Study of Wear Behavior of Aluminum based MMCs using Taguchi Methods

International Journal of Research and Scientific Innovation (IJRSI) | Volume IV, Issue III, March 2017 | ISSN 2321–2705
www.rsisinternational.org Page 39
Study of Wear Behavior of Aluminum based MMCs
using Taguchi Methods
Dr. Arun Kumar M.B.
Department of Mechanical Engineering, D.Y. Patil College of Engineering, Pune, Maharashtra, India
Abstract: A composite material is a material system composed of
a mixture or combination of two or more micro or macro
constituents that differ in form and chemical composition and
which are essentially insoluble in each other. Stir casting method
is used to fabricate Metal Matrix composites (MMCs) having fly
ash in 3, 6, 9 wt% and e-glass fibre in 3 wt%. The dry sliding
wear tests were conducted on composite material using a pin-on-
disc wear testing setup with a normal pin load of 20N, 40N and
60N, and wear rate for each load is calculated for different
sliding speeds. Analytical modelling of composite material is
carried out by Taguchi’s design of experiments (DOE) using L27
orthogonal array. The experimental results were transformed
into a signal-to-noise (S/N) ratios. Analysis of Variance
(ANOVA) was used to determine the design parameters
significantly influencing the wear rate of the composite material.
Based on the experimental results, a multiple linear regression
model was developed. A regression equation thus generated
establishes correlation between the significant terms obtained
from ANOVA, namely, load, sliding speed, fly ash content and
their interactions. In order to validate the regression model,
confirmation wear tests were conducted with parameter levels
that were different from those used for analysis. The error
associated with the relationship between the experimental values
and the computed values of the regression model for hybrid
composites was around ±5%.
Index Terms—Composites, Metal Matrix, Stir Casting, Taguchi
Approach
I. INTRODUCTION
aterials are probably more deep-seated in our culture
than most of us realize. Transportation, housing,
clothing, communication, recreation, and food production
virtually every segment of our everyday lives is influenced to
one degree or another by materials. The development of many
technologies that make our existence so comfortable has been
intimately associated with the accessibility of suitable
materials. Many of our modern technologies require materials
with unusual combinations of properties that cannot be met by
the conventional metal alloys, ceramics, and polymeric
materials. This is especially true for materials that are needed
for aerospace, underwater, and transportation applications.
Frequently, strong materials are relatively dense; also,
increasing the strength or stiffness generally results in a
decrease in impact strength. Material property combinations
and ranges have been, and are yet being, extended by the
development of composite materials. Generally speaking, a
composite is considered to be any multiphase material that
exhibits a significant proportion of the property of both
constituent phases such that a better combination of properties
is realized. According to this principle of combined action,
better property combinations are fashioned by the judicious
combination of two or more distinct materials. Property trade-
offs are also made for many composites [1].
For metal-matrix composites (MMCs) the matrix is a
ductile metal. These materials may be utilized at higher
service temperatures than their base metal counterparts;
furthermore, the reinforcement may improve specific
stiffness, specific strength, abrasion resistance, creep
resistance, thermal conductivity, and dimensional stability.
Some of the advantages of these materials include higher
operating temperatures, non flammable and greater resistance
to degradation by organic fluids [2]. Aluminium alloy 6061 is
one of the most extensively used of the 6000 series aluminium
alloys as a matrix material in MMCs. It is a versatile heat
treatable extruded alloy with medium to high strength
capabilities. Typical properties of aluminium alloy 6061
include medium to high strength, good toughness, good
surface finish, excellent corrosion resistance to atmospheric
conditions, good corrosion resistance to sea water, it can be
anodized, good weldability and brazability and good
workability [3]. Typical applications for aluminium alloy
6061 include aircraft and aerospace components, marine
fittings, transport, bicycle frames, camera lenses, drive shafts,
electrical fittings and connectors, brake components, valves
and couplings [4].
Millions of tons of fly ash powder are generated in coal-
based power plants and only a small portion of it is being
utilized. This is a waste by-product and is being used as filler
in aluminium matrices. When fly ash particles are used as
filler in aluminium alloy casting, it reduces cost and density
and increases wear, seizure and abrasion resistance and
stiffness. Various components such as pistons, engine cover,
and connecting rod castings have been made out of cast
aluminium alloy fly ash composites. Aluminium fly ash
composites find potential application in automobile industries.
But, adequate research work in this area of composite material
has to be carried out [5].
II. COMPOSITE FABRICATION
The matrix material used in this research work is
aluminium alloy Al6061. The chemical composition of the
aluminium alloy Al6061 used for the fabrication of MMC is
reported in the table 1. For the fabrication of composite
M

material using stir casting method, cast billets of Al6061
aluminium alloy are used as raw material.
Table 1: Chemical composition of Al6061 (wt %)
Element Percentage
Mg 0.92 %
Si 0.76 %
Fe 0.28 %
Cu 0.22 %
Ti 0.10 %
Cr 0.07 %
Zn 0.06 %
Mn 0.04 %
Al Bal
The preference to use fly ash as a filler or reinforcement in
metal matrix composite is that fly ash is a byproduct of coal
combustion, available in very large quantities at very low
costs since much of this is currently land filled. The material
costs of composites can be reduced significantly by
incorporating fly ash into the matrix of metallic alloys. Fly ash
may turn out to be one of the lowest cost fillers in terms of the
cost per volume. The high electrical resistivity, low thermal
conductivity and low density of fly ash may be helpful for
making a lightweight insulating composite. Fly ash as a filler
in Al casting reduces cost, decreases density and increase
hardness, stiffness, wear and abrasion resistance. It also
improves the maintainability, damping capacity, coefficient of
friction etc. which are needed in various industrial
applications.
“Class F Fly ash” is used as one of the reinforcement in the
research work carried out. Fly ash particles with different size
distributions were supplied by Harihar Polyfibers and
Grasilene Division, Harihar. These particles are mostly
spherical in shape and had a particle size in the range of 3-
40µm. The chemical composition of the fly ash used for
composite fabrication is given in the table 2.
Table 2: Chemical composition of Class F Fly Ash
Chemical Compound Wt %
SiO 54.9
Al2O3 25.8
Fe2O3 06.9
CaO 08.7
MgO 01.8
SO3 00.6
Na2 & K2O 00.6
E-glass fibre is a second reinforcement phase used for the
fabrication of composite material. The e-glass fibres used are
short chopped fibres having an approximate length of 2 mm
and were supplied by Suntech Fibres Pvt. Ltd., Bangalore.
Figure 1: (a) and (b) Stir casting Setup
Casting of metal matrix composites is an attractive
processing method since it is relatively inexpensive and it
offers a wide variety of material and processing condition
options. Good wetting between the solid particulate phase and
the liquid metal matrix is an essential condition for the
generation of a satisfactory bond between these two during
casting. The mechanical properties of MMCs are controlled to
a large extent by the structure and properties of this
reinforcement-metal interface [6-9].
Figure 2: Stir casting process (a) Molten metal, (b) permanent moulds
Liquid metallurgy technique or stir casting method was
used to reinforce fine fly ash particles and e-glass fibre in the
aluminium 6061 matrix material. The reinforcing materials
[a] [b]
[a]
[b]

were introduced into the molten metal pool through a vortex
created in the melt by the use of alumina coated stainless steel
stirrer. The coating of alumina to the blades of the stirrer is
essential to prevent the migration of ferrous ions from the
stirrer into the molten metal. The stirrer was rotated at 550
rpm and the depth of immersion of the stirrer was maintained
about two-thirds the depth of the molten metal. The pre heated
reinforcing materials in measured quantity were added one
after the other into the vortex of the liquid melts which was
degassed using pure nitrogen for about 3-4 minutes. The
resulting mixture was tilt poured into the preheated permanent
metal moulds [16-17]. Figure 1(a) and figure 1(b) show the
images of the stir casting setup figure 2 (a) shows hot molten
aluminium 6061 at 800O
C and figure 2 (b) shows the metal
dies used for the fabrication purpose.
Composite specimens are fabricated with fly ash and e-
glass fibre in different proportions in the aluminium alloy
Al6061 matrix. Fly ash in 3-9 wt% in steps of 3 wt% and 3
wt% of e-glass fibre is reinforced in Al6061 matrix material,
resulting in 3 different composite specimens of Al6061 - Fly
ash - e-glass fibre metal matrix composites.
III. TESTING
Figure 3: Pin-on-disc wear testing setup
The adhesive wear resistance of Al6061 alloy and its
composites were studied by conducting dry sliding wear test.
The wear testing specimen is prepared according to ASTM
G99. A pin on disc wear testing configuration as shown in
figure 3, is used to check dry sliding wear properties of
composite specimen. The specimen is rubbed against rotating
hard steel disc without any lubricant. Tests were conducted
for different variables such as speed ranging from 200 to 500
rpm, normal load from 20 N to 60 N in steps of 20 N, and for
a sliding time of 5-10 minutes. Common track diameter of 120
mm is used to test the specimen. After testing, the specimen is
removed from the machine and weighed. The difference in
initial and final weight gives the loss of material in turn wear
rate of the material. The specimen is weighed using electronic
balance. Tests are conducted at normal room temperature.
IV. RESULTS AND DISCUSSION
The Taguchi technique is a powerful design of experiment
tool for acquiring the data in a controlled way and to analyze
the influence of process variable over some specific variable
which is an unknown function of these process variables and
for the design of high quality systems. This method was being
successfully used by many researchers in the study of the
wear behavior of aluminium metal matrix composites [7-12].
Taguchi creates a standard orthogonal array to accommodate
the effect of several factors on the target value and defines the
plan of experiment. 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 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.
In the present investigation, an L27 orthogonal array was
selected and it has 27 rows and 13 columns. 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
equal to the sum of the variables. Each variable and the
corresponding interactions were assigned to a column defined
by Taguchi method.
In the study, prediction of wear rate of the composite
materials is carried out by selecting load (L), sliding speed (S)
and wt% of fly ash (F) as control variables. The control
variables and their levels are shown in table 3.
Table 3: Factors and their levels
Symbol
Factor
Level1
Level2
Level3
L
Normal Load
N
20 40 60
S
Sliding Speed
m/s
1.05 1.83 2.62
F
Fly ash content
Wt%
3 6 9
The first column of the L27 orthogonal was assigned to load
(L), the second column to sliding speed (S), the fifth column
to fly ash content (F), and the remaining columns were
assigned to their interactions. The response variable to be
studied is wear rate. The experiments were conducted based
on the rank order generated by Taguchi model and the results
were obtained. The analysis of the experimental data was
carried out using MINITAB 15 software, which is specially
used in DOE applications. The experimental results were
transformed into a Signal-to-Noise (S/N) ratios. The S/N ratio
indicates the degree of the predictable performance of a

product or process in the presence of noise factors. The S/N
ratio for wear rate is calculated using „smaller the better‟
characteristic, which can be calculated as a logarithmic
transformation of the loss function, and is given in the
equation 1.
∑ (1)
Where, y is the observed data (wear rate) and n is the
number of observations. The above S/N ratio transformation is
suitable for minimization of wear rate.
Table 4: Response table for S/N ratios - smaller is better (wear
rate)
Level
Load
(N)
Speed
(m/s)
Fly ash
(Wt %)
1 4.869 5.195 6.334
2 5.786 6.006 5.801
3 6.86 6.315 5.38
Delta 1.992 1.12 0.953
Rank 1 2 3
The influence of control parameters such as load, sliding
speed and fly ash content on wear rate has been evaluated
using S/N ratio response analysis. The control parameter with
the strongest influence was determined by the difference
between the maximum and minimum value of the mean of
S/N ratios. Higher the difference between the mean of S/N
ratios, the more influential was the control parameter. The S/N
ratio response analysis, presented in Table 4 shows that
among all the factors, load was the most influential and
significant parameter followed by sliding speed and fly ash
content. Figure 4 shows the mean of wear rate graphically and
figure 5 depicts the main effects plot for means of S/N ratio
for wear rate. From the analysis of these results, it can be
inferred that parameter combination of L = 20 N, S = 1.05 m/s
and F = 9% gave the minimum wear rate for the range of
parameters tested.
604020
7.0
6.5
6.0
5.5
5.0
2.621.831.05
963
7.0
6.5
6.0
5.5
5.0
L
MeanofMeans
S
FA
Main Effects Plot for Means
Data Means
Figure 4: Main Effects plot for Means
604020
-14
-15
-16
-17
2.621.831.05
963
-14
-15
-16
-17
L
MeanofSNratios
S
FA
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Smaller is better
Figure 5: Main Effects plot for S-N Ratios
Analysis of Variance (ANOVA) was used to determine the
design parameters significantly influencing the wear rate
(response). This analysis was evaluated for a confidence level
of 95%, that is for significance level of α=0.05. It can be
observed from the results obtained that load was the most
significant parameter having the highest statistical influence
(61.38%) on the dry sliding wear of composites followed by
sliding speed (20.68%) and fly ash content (14.10%). When
the P-value for this model is less than 0.5, then the parameter
or interaction can be considered as statistically significant.
This is desirable as it demonstrates that the parameter or
interaction in the model has a significant effect on the
response. From an analysis of the results obtained, it is
observed that the load (L), sliding speed (S), fly ash content
(F), the interaction effect of load with fly ash content (L x F)
is significant model terms influencing wear rate of
composites, since they have obtained the P - value < 0.5.
Although the interaction effect of load with sliding speed (L x
S) exerts some influence on the dry sliding wear, it may be
considered statistically insignificant for its P-value is greater
than 0.5, and hence it is neglected. The coefficient of
determination (R2
) is defined as the ratio of the explained
variation to the total variation. It is a measure of the degree of
fit. When R2
approaches unity, a better response model results
and it fits the actual data. The value of R2
calculated for this
model was 0.988, i.e., very close to unity, and thus acceptable.
It demonstrates that 98.8% of the variability in the data can be
explained by this model. Thus, it is confirmed that this model
provides reasonably good explanation of the relationship
between the independent factors and the response [10-14].
A multiple linear regression analysis attempts to model the
relationship between two or more predictor variables and a
response variable by fitting a linear equation to the observed
data. Based on the experimental results, multiple linear
regression models were developed using MINITAB 15.
Regression equations thus generated establish correlation
between the significant terms obtained from ANOVA,
namely, load, sliding speed, fly ash content and their
interactions. The regression equations developed for wear rate
is:

W3% eg = 3.07 + 0.0436 L + 0.713 S - 0.0988 F - 0.00177 L x
F, for 3 wt% E-glass
(2)
Where,
W is the wear rate of the composite, in Kg/m x10-5
L is the load on the composite pin, in N
S is the sliding speed, in m/s
F is the fly ash content in composites, in wt%
The above equations can be used to predict the wear rate of
the hybrid composites. The constant in the equation is the
residue. The regression coefficient (R2
) obtained for the model
was 0.953 and this indicates that wear data was not scattered.
The coefficient associated with load (L) in the regression
equations is positive and it indicates that as the load increases,
the wear rate of the composite also increases. The coefficient
associated with sliding speed (S) in the regression equations is
also positive and this suggests that the wear rate of the
composite increases with increasing sliding speed. It can be
inferred from the negative value of the coefficient associated
with fly ash content (F) in the regression equations that as the
weight percentage of fly ash content increases, wear rate of
the composite reduces. The wear resistance of hybrid
composite has increased due to the increase in the weight
fraction of fly ash. The increased area fraction of hard fly ash
particles, improved the load carrying capacity and the
abrasion resistance of composites. 9 wt% fly ash reinforced
hybrid composite exhibit, the lowest wear rate at all loads and
sliding speeds. The other interaction effects indicated in the
equation was marginal.
Table 5: Parameters used for confirmation tests
Test No.
Load, N
(L)
Speed, m/s
(S)
Fly ash
wt% (F)
1 10 1.5 3
2 30 2.0 6
3 50 2.5 9
In order to validate the regression model, confirmation
wear tests were conducted with parameter levels that were
different from those used for analysis. The different parameter
levels chosen for the confirmation tests are shown in Table 5.
The results of the confirmation test were obtained and a
comparison was made between the experimental wear rate
values and the computed values obtained from the regression
model (Table 6). The error associated with the relationship
between the experimental values and the computed values of
the regression model for hybrid composites was very less
(around ±5 error). Hence, the regression model developed
demonstrates a feasible and effective way to predict the wear
rate of the hybrid composites.
Table 6: Confirmation test results
Test
No.
Experiment Model of equations
Error
(%)
1 4.5631 4.4213 3.1
2 5.6891 5.5609 2.3
3 6.7845 6.4389 5.1
V. CONCLUSION
Using the stir casting method, fly ash and e-glass fibre can
be successfully introduced in the Al6061 alloy matrix to
fabricate hybrid metal matrix composite material. The
reinforcement of Al6061 alloy with fly ash and e-glass fibre
has a marked effect on the wear rate. The wear rate decreases
with increasing content of reinforcements. The wear rate of
the hybrid composites is less when compared with the matrix
alloy and the individual composites. Design of experiment
technique is successfully used to determine dry sliding wear
of composite material and it was found that the sliding load
and wt% of fly ash are the major factor influencing the wear
of the composite material. Multiple linear regression models
were developed and it can be used to predict the wear rate of
the hybrid composites. A good agreement between the
predicted and actual wear rate was observed.
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