Journal of Minerals and Materials Characterization and Engineering, 2012, 11, 757-768
Published Online August 2012 (http://www.SciRP.org/journal/jmmce)
Optimization of Friction and Wear Behaviour in Hyb rid
Metal Matrix Composites Using Taguchi Technique
V. C. Uvaraja1*, N. Natarajan2
1Department of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India
2Suguna College of Engineering, Coimbatore, India
Received February 5, 2012; revised March 12, 2012; accepted March 31, 2012
ABSTRACT
Al-7075 alloy-base matrix, reinforced with mixtures of silicon carbide (SiC) and boron carbide (B4C) particles, know as
hybrid composites have been fabricated by stir casting technique (liquid metallurgy route) and optimized at different
parameters like sliding speed, applied load, sliding time, and percentage of reinforcement by Taguchi method. The
specimens were examined by Rockwell hardness test machine, Pin on Disc, Scanning Electron Microscope (SEM) and
Optical Microscope. A plan of experiment generated through Taguchi’s technique is used to conduct experiments based
on L27 orthogonal array. The developed ANOVA and the regression equations were used to find the optimum wear as
well as coefficient of friction under the influence of sliding speed, applied load, sliding time and percentage of rein-
forcement. The dry sliding wear resistance was analyzed on the basis of “smaller the best”. Finally, confirmation tests
were carried out to verify the experimental results.
Keywords: Hybrid Metal Matrix Composites; Stir-Cast; Dry Sliding Wear, Orthogonal Array; Taguchi Technique;
Analysis of Variance
1. Introduction
Two phases namely a matrix phase and a reinforcement
phase constitute composite materials. Matrix and rein-
forcement phase work together to produce combination
of material properties that cannot be met by the conven-
tional materials [1]. In this study the composite is pro-
duced by using stir casting method, which is one of the
economic and commonly used methods in Liquid Metal-
lurgy. Most of studies made in automotive and aerospace
field shows that the material used for components should
posses good toughness with better tribological properties.
Hence to meet the automotive application requirements
an attempt has been made to develop the Al-7075 based
hybrid composite, having combination of both toughness
and tribological properties like wear resistance. The
greatest improvement in tribological properties of com-
posite is generally obtained using particle reinforcement
of silicon carbide and boron carbide. Yoshiro Iwai et al.
[2] found that the initial sliding distance require to
achieve mild wear decreased with increasing volume
fraction and also wear rate decrease linearly with volume
fraction. Daoud et al. [3] reported that the addition of
magnesium alloy to composite during production ensures
good bonding between the matrix and the reinforcement.
Aluminum alloys possess a number of mechanical and
physical properties that make them attractive for automo-
tive applications, but they exhibit extremely poor resis-
tance to seizure and galling [4]. N. Natarajan et al. [5]
recommended SiC particulate reinforcement of metal
matrix is more appropriate aspirant material for automo-
bile purpose, but a new friction material is to be devel-
oped. S. Basavarajappa et al. [6] inspected in detail slid-
ing speed, load, sliding distance, percentage of rein-
forcement and mutual effect of these factors, which ma-
nipulate the dry sliding wear performance of matrix alloy
(Al-2219) reinforced with SiC. N. Radhika et al. [7]
found taguchi technique as a valuable technique to deal
with responses influenced by multi-variables. It is for-
mulated for process optimization and detection of opti-
mal combination of the parameters for a given response.
This method significantly reduces the number of trials
that are required to model the response function com-
pared with the full factorial design of experiments. The
most important benefit of this technique is to find out the
possible interaction between the factors. In view of the
above article, an assessment is made to investigate the
outcome of sliding speed, load, sliding time and volume
fraction of reinforcement on the dry sliding wear behav-
ior of the particulate reinforced Al-7075 alloy with a
constant weight percentage (3%) of B4C particulate and
*Corresponding author.

V. C. UVARAJA, N. NATARAJAN
758
varying range (5%, 10%, 15%) of SiC particulate com-
posites using taguchi method. The Analysis of variance
was used to find the percentage contribution (Pr) of
various process parameters and their correlations on dry
sliding wear of the hybrid composite materials.
2. Material Selection
In the present investigation, Dry sliding wear tests were
performed on SiC and B4C particulates reinforced Al-
7075 alloy matrix composite. The Al-Mg-Si base alloys
were purchased from Metal Mart Pvt. Ltd., Coimbatore
(Tamil Nadu) India. Table 1 shows the nominal compo-
sition weight percentage of matrix materials. The hard-
ness measurements were made by applying a load of
100 kg and the average is calculated from 10 different
values of the experiments. The density measurements
were all set according to the ASTM standard C1270-88.
The value of hardness and density for matrix material
were 67.17 HRC and 2.81 g/m3 respectively in tempered
condition. The particulate morphology study results
such as shape of both reinforcements were angu-
lar-irregular and size of SiC (30 μm - 70 μm) and B4C (5
μm - 20 μm).
2.1. Manufacturing of the Hybrid Composites
Material
Stir casting technique is one of the popular Liquid Met-
allurgy Route (LMR) and also known as a very promis-
ing route for manufacturing near net shape hybrid metal
matrix composite components at a normal cost [8]. In
this present work, stir casting technique was used to fab-
ricate Al-7075 alloy with varying weight percentages of
SiC (5%, 10%, and 15%) and a constant weight percent-
age of B4C (3%) reinforcements. In order to achieve
good binding between the matrix and particulates, one
weight percent of magnesium alloy is added. The ex-
perimental set up was shown in Plate 1. The stir casting
furnace is mounted on the floor and the temperature of
the furnace is precisely measured and controlled in order
to achieve sound quality composite. Two thermocouples
and one PID controller were used for this purpose. As
mild steel materials are having high temperature stability,
they are selected as stirrer rod and impeller.
This stirrer was connected to 1HP DC Motor through
flexible link and was used to stir the molten metal in
semi solid state. The screw operator lift is used to bring
the stirrer in contact with the composite material. The
melt was maintained at a temperature between 800˚C to
875˚C for one hour. Vortex was created by using a me-
chanical stirrer. Weighed quantity of SiC (5, 10 and 15
wt%) along with 3 weight percentage of B4C particulate,
preheated to 600˚C were added to the melt with constant
stirring for about 10 min at 500 to 650 rpm. After com-
plete addition of the particles to the melt, the composite
alloy was tilt poured into the preheated (300˚C) perma-
nent steel mould and allowed to cool in atmospheric air.
The billet was then removed from the mould and ma-
chined for required dimensions. The uniform distribution
of particulates reinforced in the matrix was examined
with the help of Optical-Microscope. The optical micro-
graphs of unreinforced alloy and the composite with 5,
10, 15 wt% of reinforcement are shown in Plates 2-5
respectively.
Table 1. Chemical composition of the matrix alloy.
Si Fe Cu Mn Mg Cr Ni Zn Ti Ag
>0.130 >0.203 1.62 0.0742.42 0.1830.03 >3.6 0.0490.024
B Be Bi Ca Cd Co Li Na P Pb
0.0025 0.001 0.0013 0.00050.00030.0003<0.0010<0.00020.0005<0.0010
Sn Sr V Zr Al
0.004 <0.0001 0.0066 0.0056 90.52
Plate 1. Stir casting setup.

V. C. UVARAJA, N. NATARAJAN 759
Plate 2. Optical Microscopic image of unreinforced Al (7075)
alloy at 100× magnification.
Plate 3. Optical Microscope image of 5% SiC + 3% B4C
particulate reinforced Al (7075) composite at 100× magni-
fication.
Plate 4. Optical Microscope image of 10% SiC + 3% B4C
particulate reinforced Al (7075) composite at 100× magni-
fication.
Plate 5. Optical Microscope image of 15% SiC + 3% B4C
particulate reinforced Al (7075) composite at 100× magni-
fication.
051015
60
80
100
2.2. Microstructure and Hardness
The resistance indentation or scratch is termed as hard-
ness. Hardness test was carried out at room temperature
using Rockwell hardness tester with at least six indenta-
tions of each sample and then the average values were
utilized to calculate hardness number. The hardness of
MMC increases with the volume fraction of particulate in
the alloy matrix. The added amount of SiC and B4C par-
ticles enhances hardness, as these particles are harder
than Al alloy rendering their inherent property of hard-
ness to soft matrix as shown in Figure 1. Composites
with higher hardness could be achieved by this technique
which may be due to the fact that silicon carbide and
boron carbide particles act as obstacles to the motion of
dislocation.
Hardness (HRC)
Percentage of reinforcement
The hardness graph shows that the sample with less
than 5 wt% of SiC and 3 wt% B4C particulate behaves
almost the same as unreinforced. But the sample with 15
wt% SiC and 3 wt% B4C showed slightly high hardness
and low toughness as compare to 10 wt% SiC and 3 wt%
B4C. Higher the percentage of particulates in the matrix,
lesser is the toughness. Therefore, from this study it is
evidently indicated that 10 wt% SiC and 3 wt% boron
Figure 1. Hardness of unreinforced alloy and composites at
different volume fraction.
carbide composite sample have high hardness and good
toughness. Hence this may be considered as the optimum
weight percentage of the particulate to achieve better
hybrid composite properties for heavy vehicle applica-
tions.

V. C. UVARAJA, N. NATARAJAN
760
2.3. Mechanism of Wear Test
The composite specimens were rubbed against hardened
steel. Dry sliding wear tests were carried out using pin-
on-disc type wear tester at different parameters like slid-
ing speed, applying load, Sliding time and percentage of
reinforcement were varied in the range given in Table 2.
Plate 6 shows the complete pin-on-disc wear test ex-
perimental setup. The slider disc is made up of 0.95% to
1.20% carbon (EN31) hardened steel disc with hardness
of 62 HRC having diameter 165 mm. The pin test sample
dimensions were 12 mm diameter and 32 mm height.
Care should be taken to note that the test sample’s end
surfaces were flat and polished metallographically prior
to testing. Conventional aluminium alloy polishing tech-
niques were used to get ready the contact surfaces of the
monolithic composite aluminium specimen for wear test.
The procedure involves grinding of composite alumin-
ium surfaces manually by 240, 320, 400 and 600 grit
silicon carbide papers and then polishing them with 5, 1
& 0.5 µm alumina using low speed polishing machine.
This preparation technique created considerable surface
relief between hard and soft aluminium matrix. The pol-
ished surfaces were cleaned ultrasonically with acetone
and methanol solutions.
The counter face materials were also polished and
cleaned ultrasonically in acetone and methanol solutions
before each wear test. The steel slider was polished using
the above described procedure and all the tests were
conducted at room temperature. The wear rates measured
in weight units were obtained by weighing the specimen
before and after the experiment and then converted to
volumetric wear rates. The micro structural investigation
and semi quantitative chemical analysis on the worn sur-
faces were performed by SEM.
Table 2. Process parameters and levels.
Level Speed
(m/s)
Load
(N)
Time
(min)
Percentage of
Reinforcement %
1 1.5 10 5 5
2 3 20 10 10
3 4.5 40 15 15
Plate 6. A pin-on-disc wear testing machine.
3. Design of Experiment
An experiment is designed in such a way to evaluate si-
multaneously two or more factors which possess their
ability to affect the resultant average or variability of
particular product or process characteristics. To accom-
plish this in an effective and statistically proper fashion,
levels of the factors are varied in a strategic manner. The
results of the particular test combinations are observed
and the complete set of results are analyzed to determine
the preferred level of the various influencing factors
whether increases or decreases of those levels will poten-
tially lead to further improvement [9]. The design of ex-
periment process is divided into three main phases or
planning phases namely, the conducting phase and the
analysis phase, which encompass all experimentation
approaches.
Investigation of the experimental outcomes uses signal
to noise ratio to support the determination of the finest
process design. This method is effectively used to study
of dry sliding wear behavior of composites materials [10].
In this work, the “smaller the best” quality characteristics
were taken to finding the minimum wear rate and coeffi-
cient of friction.
4. Plan of Experiment
The experiments were conduct as per the standard or-
thogonal array. The selection of the orthogonal array is
based on the condition that the degrees of freedom for
the orthogonal array should be greater than or at least
equals sum of those of wear parameters. In the present
investigation an L27 (313) orthogonal array was chosen as
shown in Figure 2, which has 27 rows corresponding to
the number of tests (20 degrees of freedom) and 13 col-
umns at three levels and four factors, as shown in Table
3.
1
5
2
(3, 4)
(8, 11)
(6, 7)
13 12 109
Figure 2. Linear graphs for L27 array.

V. C. UVARAJA, N. NATARAJAN
761
best, which is calculated as logarithmic transformation of
loss function as shown below,
The wear parameters chosen for the experiment are 1)
sliding speed; 2) applied load; 3) sliding time; and 4)
percentage of reinforcement of SiC and B4C materials.
The experiment consists of 27 tests (each row in the L27
orthogonal array) and the columns were assigned with
parameters. The first column was assigned to sliding
speed (S), second column was assigned to applied load
(L), fifth column was assigned to sliding time (T) and
ninth column was assigned to percentage of reinforce-
ment (R) and the remaining columns were assigned to
their interactions. The experiments were conducted as
per the orthogonal array with level of parameters given in
each array row. The output to be studied is wear rate and
coefficients of friction of the test samples are repeated
three times corresponding to 81 tests. The experimental
observations are further transformed into Signal to noise
ratio. The response to be studied was the wear rate and
coefficient of friction with the objective as smaller the

2
1
10 logi
SY
Nn

 

  (1)
where “n” is the numbers of observations, “Yi” is the
measured value of wear rate and coefficient of friction. It
is suggested that quality characteistics are optimised
when the SN response is as smaller as possible.
5. Results and Dissussion
Experimental values of wear rate and coefficient of fric-
tion and the calculated values of signal to noise ratio for
a given response using Equation (1), and are listed in
Table 4. The Taguchi’s technique suggested that the ana-
lyzing of signal to noise ratio using conceptual approach
that involves graphing the special effects and visually
making out the significant aspects.
Table 3. Orthogonal array L27 (313) of taguchi method.
L27 (313) 1 2 3 4 5 6 7 8 9 10 11 12 13
1 1 1 1 1 1 1 1 1 1 1 1 1 1
2 1 1 1 1 2 2 2 2 2 2 2 2 2
3 1 1 1 1 3 3 3 3 3 3 3 3 3
4 1 2 2 2 1 1 1 2 2 2 3 3 3
5 1 2 2 2 2 2 2 3 3 3 1 1 1
6 1 2 2 2 3 3 3 1 1 1 2 2 2
7 1 3 3 3 1 1 1 3 3 3 2 2 2
8 1 3 3 3 2 2 2 1 1 1 3 3 3
9 1 3 3 3 3 3 3 2 2 2 1 1 1
10 2 1 2 3 1 2 3 1 2 3 1 2 3
11 2 1 2 3 2 3 1 2 3 1 2 3 1
12 2 1 2 3 3 1 2 3 1 2 3 1 2
13 2 2 3 1 1 2 3 2 3 1 3 1 2
14 2 2 3 1 2 3 1 3 1 2 1 2 3
15 2 2 3 1 3 1 2 1 2 3 2 3 1
16 2 3 1 2 1 2 3 3 1 2 2 3 1
17 2 3 1 2 2 3 1 1 2 3 3 1 2
18 2 3 1 2 3 1 2 2 3 1 1 2 3
19 3 1 3 2 1 3 2 1 3 2 1 3 2
20 3 1 3 2 2 1 3 2 1 3 2 1 3
21 3 1 3 2 3 2 1 3 2 1 3 2 1
22 3 2 1 3 1 3 2 2 1 3 3 2 1
23 3 2 1 3 2 1 3 3 2 1 1 3 2
24 3 2 1 3 3 2 1 1 3 2 2 1 3
25 3 3 2 1 1 3 2 3 2 1 2 1 3
26 3 3 2 1 2 1 3 1 3 2 3 2 1
27 3 3 2 1 3 2 1 2 1 3 1 3 2

V. C. UVARAJA, N. NATARAJAN
762
Table 4. Orthogonal array and results of HMMC’s.
Ex.
No
Speed
(m/s) S
Load
(N) L
Time
(min) T
Reinforcement
(%) R
Wear Rate
mm3/m Wr
S/N Ratio for
Wear (db)
Coefficient of
Friction Cf
S/N Ratio for Coefficient
of Friction (db)
1 1.5 10 5 5 0.00235452.563870 0.38348 8.325145
2 1.5 10 10 10 0.00175655.109509 0.32987 9.633143
3 1.5 10 15 15 0.001236858.154010 0.306 10.28557
4 1.5 20 5 10 0.001912354.368879 0.331 9.603440
5 1.5 20 10 15 0.00134857.406202 0.32 9.897000
6 1.5 20 15 5 0.002510352.005487 0.39 8.178707
7 1.5 40 5 15 0.00198154.062310 0.3422 9.314399
8 1.5 40 10 5 0.00278251.112857 0.432 7.290325
9 1.5 40 15 10 0.00231 52.727760 0.3623 8.818633
10 3 10 5 10 0.00101259.896389 0.30554 10.29863
11 3 10 10 15 0.00073962.627111 0.2867 10.85144
12 3 10 15 5 0.00112 59.015639 0.35608 8.969048
13 3 20 5 15 0.00103959.667689 0.30335 10.36112
14 3 20 10 5 0.00239 52.432041 0.3784 8.440977
15 3 20 15 10 0.00168 55.493814 0.3213 9.861785
16 3 40 5 5 0.00239252.424776 0.4052 7.846611
17 3 40 10 10 0.00207653.655453 0.3418 9.324558
18 3 40 15 15 0.00176 55.089746 0.322775 9.822002
19 4.5 10 5 15 0.00061364.250790 0.16677 15.55764
20 4.5 10 10 5 0.00101359.887811 0.28376 10.94097
21 4.5 10 15 10 0.000732662.702661 0.2837 10.94281
22 4.5 20 5 5 0.00173 55.239077 0.29918 10.48134
23 4.5 20 10 10 0.00158256.015870 0.27819 11.11316
24 4.5 20 15 15 0.00099860.017389 0.246 12.18129
25 4.5 40 5 10 0.00167255.535274 0.3222 9.837489
26 4.5 40 10 15 0.00103259.726406 0.31751 9.964851
27 4.5 40 15 5 0.00186 54.609741 0.354 9.019934

V. C. UVARAJA, N. NATARAJAN
Copyright JMMCE
763
5.1. Results of Statistical Analysis of
Experiments Table 5. Response for signal to noise ratio—smaller is bet-
ter (wear rate).
Level Speed
(m/s)
Load
(N)
Time
(min)
Percentage of
Reinforcement %
1 54.17 59.36 56.45 54.37
2 56.70 55.85 56.44 56.17
3 58.67 54.33 56.65 59.00
Delta4.50 5.03 0.20 4.63
Rank 3 1 4 2
The investigational results and calculated values were
obtained based on the plan of experiment and then the
results were analyzed with the help of commercial soft-
ware MINITAB 14 as specially utilized for the design of
experiment and statistical analysis of experiment appli-
ances. The influence of controlled process parameters
such as sliding speed, applied load, sliding time and per-
centage of reinforcement has been analyzed and the rank
of involved factors like wear rate and coefficient of fric-
tion which supports signal to noise response is given in
Tables 5 and 6.
Table 6. Response for signal to noise ratio—smaller is bet-
ter (coefficient of friction).
Level Speed
(m/s)
Load
(N)
Time
(min)
Percentage of
Reinforcement %
1 9.038 10.645 10.181 8.833
2 9.531 10.013 9.717 9.937
3 11.116 9.027 9.787 10.915
Delta 2.077 1.618 0.463 2.082
Rank 2 3 4 1
It is evident from the tables that, among these parame-
ters, load is a dominant factor on the wear rate and per-
centage of reinforcement for coefficient of friction. The
influence of controlled process parameters on wear rate
and coefficient of friction are graphically represented in
Figures 3-6.
Figure 3. Main effects plot for means—wear rate.
Figure 4. Main effects plot for S/N ratios—wear rate.
© 2012 SciRes.

V. C. UVARAJA, N. NATARAJAN
764
Figure 5. Main effects plot for means—coefficient of friction.
Figure 6. Main effects plot for S/N ratios—coefficient of friction.
Based on the analysis of these experimental results
with the help of signal to noise ratio, the optimum condi-
tions resulting in wear rate and coefficient of friction are
shown in Figures 4 and 6. The figures clearly indicate
that the third level of sliding speed, first level of load and
third level of both sliding time and percentages of rein-
forcement are the optimum points, but these optimum
conditions are not available in L27 orthogonal array.
Hence the optimum conditions tested separately and the
results are given in Table 7.
5.2. Analysis of Variance for Wear Rate
Table 8 shows the results of the analysis of variance on
the wear rate for SiC and B4C particulates reinforced
Al-7075 alloy matrix composite. This analysis is carried
out at a level of 5% significance that is up to a confi-
dence level of 95%. The last column of the table indi-
cates the percentage of contribution (Pr) of each factor on
the total variation indicating the degree of their influence
on the results.
From Table 8, one can easily observe that the per-
centage of reinforcement factor has grater influence on
wear rate (Pr-R = 30.99%). Hence the percentage of re-
inforcement is an important control process parameter to
be taken into account while wear process. Percentage of
reinforcement is further followed by applied load (Pr-L =
29.96%), sliding speed (Pr-S = 26.89%). Based on the
interaction terms, the interaction between sliding speed
and load alone have significance influence (Pr-S*L =
2.45%) on wear rate of the hybrid metal matrix compos-
ites.
5.3. Analysis of Variance for Coefficient of
Friction
From Table 9, one can clearly infer that the percentage
of reinforcement factor has greater control on coefficient

V. C. UVARAJA, N. NATARAJAN 765
of friction (Pr-R = 34.16%) than the other factors. Hence
added percentage of reinforcement is an important pa-
rameter to be taken into account while considering coef-
ficient of friction. This parameter is then followed by
sliding speed (Pr-S = 33.31%), load (Pr-L = 18.89%),
and sliding time (Pr-T = 0.18%). Based on the interaction
terms, the interaction between load and sliding speed
alone have significance influence (Pr-L*S = 1.59%) on
coefficient of friction of the hybrid metal matrix compos-
ites.
Table 7. Optimum level process parameters for wear rate and coefficient of friction.
Ex.
No
Speed
(m/s)
Load
(N)
Time
(min)
Percentage of
Reinforcement %
Wear Rate
mm3/m
S/N Ratio for
Wear (db)
Coefficient
of Friction
S/N Ratio for Coefficient of
Friction (db)
1 4.5 10 5 15 0.000613 64.25079 0.1667 15.55764
Table 8. Analysis of variance for wear rate (mm3/m).
Source DF Seq SS Adj SS Adj MS F P Pr%
S (m/sec) 2 0.0000027 0.0000027 0.0000014 34.67702069 0.000504817 26.88556
L (N) 2 0.000003 0.000003 0.0000015 37.15395074 0.000417041 29.96494
T (Min) 2 0 0 0 0 1 –0.82882
R (%) 2 0.0000031 0.0000031 0.0000015 37.15395075 0.000417041 30.99139
S (m/sec)*L (N) 4 0.0000004 0.0000004 0.0000001 2.476930049 0.153914958 2.44820
L (N)*R (%) 4 0.0000002 0.0000002 0 0 1 0.39529
R (%)*S (m/sec) 4 0.0000001 0.0000001 0 0 1 –0.63117
Error 6 0.0000036 10.77459
Total 26 0.0000131 100
Table 9. Analysis of variance for coefficient of friction.
Source DF Seq SS Adj SS Adj MS F P Pr%
S (m/sec) 2 0.0247533 0.0247533 0.0123766 41.6568802 0.000303179 33.3057555
L (N) 2 0.0142994 0.0142994 0.0071497 24.06420125 0.001362003 18.89398901
T (Min) 2 0.0007243 0.0007243 0.0003622 1.21891135 0.359552708 0.179330236
R (%) 2 0.0253694 0.0253694 0.0126847 42.69370373 0.000283005 34.15511214
S (m/sec)*L (N) 4 0.0023454 0.0023454 0.0005863 1.973515588 0.21774874 1.594990663
L (N)*R (%) 4 0.0013486 0.0013486 0.0003371 1.134767256 0.422936085 0.220800281
R (%)*S(m/sec) 4 0.0019142 0.0019142 0.0005785 1.610686253 0.28629882 1.00053752
Error 6 0.0017827 10.64948465
Total 26 0.0725373 100

V. C. UVARAJA, N. NATARAJAN
766
5.4. Multiple Linear Regression Models
Statistical software MINITAB R14 is used for develop-
ing a multiple linear regression equation. This developed
model gives the relationship between independent/pre-
dictor variable and a response variable by fitting a linear
equation to the measured data.
The regression equation developed for wear rate is,
Wr = 0.00268 – 0.000258 S (m/s) + 0.000025 L (N)
– 0.000006 T (min) – 0.000082 R (%) (2)
R-Sq = 86.5%
The regression equation developed for coefficient of
friction is,
Cf = 0.419 – 0.0239 S (m/s) + 0.00185 L (N)
+ 0.000925 T (min) – 0.00745 R (%) (3)
R-Sq = 86.6%
5.5. Scanning Electron Microscope Examination
Wear rate depends on the presence of carbide phase in
matrix. Plates 7-9 show the SEM worn surface micro-
graphs of reinforced samples. The pure Al-7075 has a
smooth surface nature and more tribolayers are formed
hence the wear rate is more in the unreinforced sample.
The examination of hybrid composite sample worn sur-
faces as shown in Plates 7-9 showed that rough surface
nature and less tribolayers due to silicon carbides and
boron carbide embedded in matrix as compared to the
unreinforced alloy.
6. Confirmation Experiment
Confirmation test is the last step in the plan process. Table
10 indicates the values used for conducting the dry sliding
wear test and Table 11 shows the results of confirmation
experiment and their comparison with regression model
which helps to identify the optical parameter values from
the experimental analysis. The mathematical model was
developed with the help of regression Equations (2) and
Plate 7. SEM texture of 5% SiC + 3% B4C particulate rein-
forced Al (7075) composite after wear.
Plate 8. SEM texture of 10% SiC + 3% B4C particulate
reinforced Al (7075) composite after wear.
Plate 9. SEM texture of 15% SiC + 3% B4C particulate
reinforced Al (7075) composite after wear.
Table 10. Confirmation experiment for wear rate and coef-
ficient of friction.
Level Speed
(m/s)
Load
(N)
Time
(min)
Percentage of
Reinforcement %
1 1.8 15 6 5
2 3.3 25 9 10
3 4.2 35 12 15
(3) and also the comparison result values obtained ex-
perimentally were analyzed. From the analysis, the actual
wear rate and coefficient of friction are found to be
varying from the calculated one using regression equa-
tion and the error percentage ranges between 6.90% to
11.76% for wear rate and 4.66% to 9.23% for coefficient
of friction. As these values are closely resembling the
actual data with minimum error, design of experiments
by Taguchi method was successful for calculating wear
rate and coefficient of friction from the regression equa-
tion. Figure 7 shows a comparison between wear rate
and coefficient of the obtained contribution percentage
(Pr%) of each factor with the source of variance.

V. C. UVARAJA, N. NATARAJAN 767
Table 11. Result of confirmation experiment an their comparison with regression model.
Exp. Exp. wear rate Reg. model Equation (1), % Error Exp. coefficient Reg. model Equation (2), % Error
d
No (mm3/m) Wear Rate (mm3/m) of friction coefficient of friction
1 0 9.37 9.23 .0023455490.0021446 0.4064 0.37203
2 0.001688592 0.0015796 6.90 0.3473 0.32021 8.47
3 0.001306921 0.0011694 11.76 0.2959 0.28272 4.66
SpeedLoadTime %
0
10
20
30
Reinfo rcement
40
Percentage of contribution
Wear rate
Fr ic tio n C o-e ffic ie nt
contribution
7. Conclusions
s used to find the optimum conditions
ar rate and coefficient of friction were
ob
different parameters
in
inated by different pa-
ra
, 5 min slid-
in
r
fa
sliding speed and the
ap
led error obtained with the help of A
w
The multiple regression values obtained for wear rate and
to
xpress their deep sense of
Kumar, Assistant Professor,
ERENCES
[1] S. J. Harris, “posites,” Materials
Science and T 3, 1988, pp. 231-
Figure 7. Wear and friction coefficient ofper-
centage (Pr%).
Taguchi’s method i
for dry sliding wear of the hybrid metal matrix composite
materials. The following are the conclusions drawn from
the present study.
1) Optimum we
tained from the experiment.
2) The wear rate is dominated by
the order of percentage of reinforcement, applied load,
sliding speed, and sliding time. ANOVA test concluded
that as percentage of reinforcement increases the wear
rate also decreases significantly.
3) Coefficient of friction is dom
meters in the order of percentage of reinforcement,
sliding speed, applied load, and sliding time.
4) 4.5 m/s sliding speed, 10 N applied load
g time and 15% of reinforcement are the optimum con-
ditions for both wear rate and the coefficient of friction.
5) Percentage of reinforcement (30.99%) is the wea
ctor that has the highest physical properties as well as
statistical influence on the dry sliding wear rate of the
composites among the other factors such as applied load
(29.96%), sliding speed (29.89%).
6) The interactions between the
plied load will contribute more (2.45%) than other
interactions.
7) The poonova
ith respect to the parameters for wear rate and coeffi-
cient of friction are 10.78% and 10.65% respectively.
coefficient of friction are 0.865 and 0.866 respectively.
8) From confirmation tests, the errors associated with
wear rate ranges between 6.90% to 11.76 % and 4.66 %
9.23 % for coefficient of friction resulting in the conclu-
sion that the design of experiments by Taguchi method
was successful for calculating wear rate and coefficient
of friction from the regression equation.
8. Acknowledgements
The authors would like to e
gratitude to Prof. P. Suresh
Department of Mechanical Engineering at Bannari Am-
man Institute of Technology, Sathyamagalam, for his
valuable support and guidance. They also extend their
gratefulness to Mr. S. Sudhakar, Assistant Manager,
BEML Ltd., Mysore, for his constant guidance and dis-
cussion throughout the research work. The authors also
would like to thank their M.E., Students for all their sup-
ports. The authors would like to extend their gratitude to
Bannari Amman Institute of Technology for providing
infrastructural support.
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