse on time, pulse6off time, voltage gap and servo speed on surface

finish and reduced taper during EDM of magnesium nano alumina

composites. They found that the pulse6on6time and the servo speed are the

most influencing factors on surface finish and reduced taper.

36

Ko6Ta Chiang (2008) studied the effects of machining parameters

on the performance characteristics in the EDM of Al2O3+TiC mixed ceramic.

They found that two main factors affect the metal removal rate are the

discharge current and duty factor. The discharge current and the pulse6on6time

also have statistical significance on both electrode wear ratio and surface

roughness.

Mohan Kumar Pradhan and Chandan Kumar Biswas (2008)

developed a model for metal removal rate during EDM. The significant

coefficients were obtained by performing Analysis of Variance (ANOVA) at

5% level of significance. They found that, the discharge current, pulse

duration and pulse 6off6 time had significant effect on the Metal Removal

Rate (MRR).

Summarizing the literature, it can be stated that a good volume

research have been carried out on the mechanical and wear properties and

machining characteristics of aluminium metal matrix composites by taking

different reinforcement materials. In the case of hybrid Al 6061 alloy

composites, limited amount literature only is available encompassing various

aspects such as microstructure, mechanical properties and wear behaviour in

as cast and heat treated conditions as well as modelling with Design of

experiments an ANN. Based on the literature review in the present

investigation, an attempt was made to study the wear, heat treatment and

mechanical properties, microstructure and machining characteristics of Al

6061 hybrid composites reinforced with SiC and graphite particles.

Mathematical Modelling was also carried out.