Journal of Minerals and Materials Characterization and Engineering, 2013, 1, 8-14
http://dx.doi.org/10.4236/jmmce.2013.11002 Published Online January 2013 (http://www.scirp.org/journal/jmmce)
Preparation & Characterization of Al-5083
Sourabh Gargatte*, Rahul R. Upadhye, Venkatesh S. Dandagi,
Srikanth R. Desai, Bhimappa S. Waghamode
Department of Industrial & Production Engineering, B. V. B. College of Engineering & Technology, Hubli, India
Email: *firstname.lastname@example.org, email@example.com, firstname.lastname@example.org
Received September 17, 2012; revised October 25, 2012; accepted November 5, 2012
This paper reports the dry sliding wear behavior & Brinell hardness test of AA 5083 aluminium reinforced with SiC
particles fabricated by stir casting technique. Different volume fraction of SiC particles (3, 5 and 7 wt%) were used for
synthesis. The wear test has been conducted on pin-on-disc testing machine to examine the wear behaviour of the alu-
minium alloy and its composites. An attempt has been made to study the influence of wear parameters like applied load,
sliding speed, sliding distance and percentage of reinforcement on the dry sliding wear of metal matrix composites
(MMCs). A plan of experiments, based on the techniques of Taguchi, was performed to acquire data in controlled way.
An orthogonal array of L9 (34) and signal to noise ratios as smaller the better was selected. Analysis of variance
(ANOVA) was employed to investigate the influence of wear parameter on pin of aluminium MMCs. The correlation
was obtained by multiple general regressions model. Finally, conformation tests were done to make a comparison be-
tween the experimental results foreseen from the mentioned correlation.
Keywords: Metal Matrix Composites; Wear; Brinell Hardness; Orthogonal Array; Analysis of Variance;
Conventional monolithic materials have limitations with
respect to achievable combinations of strength, stiffness,
and density. In order to overcome these shortcomings
and to meet the ever-increasing engineering demands of
modern technology, Metal Matrix Composites (MMCs)
are gaining importance. In recent years, discontinuously
reinforced aluminium (DRAMMCs) based metal matrix
composites have attracted worldwide attention as a result
of their potential to replace their monolithic counterparts
primarily in automobile and energy sector.
Aluminium Matrix Composites (AMCs) refer to the
class of light weight high performance aluminium centric
material systems. The reinforcement in AMCs could be
in the form of continuous/discontinuous fibers, whisker
or particulates, in volume fractions ranging from a few
percent to 50%. Aluminium matrix composites are de-
signed to have the toughness of the alloy matrix and the
hardness, stiffness and strength of hard ceramic rein-
forcements. So, the major advantages of AMCs com-
pared to unreinforced materials are as follows: greater
strength, improved stiffness, reduced density, good cor-
rosion resistance, improved high temperature properties,
controlled thermal expansion coefficient, thermal/heat
management, enhanced and tailored electrical perfor-
mance, improved wear resistance and improved damping
capabilities. The most commonly employed Metal Matrix
Composites (MMCs) consists of aluminium alloy rein-
forced with hard ceramic particles usually silicon carbide,
alumina and soft particles usually graphite, talc. Silicon
carbide (SiC) are known for low density, high strength,
low thermal expansion, high thermal conductivity, high
hardness, high elastic modulus, excellent thermal shock
resistance and superior chemical inertness [1,2].
Metal Matrix Composites (MMCs) are conventionally
fabricated using different techniques such as powder me-
tallurgy, squeeze casting, semi-solid stirring process. Stir
casting is considered to be best method to prepare large
quantity of composites due to its processing simplicity,
flexibility & low cost. An inherent difficulty in fabrica-
tion of SiC-Al alloy composites is that molten Al alloys
normally do not wet considerably the ceramic reinforce-
ments. Good interfacial bond strength between SiC-Al
alloy composites can be achieved by optimizing stirring
parameters, such as stir temperature and speed. It is well
known that the SiC reinforcement tend to react with
Aluminium during processing, leading to the formation
of Al4C3 and Si at the interface. Efforts have been di-
rected to prevent the chemical reaction at interfaces by
opyright © 2013 SciRes. JMMCE
S. GARGATTE ET AL. 9
oxidation of SiC, coating of SiC particles, or alloying of
Al matrix with Mg or Si.
Magnesium addition to aluminum reduces its casting
fluidity at the same time as it reduces the surface tension
of the aluminum sharply. The presence of magnesium in
aluminum alloy matrix during composite fabrication not
only strengthens the matrix but also scavenges the oxy-
gen from the surface of the dis-persoid, leading to an in-
crease in the surface energy, of the dis-persoids. It can
reduce Al2O3, either to form Al, MgA12O4 or MgO de-
pending upon its concentration [3-7].
Applications of AMCs materials take place in auto-
mobile, mining, mineral, aerospace, defense and other re-
lated sectors. In the automobile sector, Al composites are
used for making various components such as brake drum,
cylinder liners, cylinder blocks, disk brakes, piston crown
and drive shaft . In general, these materials are deve-
loped for the production of high wear resistant compo-
nents. The major part of application of AMCs includes
moving and sliding parts, hence the study of wear pro-
perties of these materials is very important to enhance the
understanding of the behavior of these materials while in
Wear is a surface phenomenon that occurs by dis-
placement and detachment of material because wear usual-
ly implies a progressive loss of weight and alteration of
dimensions over a period of time. All mechanical com-
ponents that undergo sliding or rolling contact are subject
to some degree of wear. Such components are bearings,
gears, seals, guides, piston rings, piston crown, disk brakes
Hardness is defined as the ability of a material to resist
plastic deformation. It is the property of a metal, which
gives it the ability to resist being permanently, deformed
(bent, broken or have its shape changed), when a load is
applied. The greater the hardness of the metal, the greater
resistance it has to deformation.
On the present investigation an attempt is made to find
the influence of wear parameters on dry sliding wear of
the composites and to establish correlation between slid-
ing speed, load, sliding distance, percentage of rein-
forcement and combined effect of these parameters on dry
sliding wear of the composites and to carry out Brinell
hardness test on composite specimens.
A number of research works have been proposed to ex-
plain the sliding wear behavior of composites.
Dry sliding wear characteristics of Al-SiCp based com-
posites by Taguchi Approach was undertaken by S. Ba-
savarajappa and G. Chandramohan , fabricated suc-
cessfully by liquid metallurgy stir casting technique. Ex-
periment was carried out using a pin-on-disc machine. In
their studies, addition of SiC reinforcement has been
found to reduce the wear rate, compared to matrix alloy.
Results of ANOVA, indicates sliding distance & load has
major influence for mass loss. During the rubbing action
between the surfaces, the exposed particles of SiC,
causes the scratching of counter surface, subsequently
resulting in oxidation of surface. The formation of thin
film of oxide layer acts as contaminant layers between
the sliding surfaces, resulting reduce wear. At higher load
and increased sliding distance, the oxide layer is removed
resulting in increased mass loss.
The wear properties of the Al-Mg-Cu alloys were con-
siderably improved by the addition of SiC particles; how-
ever, wear resistance of the composites was much higher
than that of the unreinforced aluminium alloys. Addition
of SiC particles caused improvement of wear resistance
of Al-4 wt% Mg-Cu alloy. The hardness and wear resis-
tance of Al-4 wt% Mg alloy increased with increase of
copper contains up to 5 wt%, successfully fabricated by
liquid metallurgy method by Adel Md Hassan .
It is observed from the literature review that the wear
behavior of composite is influenced by operating pa-
rameters like sliding speed, load, sliding distance & per-
centage reinforcement. And by adopting Taguchi’s opti-
mization method, the results were nearly accurate to the
predicted values. The wear resistance and overall me-
chanical properties has improved by increase of percen-
tage reinforcement of particulates in the matrix.
2. Taguchi Technique
The Taguchi method drastically reduces the number of
experiments that are required to model the response func-
tion compared with the full factorial design of experi-
ments. The major advantage of this technique is to find
out the possible interaction between the parameters. The
Taguchi technique is devised for process optimization
and identification of optimal combination of the factors
for a given response . This technique is divided into
three main phases, which encompasses all experimenta-
tion approaches. The three phases are 1) The planning
phase; 2) The conducting phase and 3) The analysis
phase. Planning phase is the most important phase of the
experiment. This technique creates a standard orthogonal
array to accommodate the effect of several factors on the
target value and defines the plan of experiments. The
experimental results are analyzed using analysis of means
and variance to study the influence of factors.
The DRAMMC material selected for the present investi-
gation was based on Al-Mg matrix alloy, designated as
aluminium association as AA 5083. This matrix alloy has
low density 2.6 gm/cm3 among all Al alloys and provides
Copyright © 2013 SciRes. JMMCE
S. GARGATTE ET AL.
excellent combination of strength, high corrosion resis-
tance to seawater, high tensile strength, exceptionally
tough and good machinability & weldability. The Table 1
shows chemical composition of AA 5083 found out by
Spectro analysis. The SiC particles, which were used to
fabricate the composite, the particle size range from 1 -
3.2. Preparation of the Composite
The synthesis of particulate metal matrix composites
(MMCs) used in the present study was carried out by the
liquid metallurgy Stir casting method to prepare compo-
site specimens. This method is most economical to fa-
bricate composites particulates. In this process, matrix
alloy (AA5083) was firstly superheated over its melting
temperature up to 760˚C in electric furnace. The slurry
was degassed and slag powder was sprayed to remove
any slag content. The temperature was lowered gradually
below the liquidus temperature at 720˚C. At this tem-
perature, the SiC particles, preheated at 800˚C to form a
layer of SiO2 on their surface to improve their wettability
with molten metal, were incorporated into the slurry by
weight ratio. The slurry temperature was increased and
automatic stirring was continued for 10 min at an average
stirring speed of 450 ~ 500 rpm. The melt was super-
heated above liquidus temperature and finally poured
into the Cast Iron (CI) permanent moulds of 20 mm in
diameter and height of 210 mm. SiC reinforcement per-
centage were 3, 5 and 7 wt%. Figure 1 shows the melt-
ing and stirring mechanism and CI permanent moulds.
3.3. Experimental Setup and Procedure
A pin on disc model (model type, TR-201CL of Ducom,
Table 1. Composition of AA 5083 wt%.
India), was used to investigate the dry sliding wear char
acteristics of composite as per ASTM G99 - 95 standards.
The wear specimen (pin) of 8mm diameter and 25 mm
height was cut from as cast samples machined. The weight
of specimen was measured on a digital micro balance
weighing machine with an accurate 0.1 mg and initial
weight of the specimen was noted. During the test the pin
was pressed against the counterpart, rotating against
EN32 steel disc with hardness of 65 HRC, shown in
Figure 2. After running through a fixed sliding distance,
the specimen were removed and weighed to determine
the weight loss due to wear. The difference in the weight
measured before and after test gave the sliding wear of
the composite specimen and then volume loss is calcu-
Brinell hardness test was used to investigate the hardness
of composite specimens as per ASTM E-10 standard.
The specimens of 20 mm × 20 mm were cut from as cast
condition and specimen surfaces were polished. Brinell
hardness test is conducted for soft material like alumi-
num, aluminum alloy with a load of 500 kgf. The hard-
ened-steel round ball of 10 mm diameter was used as
penetrator, Brinell hardness samples shown in Figure 3.
Figure 1. Preparation of Al-5083 composite & CI perma-
Figure 2. Pin on disc se tup.
Copyright © 2013 SciRes. JMMCE
S. GARGATTE ET AL. 11
3.4. Plan of Experiment
3.4.1. Wear Test
The experiments were conducted as per the standard Ta-
guchi’s orthogonal array. The selection of the orthogonal
array was based on the condition that the degrees of
freedom for the orthogonal array should be greater than
or equal to sum of those wear parameters. In the present
investigation an L9 (34) orthogonal array was chosen,
which has 9 rows and 4 columns as shown in Table 2. In
the present investigation the wear parameters chosen for
the experiments were 1) sliding speed, 2) load, 3) sliding
distance, 4) weight percentage of SiC. Table 3 indicates
the factors and their level. The experiment consists of 9
tests (each row in the L9 orthogonal array) and the co-
lumns were assigned with parameters.
The first column in Table 3 was assigned to sliding
speed, second column was assigned to load, third column
was assigned to sliding distance and the fourth column
was assigned to weight percentage of SiC. The response
to be studied was the wear with the objective as smaller
the better S/N ratio.
Figure 3. Hardness specimens of Al-5083 composites.
Table 2. Orthogonal array L9 (34) of Taguchi.
L9 (34) test 1 2 3 4
1 1 1 1 1
2 1 2 2 2
3 1 3 3 3
4 2 1 2 3
5 2 2 3 1
6 2 3 1 2
7 3 1 3 2
8 3 2 1 3
9 3 3 2 1
Table 3. Process parameters with their values at three levels.
Level Sliding speed
N Sliding distance
1 0.314 9.81 500 3
2 0.942 29.43 1000 5
3 1.570 49.05 1500 7
4. Results and Discussion
Brinell hardness test was carried out on Al-5083 SiC
composites and average of three reading for each speci-
men was calculated. Table 4 & Figure 4 show the varia-
tion of Brinell hardness number respect to different per-
centage of reinforcement. Al-5083 reinforced with 3, 5,
& 7 wt pct of SiC shows greater Brinell hardness num-
ber compared base metal. As the reinforced percentage of
SiC has increased the Brinell hardness number has in-
4.2. Wear Test
The plan of tests was developed with the aim of relating
the influence of sliding speed, load, sliding distance &
reinforcement percentage with dry sliding wear of the
composite. On conducting experiments as per orthogonal
array L9 (34), the wear results for various combinations of
parameters were obtained and are shown in Table 5.
4.3. Main Effects
The values of wear rate for each factor i.e. sliding speed,
load, sliding distance & reinforcement percentage at each
level i.e. level 1, level 2 and level 3 was obtained and
results is summarized in Tables 5 and 6 presents the
main effect graph for wear rate. The quality characteris-
tics investigated in this study was “smaller the better”.
4.4. Signal to Noise Ratios (S/N)
The signal to noise ratio measures the sensitivity of the
Table 4. Brinell hardness test.
0 3 5 7
Mean BHN value57.5 59.5 63.6 63.4
Figure 4. Brinell hardness number of Al-5083 composite.
Copyright © 2013 SciRes. JMMCE
S. GARGATTE ET AL.
Table 5. Orthogonal array of Taguchi for wear L9 array.
m2/N S/N ratio
1 0.314 9.81 500 3 9.330‒19.39
2 0.314 29.43 1000 5 6.370‒16.08
3 0.314 49.05 1500 7 4.598‒13.25
4 0.942 9.81 1000 7 0.11119.09
5 0.942 29.43 1500 3 2.160 ‒6.68
6 0.942 49.05 500 5 0.10020
7 1.570 9.81 1500 5 0.05026.02
8 1.570 29.43 500 7 0.4536.87
9 1.570 49.05 1000 3 0.141 17.01
Table 6. Response table for S/N ratio smaller the better
Level Sliding Speed
SiC wt pct
1 ‒16.244 8.572 2.493 ‒3.024
2 10.801 ‒5.2986.675 9.979
3 16.638 7.921 2.027 4.240
quality investigated to those uncontrollable factors in the
experiment. The higher value of S/N ratio is always de-
sirable because greater S/N ratio will result in smaller
product variance around the target value. As mentioned
earlier the quality characteristic used in this study was
“Smaller the better”. The S/N ratio analysis was per-
formed using statistical software “MINITAB R15”.
From Table 5 it can be seen that wear rate of
experiment number 7 is smaller and S/N ratio is largest.
Based on main effects of S/N ratio, from the Figure 5 the
optimal combination of parameters and their levels for
achieving minimum wear rate is A3B1C2D2 i.e. sliding
speed at level 3 (1.57 m/s), load at level 1 (9.81 N),
sliding distance at level 2 (1000 m) & wt% of SiC at
level 2 (5 wt%).
4.4.1. Effect of Sliding Speed
The influence of sliding speed on wear rate is depicted in
Table 5. From the table it can be inferred the wear
mechanism strongly dependent on the sliding speed. At-
low sliding speed the wear rate of composite is higher
and the sliding speed as increases from 0.314 to 1.570
m/s the wear rate was observed to decrease. This is be-
cause during the rubbing action between the surfaces, the
exposed particles of SiC, causes the scratching of counter
surface, subsequently resulting in oxidation of surface.
The formation of thin film of oxide layer acts as con-
taminant layers between the sliding surfaces, resulting
1. 5700. 9420. 314
-20 49. 0529. 439. 81
Me an of SN ratios
Sliding distancewt % of SiC
M a in E ffec t s Plo t for SN rat ios
Sign al-t o-n o ise: Smaller is bett er
Figure 5. Main effects plot for S/N ratios.
reduce wear. At higher load and increased sliding dis-
tance, the oxide layer is removed resulting in increased
4.4.2. Effect of Applied Load
Increase in the addition of SiC restricts the flow or de-
formation of matrix material with respect to load. At
lower load the wear rate for 3 wt% is greater but for
same load for 7 wt% is less and for 5 wt% wear rate is
lesser compared to 3 & 7 wt%. For higher load (49.05 N),
wear rate has observed higher for 7% reinforcement with
respect to sliding distance.
4.4.3. Effect of Reinforcement Percentage
High wear resistance of composites materials is due to
the presence of SiC particles that act as load-supporting
elements. The effect of wear rate for different values of
speed, load & distance is shown in Table 5. As the rein-
forcement percentage from 3% to 7 wt% increases, it is
observed that the wear rate has decreased with respect to
speed, load & distance. The addition of SiC particles in-
creases the hardness which may increases the wear resis-
tance of the composites.
4.5. Analysis of Variance (ANOVA)
Analysis of variance was performed using statistical
software “MINITAB R15”. ANOVA has been carried
out to analyze the influence of wear parameter 1) Sliding
speed, 2) Load, 3) Sliding distance and 4) Weight per-
centage of SiC particles. The analysis was carried out for
a level of significance of 5% (i.e. the level of confidence
95%). Table 7 shows the results of ANOVA analysis.
One can observe from ANOVA analysis that 1) sliding
speed, 2) Load, 3) sliding distance and 4) weight per-
centage of SiC particles has the influence on wear of the
composite. The last column in Tabl e 7 shows percentage
contribution (p) of factors on the total variation indicat-
ing their degree of influence the results.
One can observe from ANOVA table the sliding speed
Copyright © 2013 SciRes. JMMCE
S. GARGATTE ET AL. 13
(p = 84.46%) has major influence on wear rate, load
(p = 4.65%) and weight percentage of SiC (p = 8.31%)
has moderate influence on wear rate and sliding distance
(p = 2.39%) has negligible influence on wear rate. The
sliding distance is influencing comparatively less (p =
2.39%) which indicates that there is no appreciable in-
crease in wear by increasing the sliding distance from
500 to 1500 m.
The ANOVA has resulted in zero degree of freedom
for error term, it is necessary to pool the factor having
less influence, for correct interpretation of results. In
Table 8 shows F-test, if F > 4, then it means that the
change of the design parameter has significant effect on
quality characteristic. Table 8 shows pooled ANOVA
table, shows that the pooled error is 2% were important
factors are not omitted from experiments
4.6. General Regression Analysis
To establish correlation between the wear parameters 1)
Sliding speed, 2) Load, 3) Sliding distance, 4) Weight
percentage of SiC particles and dry sliding wear, General
regression model was obtained using statistical software
“MINITAB R15”. The terms that are statistically sig-
nificant are included in model. The regression co-effi-
cient of model is 0.82 The equation obtained as follows
W = 12.4 ‒ 5.22 Sliding speed ‒0.0395 Load ‒0.00102
Sliding distance ‒0.539 SiC/wt pct-(1)
The sliding wear of composites can be calculated by
substituting the recorded values of variables for the
Table 7. Summary of ANOVA.
Sliding speed 2 78.960 39.480 84.46
Load 2 4.341 2.171 4.65
distance 2 2.236 1.118 2.39
SiC/wt pct 2 7.757 3.879 8.31
Error 0 - -
Total 8 93.294
Table 8. Summary of pooled ANOVA.
speed 2 78.960 39.480 35.31 84.46
Load 2 4.341 2.171 1.94 4.65
pct 2 7.757 3.879 3.47 8.31
Error 2 2.236 1.118
Total 8 93.294
Equation (1). The positive value of co-efficient suggests
that the sliding wear of material increases with their as-
sociated variables. Whereas the negative value of the co-
efficient suggests that the sliding wear of the material
will decreases with the increase in associated variables.
The magnitude of the variables indicates the weightage
of each of these factors. It is observed from Equation (1)
that the sliding speed has major effect on wear of the
composites, which is followed by reinforcement, applied
load and sliding distance for the tested range of the vari-
4.7. Confirmation Test
Once the optimal combination of process parameters and
their levels was obtained, the final step was to verify the
estimated results against experimental value. It may be
noted that if the optimal combination of parameters and
their levels coincidently match with one of the experi-
ments in the orthogonal array (OA), then no confirmation
test is required.
Confirmation test was required in present case because
the optimum combination of parameter and their levels
3B1C2D2 did not correspond to any experiment of
the orthogonal array.
One tray at optimal combination of parameter and
their levels A3B1C2D2 experiment was conducted on pi-
non-disc machine. The value of wear rate obtained from
experiment was compared with estimated value from the
regression model Equation (1) as shown in Table 9. It
can be seen from this Table 9 the difference between
experimental results and the estimated results is 0.009.
This indicates that the experimental result of wear rate is
close to the estimated value. This verifies that the ex-
perimental results are strongly correlated with the esti-
mated result, as the error is 8.1%.
From the analysis on the results of dry sliding wear of the
SiC particles reinforced MMCs and results of hardness of
material obtained, the following conclusions can be drawn
from the study.
1) A Taguchi orthogonal array, the signal to noise
(S/N) ratio, analysis of variance (ANOVA) and General
regression model were successfully used for optimization
of wear parameters.
Table 9. Results of confirmation experiment.
Experiment Regression model
Equation (1) Difference Error
Level A3B1C2D2 A
m2/N 0.111 0.102 0.009 8.1
Copyright © 2013 SciRes. JMMCE
S. GARGATTE ET AL.
Copyright © 2013 SciRes. JMMCE
2) Based on main effects of S/N ratios the optimal
combination of parameters and their levels for achieving
minimum wear rate is A3B1C2D2 i.e. sliding speed at
level 3 (1.57 m/s), load at level 1 (9.81 N), sliding dis-
tance at level 2 (1000 m) & wt pct of SiC at level 2 (5
3) From the ANOVA analysis, results shows that sli-
ding speed (p = 84.46%) has major influence on wear
rate, load (p = 4.65%) and weight percentage of SiC (p =
8.31%) has moderate influence on wear rate and sliding
distance (p = 2.39%) has negligible influence on wear
rate. The Pooled error is 2% for the factors and co-effi-
cient of regression value of 0.82 shows satisfactory cor-
relation was obtained.
4) A General regression mathematical model has been
successfully developed to predict the wear rate of
AA5083-SiC particle composite. The developed model
can be effectively used to predict the wear rate of com-
posite at 95% confidence level within the range of inves-
5) The confirmation test showed that error associated
with dry sliding wear of composite with combination of
optimal parameter A3B1C2D2 is 8.1%.
6) It was observed that the wear rate decreased for in-
creasing the reinforcement percentage of SiC particles.
Al-5083 reinforced with 3, 5 & 7 wt pct shows lesser
wear rate compared with pure Al-5083 alloy.
The hardness of Al-5083 increased considerably with
increase in SiC particles up to 7 wt%.
The authors like to thank Prof. N. Vijaya Kumar, Dr. V.
N. Gaitonde, Prof B. S. Kakol, Dr. B. B. Kotturshettar,
Prof S. B. Burli, Prof Vijay Jatti, Prof Siddhalingeshwar,
Om Gargatte and our parents. Authors also thank De-
partment of Industrial & Production Engineering, B. V. B
College of Engineering & Technology Hubli, Universal
Trading Corporation Mumbai, Ghousia College of Engi-
neering Bangalore, Basaveshwar Engineering College,
Bagalkot, Karnataka Material Testing & Research Centre
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