Journal of Minerals & Materials Characterization & Engineering, Vol. 10, No.1, pp.59-91, 2011
jmmce.org Printed in the USA. All rights reserved
59
Mechanical and Tribological Behavior of Particulate Reinforced Aluminum
Metal Matrix Composites – a review
G. B. Veeresh Kumar1*, C. S. P. Rao2, N. Selvaraj2
1Research Scholar, National Institute of Technology, Warangal, (A.P), India & Department
of Mechanical Engineering, S B M Jain College of Engineering, Jakkasandra (P),
Kanakapura (T), Ramanagara (D)-562 112, Karnataka, India.
2Department of Mechanical Engineering, National Institute of Technology, Warangal, (A.P),
India.
*Corresponding author: veeru232@yahoo.com
ABSTRACT
Aluminum Metal Matrix Composites (MMCs) sought over other conventional materials in the
field of aerospace, automotive and marine applications owing to their excellent improved
properties. These materials are of much interest to the researchers from few decades. These
composites initially replaced Cast Iron and Bronze alloys but owing to their poor wear and
seizure resistance, they were subjected to many experiments and the wear behavior of these
composites were explored to a maximum extent and were reported by number of research
scholars for the past 25 years. In this paper an attempt has been made to consolidate some of the
aspects of mechanical and wear behavior of Al-MMCs and the prediction of the Mechanical and
Tribological properties of Aluminum MMCs.
Key Words: Al-MMCs, Density, Hardness, Mechanical Properties, Wear, Prediction.
1. INTRODUCTION
Metal Matrix Composites are being increasingly used in aerospace and automobile industries
owing to their enhanced properties such as elastic modulus, hardness, tensile strength at room
and elevated temperatures, wear resistance combined with significant weight savings over
unreinforced alloys [1-4]. The commonly used metallic matrices include Al, Mg, Ti, Cu and their
alloys. These alloys are preferred matrix materials for the production of MMCs. The
reinforcements being used are fibers, whiskers and particulates [5]. The advantages of
60 G. B. Veeresh Kumar, C. S. P. Rao, N. Selvaraj Vol.10, No.1
particulate-reinforced composites over others are their formability with cost advantage [6].
Further, they are inherent with heat and wear resistant properties [7, 8]. For MMCs SiC, Al2O3
and Gr are widely used particulate reinforcements. The ceramic particulate reinforced
composites exhibit improved abrasion resistance [9]. They find applications as cylinder blocks,
pistons, piston insert rings, brake disks and calipers [10]. The strength of these composites is
proportional to the percentage volume and fineness of the reinforced particles [11]. These
ceramic particulate reinforced Al-alloy composites led to a new generation tailorable engineering
materials with improved specific properties [12, 13]. The structure and the properties of these
composites are controlled by the type and size of the reinforcement and also the nature of
bonding [14-16]. From the contributions of several researchers, some of the techniques for the
development of these composites are stir casting [17], powder metallurgy [18], spray atomization
and co-deposition [19], plasma spraying [20] and squeeze-casting [21]. The above processes are
most important of which, liquid metallurgy technique has been explored much in these days.
Therefore the present paper summarizes the studies conducted by several investigators under
sections mechanical and tribological behavior.
2. PROPERTIES OF COMPOSITE MATERIALS
From the nature and morphology of the composites, their behavior and properties can be
predicted and the factors such as intrinsic properties, structural arrangement and the interaction
between the constituents are of much importance. The intrinsic properties of constituents
determine the general order of properties that the composite will display. The interaction of
constituents results in a new set of properties. The shape and size of the individual constituents,
their structural arrangement and distribution and the relative amount of each contribute to the
overall performance of the composite. The factors that determine properties of composites are
volume fraction, microstructure, homogeneity and isotropy of the system and these are strongly
influenced by proportions and properties of the matrix and the reinforcement. The properties
such as the Young’s modulus, shear modulus, Poisson’s ratio, coefficient of friction and
coefficient of thermal expansion are predicted in terms of the properties and concentration and
the most commonly used approach is based on the assumption that each phase component is
subjected to either iso-stress or iso-strain condition.
2.1 Physical Properties
Density is the physical property that reflects the characteristics of composites. In a composite,
the proportions of the matrix and reinforcement are expressed either as the weight fraction (w),
which is relevant to fabrication, or the volume fraction (v), which is commonly used in property
calculations. By relating weight and volume fractions via density (ρ), the following expression is
obtained (m stands for matrix and for reinforcement material):
 
 
Vol.10, No.1 Mechanical and Tribological Behavior 61
The above expression can be generalized and its general form is known as law of mixture and is
as follows;
 
 
Experimentally, the density of a composite is obtained by displacement techniques [22] using a
physical balance with density measuring kit as per ASTM: D 792-66 test method. Further, the
density can also be calculated from porosity and apparent density values (sample mass and
dimensions) [11].
The results of the several investigations [23-32] regarding the density of the Al2O3/ SiC particle
reinforced Al6061 and other aluminum alloys can be summarized as follows: the reinforcements
Al2O3 and SiC enhance the density of the base alloy when they are added to the base alloy to
form the composite. Moreover, the theoretical density values match with the measured density
values of these composites. Further, Miyajima et.al. [5] reported that the density of Al2024-SiC
particle composites is greater than that of Al2024-SiC whisker reinforced composites for the
same amount of volume fraction. From the above the increase in density can be reasoned to the
fact that the ceramic particles possess higher density.
Further, the increased volume fraction of these particles contribute in increasing the density of
the composites, also they have stated that the theoretical and measured density values of these
composites match to each other. Additionally, the above discussions can be reasoned to the fact
that the ceramic particles possess higher density.
To support the above findings, few composites were developed to study the density. The Al6061-
SiC and Al7075-Al2O3 particulate reinforced composites were developed by liquid metallurgy
technique (stir casting route). The cast alloy and composite specimens were subjected to density
72 G. B. Veeresh Kumar, C. S. P. Rao, N. Selvaraj Vol.10, No.1
compared to the composite with increasing load. In case of abrasive wear, the overall effect of
abrasive size on wear rate becomes significantly less as compared to the contribution of load
when the matrix of the composite is already subjected to a certain amount of strain hardening
effect before being subjected to wear. [89].
Decreasing wear rate with sliding distance is a definite indication of more effectiveness of work
hardening of the subsurface regions due to increasing wear induced plastic deformation.
Subsurface hardening was evidenced by increased hardness in the subsurface region as compared
to the unaffected bulk [143].
With the repeated dry sliding test, a working hard layer occurs on the wear surface and this
promotes wear resistance of the composites. At the same time, the wear surface temperature
increases subsequently. As a result, re-crystallization takes place in the worn surface during the
dry sliding, which results in the decrease of the wear surface hardness and this considerably
counteracts the promoting effect of the wear resistance by work hardening. Moreover, the
oxidization layer formed on wear surface of the sample is beneficial in enhancing the wear
resistance [68].
3.4 Effect of Mechanical Mixed Layer (MML)
During sliding at higher wear-rates, high temperature is developed at the sliding surface due to
which the specimen softens and becomes plastic. It reacts with the available oxygen and forms
their respective oxides. The hard brittle oxide formed on the surface of the specimen becomes
thicker and continuous, covering the entire surface. The Aluminum oxide film acts partly as an
insulator for thermal conduction. This MML was responsible for the decrease in the wear-rate
and friction of the MMCs [144]. The transfer of steel inclusions from counter-face surfaces to the
composite wear surfaces is another mechanism which contributes to the increase in wear
resistance of the composites [145]. This indicates that the inclusions act as additional
reinforcements at the wear surface of composite and are load supporting [30] and the specific
wear rate decreased with increasing MML thickness [144]. The MML forms on the worn surface
of matrix and composite and it serves as a protective layer [80, 97] and a solid lubricant. In
composites having low volume fraction, the MML is stable under low loads and unstable under
higher loads. In the composite having higher volume fraction of reinforcement, the MML is
stable under high loads [146]. The MMLs were formed in the worn surfaces at a variety of
sliding loads. The mixed layers had micro-structural features comprising of a mixture of
ultrafine-grained structures in which the constituents varied depending on the sliding loads [147].
Venkataraman et.al found that the thickness of the transfer layer increases as the normal load
increases [148]. Due to the presence of MML, the wear rates of both the pin and disc are lower at
higher speeds. With increasing speed the amount of layer formation increases due to the higher
temperatures generated [146]. The extent of cover provided by this transfer layer is determined
Vol.10, No.1 Mechanical and Tribological Behavior 73
by the load, sliding speed and environmental conditions and it increases with increasing load
because of the increased frictional heating and hence, better compaction [149, 150].
Once the MML is formed, it provides a surface protection before critical conditions are reached
and then loose debris gets detached from the mixed layer, in agreement with the wear behavior
observations that the wear rate was lower at an intermediate load range with presence of the
MML. The MML was not uniform in thickness across the entire wear track and it actually
exhibited a wavy shape in the cross section of the worn surface. The wear rate, thus, would be
influenced by the formation and detachment of the MML in the load range used [147]. Formation
of the tribo-layer delays the mild to severe wear transition in Al-MMCs. Once the tribo-layer is
removed from the contact surface, the bulk material comes in direct contact with the counter-face
and it is difficult to form a new tribo-layer on the hot and softened matrix [26]. On further
sliding, the MML gets separated out from the pin surface due to delamination leaving behind the
fresh pin surface, which results in the drop of frictional force [122]. The results indicate that
different type of reinforcement can generate MMLs. The observations indicate that the MML
formed with material comes from three sources; the counter-face (contributing with Fe, about
20% Fe), the matrix and the particles [148].
Some characteristics of the MML, which can be used to distinguish it from the normal composite
material, are: (a) a darker color than the normal composite material when observed under optical
microscope. (b) The presence of chemical elements coming from the counter-face. (c) A higher
micro-hardness value in the MML and abrupt change to too much lower values outside the MML
[23]. The hardness of the MML was found to be much harder than that of the matrix hardness in
the composite [26]. Actually, the hardness of the MML is independent of the composite and the
value is comparable to the hardness of the steel counter-face. It is noted that the MML is not
formed in the non-reinforced material, mainly because no trace of iron was found on the worn
surface [148]. Micro-hardness studies along the vertically sectioned surface starting from the
worn surface show that the magnitude of the hardness of the specimen decreases with the
distance from the worn surface, which indicates that the sub-surface nearer to the worn surface
was hardened due to strain hardening effect than the region away from the worn surface.
Li and Tandon [151] were among the researchers who have reported the formation of iron-rich
oxidized tribo-layers on the contact surfaces. Detailed investigations of the tribo-layers on the
Al-Si alloy worn surfaces were also presented by Biswas [152]. The transition between the mild
and the severe wear regimes were attributed to the removal of these layers [153]. Almost all the
investigations performed to date on the formation of tribo-layers and material transfer
phenomena accompanying sliding wear of Al–Si alloys were conducted in an ambient
atmosphere as a function of applied normal load and sliding speed. The SiC undergoes tribo-
chemical interaction during sliding and forms SiO2, which acts like a lubricant, especially at
higher speeds [146].
74 G. B. Veeresh Kumar, C. S. P. Rao, N. Selvaraj Vol.10, No.1
The protection cover provided by MML is observed to increase with increasing volume fraction
of TiC. This may be attributed to the higher hardness of the substrate having relatively higher
amount of TiC, which is able to hold a thicker transfer layer of compacted oxide as compared to
the substrate of lower hardness [149, 150]. When the reinforcement in the matrix has wide size
distribution, wear rate and friction coefficients are found to be higher compared to composite
containing mono-size reinforcement [80].
3.5 Effect of Heat Treatment
The alloy and composites exhibit minimum wear rate after heat treatment due to improved
hardness [57]. In case of cast alloy, the value of wear constant was higher than that of the heat-
treated alloy and composite. During the wear process, the cracks are mainly nucleated at the
matrix and reinforcement interfaces. Heat-treated alloy and composite showed better strength
and hardness that resulted in fewer propensities for crack nucleation and showed enhancement in
wear resistance [46, 55]. In case of heat-treated alloy, the effective stress applied on the
composite surface during wear process is less due to higher strength and ductility of the Al
matrix. This resulted in less cracking tendency of the composite surface as compared to the cast
alloy [55]. The heat treatment did not radically change the morphology but hardening of the
matrix by precipitation hardening took place, which led to higher hardness and strength [46].
The highest wear resistance was obtained for T6 thermal treatment condition. The studies have
determined that the maximum hardening of the matrix was obtained when the composite material
was solubilied at a temperature of 5600C for 3 hours, quenched in ice water at 00C and ageing
done at a temperature of 1750C for 7 hours. It was found that the heat treatment T6 7 hours was
the one that provided the matrix greater hardness and therefore it was the one, which gave the
MMC the higher wear resistance [154]. The higher hardness and yield strength of the composite
by T6 heat treatment would have the advantage of preventing the formation of aluminum debris
and decreasing its transfer to the surface of steel [155]. When aged at the lowest temperatures
(between 50-1500C), the hardness and abrasive wear resistance of under-aged composites were
found to be relatively low. Raising the ageing temperature to 2000C increased the hardness and
abrasion resistance of the composites to the peak-aged condition. At 2500C the composites were
over-aged and this resulted in a reduction in hardness and wear resistance due to the coarsening
of the inter-metallic precipitates [56]. Decreasing the discontinuously reinforced aluminum
(DRA) matrix strength through under-aging and over-aging heat treatments decreases the DRA
wear rate under abrasion conditions by enhancing the formation of a protective solid film [123].
Vol.10, No.1 Mechanical and Tribological Behavior 75
4. MODELS FOR PREDICTION OF WEAR PROPERTIES
In automobile, aerospace, mining and mineral sectors, there exist situations where two mating
parts are in sliding contact with each other. Due to the relative motion of these sliding parts,
there is an inevitable loss of material. In certain situations, if the extent of material wear is
beyond a critical limit, there are possibilities of catastrophic failure of the components leading to
huge economic losses. Extensive research has been carried out on the study of tribological
behavior of Al-MMCs [156]. The most important reason for the damage and consequent failure
of machine parts is wear. A lot of experiments must be conducted in order to study the
tribological behavior. This results in wastage of both man power and money [157]. Hence the
prediction of wear rate is of utmost importance in the present industrial scenario to assess the life
of sliding components in advance to avoid massive financial losses that are incurred due to wear.
4.1 Prediction of Wear Properties by Theoretical Models
Yang has proposed a new formulation of the wear coefficient that was developed and tested
experimentally, which was based on exponential transient wear volume equation and Archard’s
equation. The wear equation was found to be a better predictor of steady state wear coefficients
[25]. Sharma developed a theoretical model for estimating the sliding wear rate considering the
effect of frictional heat on the wear properties at contact surfaces, the effect of reinforcement,
mechanical load, sliding distance, sliding velocities on wear rates, coefficient of friction and
transition wear. This theoretical model was proposed for estimating the sliding wear rate of both
alloy and composites [33]. Kumar et.al., have successfully developed a mathematical model to
predict the wear rate of Al7075-SiC composites by incorporating the effects of volume fraction,
reinforcement size, applied load, sliding speed and hardness of the counter-face material. The
developed model can be effectively used to predict the wear rate of Al7075-SiC composites at
95% confidence level [90]. Das et.al., correlated wear behavior of Al-alloy and composites in
terms of mechanical properties, micro-structural characteristics, applied load and abrasive size
through an empirical equation; which, demonstrated the effect of size and volume fraction of
reinforcing phase and the size of the abrasive particles on the wear rate of Al alloy and
composites. It suggests that the wear rate of the composite will increase with increase in size of
reinforcing phase and the composite may suffer higher wear rate than the alloy if the abrasive
size is higher than that of reinforcing phase [57].
Sahin developed wear resistance model for the MMCs based on the Taguchi method. The
orthogonal array, signal-to-noise (S/N) ratio and analysis of variance were employed to find the
optimal testing parameters. The results showed that the abrasive grain size was the most
powerful factor on the abrasive wear, followed by weight fraction of reinforcement. Optimal
wear testing conditions were verified with an experiment. It was observed that there was a good
agreement between the predicted and actual wear resistance for a 95% confidence level [158].
Mondal et.al., predicted the wear behavior through statistical analysis of the measured wear rate
76 G. B. Veeresh Kumar, C. S. P. Rao, N. Selvaraj Vol.10, No.1
at different operating conditions. The wear rate is expressed in terms of the abrasive size and
applied load by a linear regression equation. Factorial design of experiment can be successfully
employed to describe the high stress abrasive wear behavior of Al-alloys and composites and to
develop empirical linear regression equations for predicting wear rate within a selected
experimental domain [89]. Further, few researchers also made an attempt to evaluate the wear
coefficients by using Archard’s and Yang’s theoretical models and concluded that the predicted
values of the wear coefficient are in close agreement with the experimental ones [159].
4.2 Prediction of Wear Properties of Composites by Soft Techniques
Recent progress in informatics and high capability computing devices has offered a brand new
springboard for the engineering community to reshuffle its traditional R&D criteria. Particularly,
artificial intelligence (AI), an information processing technique, exhibits outstanding
effectiveness in accommodating the highly demanding requirements of new generation
problems. AI serves as a powerful solution to complex engineering problems, for which
conventional straightforward logical algorithms are usually inefficient. Several variants
originating from fundamental AI concept can be found in application, namely expert system,
fuzzy logic, inductive learning, genetic algorithms and Artificial Neural Network (ANN).
ANN can be customized and trained using a series of typical inputs and their corresponding
expected outputs, to establish an implicit non-linear and multi-dimensional correlation between
them while avoid exploring the constitutive relation for a complicated system. Inherently
endowed with talents in adaptability, robustness and parallelism, the ANN technique has found
substantial applications in pattern recognition, classification, functional approximation and signal
processing and system identification [160]. Inspired by the biological nervous system, the ANN
approach is a fascinating mathematical tool, which can be used to simulate a wide variety of
complex scientific and engineering problems. ANN can be customized and trained by using a
certain amount of experimental data to a well designed ANN. After the network has learnt to
solve the material problems, new data from the similar domain can then be predicted without
performing too many long experiments [161]. ANN helps in reducing the cost of
experimentation when implemented with care and enough data.
Recently ANNs have received a great deal of attention as a prediction and modeling tool in many
research areas. ANNs can be defined as massively parallel distributed processors, which have a
natural tendency to store experimental knowledge and make it available to use [162]. ANN uses
interconnected nodes called neurons where inter-connections are weighted to mimic the ability of
human brain and to learn from experience and find solutions for complex nonlinear, multi-
dimensional functional relationships. The main characteristic of the network is that the network
describing the relationship is trained directly by examples without any prescriptive formulae
about the nature of the problem. The ANN method is suitable when (i) large database is
Vol.10, No.1 Mechanical and Tribological Behavior 77
available, (ii) it is difficult to find an accurate solution for a problem by mathematical
approaches, (iii) the data set is incomplete, noisy and complex [163].
There are a number of ANN topologies. The differentiating criteria include the way information
flows through the network (e.g. feedback/feed-forward) and the method used to optimize the
model coefficients. The feed-forward and recurrent networks are suited to prediction and
forecasting applications. Recurrent networks are perceived to have a number of advantages over
feed-forward networks, especially in time series applications. However, recurrent networks do
not have any advantage over feed-forward networks in which time structure is accounted for
explicitly in the model inputs. It should be noted that the processing speed of feed-forward
networks is better than recurrent networks. In addition, they have been found to perform well in
comparison with recurrent networks in a number of real life applications.
The model parameters in recurrent and feed-forward networks are generally estimated using a
‘supervised’ algorithm, the aim of which is to minimize the error between the model outputs and
corresponding historical values. This process may be viewed as a highly nonlinear optimization
problem and a number of optimization techniques are applicable. Traditionally, the back-
propagation algorithm has been used, which is based on the method of steepest descent. In the
vast majority of papers that deal with the prediction and forecasting of environmental variables,
feed-forward networks optimized with the aid of the back-propagation algorithm (known as
back-propagation networks) have been used [164]. ANNs were traditionally used to replicate
tasks which are performed well by the human brain, including recognizing handwritten
characters, contour recognition, texture recognition, face recognition and classifying two-
dimensional shapes. However, the number of uses for ANNs is expanding rapidly and in recent
years, an increasing number of engineers and scientists have been considering the use of ANNs
for environmental modeling in preference to more conventional statistical techniques. This is
because they are non-linear, relatively insensitive to data noise and perform reasonably well
when limited data is available. In addition, the statistical distribution of the data used does not
have to be known and they can cater for cyclic and seasonal variations in the data. They have
already been successfully used to assess the effect of climatic change on river hydrology and
ecology and to predict salinity, incidences of blue-green algae, nutrient concentrations, ozone
dosage, algal concentrations, sea surface temperatures, rainfall, rainfall-runoff and the density of
brown trout spawning [164].
ANNs are generally used by engineers and scientists to capture relationships between
environmental data and to help provide a better understanding of environmental phenomena.
However, as ANNs are a relatively recent addition to the toolkit of environmental modelers, they
are generally not well understood and current and potential users tend to treat them as ‘black
box’ models. In addition, “there is a tendency among users to throw a problem blindly at a neural
network in the hope that it will formulate an acceptable solution. In the model development
78 G. B. Veeresh Kumar, C. S. P. Rao, N. Selvaraj Vol.10, No.1
phase, ANN operation and the effect of a number of internal parameters are often ignored. This
can result in inferior model performance and an inability to compare accurately the performance
of different ANN models [164].
Rasit Koker et.al. [162] used ANN model for the prediction of mechanical properties of
particulate reinforced MMCs and concluded that the ANN model with three layer feed forward
structure with the Levenberg–Marquardt (LM) training algorithm gave better and faster results
than other algorithms. Rao et.al. [165] confirmed that the ANN model serves as an effective,
simple, fast, efficient and compact tool which can simulate the stress-strain response and predict
the amount of debonding at the interface for ceramic-matrix composite. Cavaliere [166] reported
that an ANN of basic structure with back propagation (BP) algorithm and a bi-polar sigmoid
activation function for hidden and linear activation function for output layers could predict flow
curves of MMC. A 3 layered BP network which is an effective tool to predict parameters with
non-linear relationships could predict density, porosity, hardness, tensile strength, flexural
strength, toughness, roughness of machined surface, flow stress and solid particle erosion with a
reasonable accuracy [167-174]. It was reported that ANN network showed excellent performance
in predicting wear volume loss, specific wear rate and friction coefficient as a function of sliding
speed and load for different compositions of fiber and particulate reinforced composites [175,
176]. Raimundo Carlos et.al. [177] used ANN approach in modeling and building of constant life
diagrams, using a small number of S–N curves in the training set. Based on the results, they
concluded that the ANN having gating network has given more reliable results. Rashed et.al.
[178] applied ANN technique to study the effect of size and weight percent of SiC particulates,
applied pressure and test temperature on the wear resistance of Al356-SiC MMCs and have
shown that ANN is an effective tool in the prediction of the properties of MMCs and is found
more useful compared with time-consuming experimental processes. Several researchers have
developed ANN models using Matlab software. From the above, it can be concluded that ANN
can be successfully implemented for the prediction of mechanical and tribological properties of
various composite materials. A well trained ANN model can be used to predict any new data
from the same knowledge domain thus avoiding repetition of long-term experiments, wastage of
manpower and money [179-182].
5. CONCLUDING REMARKS
This review presents the views, experimental results obtained and conclusions made over the
years by numerous investigators in the field of particle reinforced Al-MMCs. A considerable
amount of interest in Al-MMCs evinced by researchers from academics and industries has
helped in conduction of various studies and has enriched our knowledge about the physical
properties, mechanical properties and tribological characteristics. Several techniques are
followed by researchers for the processing of particulate reinforced MMCs.
Vol.10, No.1 Mechanical and Tribological Behavior 79
¾ It has been studied and concluded that the density of the composites increases with the
incorporation of the hard ceramic reinforcement into the matrix material. In view of the
above conclusions on density, experiments were conducted on the Al6061-SiC and Al7075-
Al2O3 to determine the density by weight to volume ratio and by rule of mixture. The
experimental and theoretical densities of the composites were found to be in line with each
other. There is an increase in the density of the composites compared to the base matrix.
¾ The hardness of the composites was reviewed and on conclusion, it is discovered that as the
reinforcement contents increased in the matrix material, the hardness of the composites also
increased. Further, the tests conducted to determine the same indicated the (Vickers and
Brinell’s hardness) increased hardness with increased reinforcement contents when compared
with the base matrix. The mechanical properties were reviewed with respect to strength. It is
evident that the structures and properties of the reinforcements control the mechanical
properties of the composites. The reported literature regarding the variations of the
compression strength of ceramic filled aluminum composites are meager.
¾ The wear performance of hard ceramic reinforced aluminum matrix composites was
reviewed with particular emphasis on the mechanical and physical factors and material
factors also with the effect of lubrication, work hardening, Mechanical Mixed Layer, heat
treatment etc. All the factors have considerable effect on the tribological performance of Al-
MMC and counterface metal couples. From the literature it can be concluded that the ceramic
reinforced Al-MMCs will have better wear resistance than the unreinforced alloys. Further,
the techniques used by the researchers to predict the wear coefficient were also discussed.
¾ Finally there is an immense potential, scope and opportunities for the researchers, in the field
of prediction of mechanical and tribological properties of the particulate reinforced metal
matrix composites by using soft computing techniques.
ACKNOWLEDGEMENTS
The authors express their thanks to Dr. R. Chenraj Jain, Chancellor, Jain University, Prof. T.S.
Sridhar, Director, Dr. Y. Vijay Kumar, Principal, Dr. Ananda Bukkambudhi, Professor and
Head, Department of Mechanical Engineering and to Prof. R. Suresh Kumar. Assistant
Professor, Department of Mechanical Engineering, S B M Jain College of Engineering, for their
support and encouragement during the research studies.
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