Fax:08251-236444. Email: srk9060@yahoo.co.in 1

Analysis of Abrasive Wear Behavior of Carbon Fabric

Reinforced Epoxy Composite using Taguchi Method

Sudarshan Rao K a *, Y S Varadarajan b and N Rajendra c

a. Research scholar, NIE, Mysore, Associate Professor,

Department of Mechanical Engineering, Vivekananda College

of Engineering & Technology, Puttur. Karnataka India.

Mobile:09448252890. Email: srk9060@yahoo.co.in

b. Professor & Head, Department of Industrial & Production

Engineering, NIE, Mysore.Karnataka India.

c. Professor , Department of Mechanical Engineering,

Vivekananda College of Engineering & Technology, Puttur.

Karnataka India.

ABSTRACT ⎯ An experimental investigation was carried out

to study the effect of the filler weight fraction, normal load, and

sliding distance on the abrasive wear behavior of

Carbon/Epoxy composite. In this study, comparative abrasive

wear performance of carbon fabric reinforced Epoxy composite

filled with varying weight fraction of graphite fillers has been

reported. Wear studies were carried out using rubber wheel

abrasion test (RWAT) rig with Silica sand particles of size 200

µm used as dry and loose abrasives. Weight loss of the

composites during abrasion has been examined as a function of

sliding distance and normal load. Investigations showed that

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abrasive wear of the composites depend on the applied load, as

well as on the weight fraction of fillers. A plan of experiments,

based on techniques of Taguchi, was performed to acquire data

in controlled way. An orthogonal array and the analysis of

variance were employed to investigate the influence of process

parameters on the wear of these composites. The objective is to

establish a correlation between abrasive wear of composites

and wear parameters. These correlations were obtained by

multiple regressions. The results showed that, the weight loss

increases with increasing applied load, sliding distance and

weight fraction of graphite filler. Finally, confirmation tests

were conducted to verify the experimental results foreseen from

the mentioned correlations.

Keywords ⎯ Composite, Fabric, Abrasive wear, silica, epoxy,

orthogonal array.

1. INTRODUCTION

Polymer matrix composites are emerging as promising

materials in many structural and tribological applications.

Because of the high strength and stiffness to weight ratios, easy

processibality and chemical resistance, the composites are

finding a wide variety of structural applications in aerospace,

automotive, and chemical Industries. Polymer Matrix

Composites are also used increasingly in applications where

friction and wear are important parameters like gears, seals,

bearings, breaks etc. Furthermore, PMCs generally have a low

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coefficient of friction even under dry sliding conditions due to

the self-lubricating characteristics & enhanced mechanical and

tribological properties provided by the unique combination of

fibers and the matrices. Polymeric composites have steadily

gained importance in recent years for Industrial applications.

The increasing the use of composites demand for better

understanding of their behavior under different working

environments. The epoxy resin is a thermo set polymer, used

as matrix material for producing composites in structural

applications. Epoxy resins possess favorable properties such as

strong adhesion to many materials, good mechanical and

electrical properties, relatively high chemical & thermal

resistance and low priced compared to advanced polymers;

however, it is relatively brittle due to its cross linked molecular

structure. Mechanical and tribological properties of the epoxy

based composites can be improved by incorporating the right

kind of reinforcements and fillers. Among the various types of

reinforcements like particulate, short, long, and bidirectional

woven fabric, bidirectional woven fabric reinforcement is the

most promising for fiber reinforced composites. Woven fabric

reinforced composites are getting acceptance in many

engineering applications because of their balanced properties in

the fabric plane as well as their ease of handling during

fabrication. They provide better resistance to impact than

unidirectional composites and display behavior that is closer to

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that of a fully isotropic material. Modification of woven fabric

reinforced composites by incorporation of fillers has been a

popular research activity in the plastics industry since the

properties of resultant materials may be significantly changed

by the introduction of fillers and fabrics. Carbon fiber is one of

the most useful reinforcement materials in composites, its

major use being the manufacture of components in the

aerospace, automotive, and leisure industries. The unique

features of carbon fiber are low density, high strength,

lightweight, high modulus and high stiffness leading to the

development of new industrial applications.

Abrasive wear can be defined as; the hard asperities on one

surface move across a softer surface under load, penetrate and

remove material from the softer surface, leaving grooves [1].

Abrasive wear can be classified as two-body or three-body.

Two-body abrasive wear occurs when a rough surface or fixed

abrasive particles slide across a surface to remove material;

three-body abrasive wear, where the particles are loose and

may move relative to one another, and possibly rotate, during

sliding across the wearing surface. Most of the abrasive wear

problems which arise in engineering and agricultural machine

components involve three-body wear, while two-body abrasion

occurs primarily in material removal operations. Three-body

abrasion is often of considerable practical importance but

appears to have received much less attention than a two-body

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problem. The data regarding three-body abrasive wear

investigations of polymer composites are limited. Very little

has been reported on the effect of filler or fiber reinforcement

on three-body abrasive wear performance of polymer

composites [2, 3]. Hence, a fundamental and comprehensive

understanding of the three-body abrasive wear behavior of

these composites is required.

Feng Hua Su et al. [4] studied the influence of nano Al2O3 and

Si3N4 particulate filler in Carbon fabric / phenolic resin

composites on tribological properties, and concluded that, filled

composites improved the friction and wear behavior of Carbon

fabric composites. Particulates increase the interfacial bonding

strength, which increases mechanical strength. Nano

particulates improve wear resistance of Carbon fabric

composites at elevated temperature. Wear rate of filled Carbon

Fiber Composite is less than the unfilled. M Savitha et al. [5]

investigated the friction and wear behavior of glass- Vinyl ester

composite with and without SiC fillers. They concluded that

SiC filled composites have high wear resistance compared to

plain Glass –Vinyl ester composites. Thomas Larsen et al. [6]

studied the friction and wear properties of glass /epoxy and

carbon aramid /epoxy composite and found that, coefficient of

friction decreased by replacing carbon aramid with glass fiber,

and wear rate of glass /epoxy composite is more than the

carbon aramid /epoxy composites. Suresha et al. [7]

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investigated the friction and wear behavior of glass-epoxy

composite with and without graphite filler. Neat glass-epoxy

composite and graphite filled glass-epoxy composite with three

different percentages of filler were fabricated through hand

layup technique. They concluded that the graphite filled glass

epoxy composite showed higher resistance to sliding wear as

compared to plain glass-epoxy composites. B.Shivamurthy et al.

[8] investigated the friction and wear behavior of glass epoxy

composite filled with SiO2 fillers. They observed that

composites filled with 6 and 9 weight % of SiO2 particulates

exhibited steady wear rate and high wear resistance. S R

Chauhan et al. [9] studied the tribological behavior of glass

fiber reinforced vinylester composites filled with fly ash

particulates using a pin on disc wear apparatus. Orthogonal

array and analysis of variance (ANOVA) were used to

investigate the influence of process parameters on the

tribological properties. The results revealed that the inclusion

of fly ash decreased the coefficient of friction and increased the

wear resistance of the composites significantly. Also they

concluded that the factorial design of experiment can be

successfully employed to describe the frictional and wear

behavior of composites and developed linear equations for

predicting wear rate with selected experimental conditions. S

Basavarajappa et al. [10] studied the tribological behavior of

glass epoxy polymer composites with SiC and Graphite

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particles using a pin on disc wear test rig under sliding

conditions. The results showed that the inclusion of SiC and

Graphite particles will increase the wear resistance of the

composite greatly. They also developed a mathematical model

using Design of experiments approach by Taguchi method.

Biswas et. al. [11] conducted study on the erosive wear

behavior of red mud filled glass epoxy composites, and used

Taguchi method for analysis of the interaction of control

factors for the wear rate. They found that the filler content in

the composites, erodent temperature, the impingement angle

and velocity are found to have substantial influence in

determining the rate of material loss from the composite

surface due to erosion. Amar Patnaik et. al. [12], investigated

the three body abrasive wear properties of glass epoxy

composite, filled with SiC, Al2O3, and pine bark dust

particulates. They fond that, abrasive wear rate decreases with

increase in abrading distance for all the samples, the pine bark-

filled composite showed better abrasive wear resistance, and

wear rate was higher in SiC-filled glass fiber-reinforced epoxy

composites.

Wealth of information is available in the literature on the

tribological behavior of various fiber reinforced polymer matrix

composites. The tribological properties are not the intrinsic

material properties of a particular tribological system, but, there

are many parameters that can influence them. A number of

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studies have been reported on the effect of normal load, sliding

distance, fiber fraction, orientation, etc., on the wear rate of

polymer composites. The knowledge of the relations between

material parameters and tribological performance is important

in the determination of strength and wear rate of the composite.

The design of experimental approach by Taguchi technique has

been successfully used by researchers in study of sliding wear

behavior of polymer composites. Taguchi parameter design can

optimize the performance characteristics through the setting of

design parameters and reduce the sensitivity of the system

performance to the source of variation [13]. This is carried out

by the efficient use of experimental runs to the combinations of

variables to be studied. This technique is a powerful tool for

acquiring the data in a controlled way and to analyze the

influence of process parameters over some specific parameters,

which is unknown function of these process variables. Taguchi

technique creates a standard orthogonal array to consider the

effect of several factors on the target value and defines the plan

of experiments. The experimental results are analyzed by using

analysis of means and variance of the influence of factors.

Considering these aspects, it was decided to study the three

body abrasive wear characteristics of graphite filled carbon

fiber epoxy composites. The present investigation focuses on

the wear characteristics of bi-directional woven carbon fiber

reinforced epoxy composites filled with graphite particles. An

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inexpensive and easy to operate experimental strategy based on

Taguchi experimental design technique is used in this study to

determine the relative significance of various control factors

influencing the wear rate.

2. EXPERIMENTAL

2.1 Materials

In this investigation, composites were fabricated using

bidirectional plain-woven carbon fabric (density 200 g/m2),

containing Polyacryl nitrile (PAN) based carbon fiber, supplied

by CS Interglass AG, BenzstraBe, as reinforcement. The matrix

system used is a medium viscosity epoxy resin (LAPOX -12),

and a room temperature curing polyamine hardener (K5), both

supplied by ATUL India Ltd, Gujarat, India.. The fillers that

have been used are graphite particulates supplied by Luba

Chemie, Bombay.

2.2 Fabrication

The matrix was prepared by mixing epoxy resin,

LAPOX -12 and hardener K5 in the ratio of 100:27 by weight

at 60°C. The composite laminates of 300 mm x 300 mm x 3

mm were fabricated by compression molding method [14].

Calculated amount of graphite filler is mixed with the resin,

then it was coated on 16 layers of carbon fabric using brush and

roller and it was kept between the pressing plates of 350 mm x

350 mm size. A layer of polyester film was provided in

between the plate and composite surface for easy release and to

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obtain smooth and uniform surface on the composites. Resin

impregnated stock of 16 layers of fabrics were allowed to cure

for a day at room temperature, then was pressed in hydraulic

press under a pressure of 0.5 MPa and temperature 140°C for

about 2 hours. Three types of samples were prepared based on

the weight fraction of graphite filler in the composite. The

details of the composites fabricated are shown in Table 1.

Table 1: Details 0f Composite Samples Prepared.

Sample Matrix Reinforcement Filler

A 38 % Epoxy 60% Carbon Fabric2 %

Graphite

B 36 % Epoxy 60% Carbon Fabric4 %

Graphite

C 34 % Epoxy 60% Carbon Fabric6 %

Graphite

2.3 Experimental setup

The dry sand rubber wheel abrasion test setup as per

ASTM G65 is used to conduct the wear studies. The schematic

diagram of the set up is as shown in the figure 1. The abrasives

are introduced between the test specimen and the rotating

wheel with a chlorobutyl rubber tire. The test specimen is

pressed against the rotating wheel at a specified force by means

of lever arm while a controlled flow of grits abrade the test

surface. The rotation of wheel is such that its contact face

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moves in the direction of grit flow. The pivot axis of the lever

arm lies within a plane, which is approximately tangential to

the rubber wheel surface and normal to the horizontal diameter

along which the load is applied. The tests were carried out for

different loads and sliding distances.

Figure 1. Dry sand rubber wheel abrasion test setup

2.4 Test procedure

The test samples were prepared by cutting the

composite laminates into 25mm X 75mm X 3mm size pieces.

The samples were cleaned, dried and its initial weight was

determined in a high precision digital electronic balance

(0.0001 gm accuracy) before it was mounted in the sample

holder. The silica sand was used as abrasives in the present

experiments. The abrasive was fed at the contacting face

between the rotating rubber wheel and the test sample. The

tests were conducted at a rotational speed of 200 rpm with 200

µm size Silica sand particle at the feeding rate of 250 gm/min.

The experiments were carried out at a normal load of 11 N,

12

23N, and 35N, and the abrading distances chosen were 300m,

600m, and 900m. The wear was measured by the loss in weight.

2.5 Taguchi technique

A plan of experiments, based on the Taguchi technique, was

performed to acquire data in a controlled way. An orthogonal

array and analysis of variance (ANOVA) were applied to

investigate the influence of process parameters on the wear

behavior of composites. The Taguchi design of experiment

approach eliminates the need for repeated experiments and thus

saves time, material and cost. Taguchi approach identifies not

only the significant control factors but also their interactions

influencing the wear rate predominantly. The most important

stage in the design of experiment lies in the selection of the

control factors. In the present work, the impacts of three such

parameters are studied using L27 (313) orthogonal array. The

operating conditions under which sliding wear tests carried out

are given in Table 2.

Table 2: Levels of Variables used in the Experiments

Control factors Units Level

I II III

Filler Content (A) % 2 4 6

Normal Load (B) N 11 23 35

Sliding distance (C) m 300 600 900

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Three parameters are percentage of filler content, normal load

and sliding distance and each at three levels are considered in

this study. Three parameters each at three levels would require

Taguchi’s factorial experiment approach to 27 runs only

offering a great advantage. The experimental observations are

transformed into a signal-to-noise (S/N) ratio. There are several

S/N ratios available depending on the type of characteristics.

The S/N ratio for minimum wear rate coming under smaller is

better characteristic, which can be calculated as logarithmic

transformation of the loss function by the equation:

S/N = - 10 log 1/n (∑ yn2) (1)

Where ‘n’ is the repeated number trial conditions and y1,

y2….yn are the response of the wear rate characteristics.

The plan of the experiments is as follows: The first column was

assigned to filler content (A), the second column to normal

load (B), the fifth column to sliding distance (C), the third and

fourth column are assigned to (A x B)1 and (A x B)2,

respectively to estimate interaction filler content (A) and

normal load (B), the sixth and seventh column are assigned to

(A x C)1 and (Ax C)2, respectively, to estimate interaction

between the filler content (A) and sliding distance (C), the

eighth and eleventh column are assigned to (B x C)1 and (B x

C)2, respectively, to estimate interaction between the normal

load (B), and sliding distance (C). The remaining columns are

assigned to error columns, respectively.

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3. RESULTS AND DISCUSSIONS

3.1 Analysis of Experimental Results

The experimental data for wear rate of the composite is

reported in the Table 3. From Table 3 the overall mean for the

S/N ratio of the wear rate is found to be 9.53197 db. The

analyses of the experimental data are carried out using the

software MINITAB 16 specially used for design of experiment

applications. Before analyzing the experimental data using this

method for predicting the measure of performance, the possible

interactions between control factors are considered. Thus

factorial design incorporate a simple means of testing for the

presence of the interaction effects. The mean response refers to

the average values of the performance characteristics at

different levels.

Table 3: Results of Dry Abrasive Wear Tests as per Orthogonal

Array L27 (313)

S. No. Filler  

Content  % of Graphite 

Normal Load  N 

Sliding Distance 

m Wear  S/N 

1  2  11 300 0.0788  22.06948 

2  2  11 600 0.1646 15.6714 

3  2  11 900 0.1909  14.38388 

4  2  23 300 0.275  11.21335 

5  2  23 600 0.3301 9.62709 

6  2  23 900 0.4872  6.245854 

7  2  35 300 0.4502  6.93189 

8  2  35 600 0.6888 3.238137 

9  2  35 900 0.7192  2.863006 

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In order to find out statistical significance of various factors

like filler content (A), normal load (B) and sliding distance

(C), and their interactions on wear rate, analysis of variance

(ANOVA) is performed on experimental data. Table 4 show the

results of the ANOVA with the wear rate. The last column of

the table indicates p-value for the individual control factors and

their possible interactions. It is known that smaller the p-value,

greater the significance of the factor/interaction corresponding

to it [13].

10  4  11 300 0.0829  21.62891 

11  4  11 600 0.1428 16.90544 

12  4  11 900 0.245  12.21668 

13  4  23 300 0.2531  11.93416 

14  4  23 600 0.4312 7.306425 

15  4  23 900 0.5972  4.477604 

16  4  35 300 0.4051  7.848755 

17  4  35 600 0.6116 4.27065 

18  4  35 900 0.8326  1.591272 

19  6  11 300 0.0821  21.71314 

20  6  11 600 0.1675  15.5197 

21  6  11 900 0.2221  13.06903 

22  6  23 300 0.3092 10.19521 

23  6  23 600 0.6823  3.320493 

24  6  23 900 0.6388  3.892702 

25  6  35 300 0.5959 4.496532 

26  6  35 600 0.6942  3.170308 

27  6  35 900 0.8354  1.562111 

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Table 4. ANOVA table for S/N ratio

Source DF Seq SS Adj SS

Adj MS F P

A 2 13.97 13.967 6.983 5.55 0.031 B 2 814.65 814.64 407.32 323.57 0.000 C 2 192.76 192.76 96.38 76.56 0.000 A*B 4 6.62 6.616 1.654 1.31 0.343 A*C 4 5.74 5.74 1.435 1.14 0.404 B*C 4 13.77 13.77 3.442 2.73 0.105 Residual Error

8 10.07 10.07 1.259

Total 26 1057.5

The ANOVA table for S/N ratio (Table 4), indicate that, the

normal load (p=0.000), sliding distance (p= 0.000) and filler

content (p=0.031) in this order, are significant control factors

affecting the wear rate. While between the interactions, the

interaction of normal load and sliding distance (p=0.105) has

greater contribution on the wear rate compared to the

interaction of filler content and normal load (p=0.343), and the

interaction of filler content and sliding distance (p=0.404).

Table 5. Response Table

Level A B C 1 10.249 17.02 13.115 2 9.798 7.579 8.781 3 8.549 3.997 6.7 Delta 1.701 13.023 6.414 Rank 3 1 2

The response table shows the average of each S/N ratios for

each level of each factor. The table includes ranks based on

Delta statistics, which compare the relative magnitude of

17

effects. The Delta statistic is the highest minus the lowest

average for each factor. Minitab assigns ranks based on Delta

values; rank one to the highest Delta value, rank two to the

second highest, and so on. From the response table 5, it is clear

that, first rank to the normal load, followed by the sliding

distance and filler content.

From both ANOVA and response tables it is clear that, the

normal load is the most significant factor and the filler content

has less influence on the performance output.

Figure 2, show graphically the effect of the three control factors

on wear rate of the composite specimens. A main effect is seen

when different levels of a factor affect the response differently.

A main effects plot graphs the response mean for each factor

level connected by a line. When the line is horizontal, then

there is no main effect present. Each level of the factor affects

the response in the same way, and the response mean is the

same across all factor levels. When the line is not horizontal,

then there is a main effect present. Different levels of the factor

affect the response differently. The steeper the slope of the line,

the greater the magnitude of the main effect on the wear rate.

For each control factors, a level with maximum value of mean

of S/N ratio will give minimum wear rate. In this case, the

analysis of results leads to the conclusion that, factors

combination A1, B1 and C1 gives minimum wear rate. That is,

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2% graphite filler, with 11 N load and 300m sliding distance

will lead to a minimum wear rate.

When the effect of one factor depends on the level of the other

factor, interaction plot can be used to visualize possible

interactions. Parallel lines in an interaction plot indicate no

interaction. The greater the difference in slope between the

lines, the higher the degree of interaction. The interaction graph

is shown in Figures 3. From the Figure it is observed that the

interaction A x B, i.e., percent filler and normal load shows

significant effect on the wear rate of the composite samples.

642

15

10

5

352311

900600300

15

10

5

% Filler

Mea

n of

SN

rati

os

Load

Distance

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Smaller is better

Figure 2. Effect of control factors on wear rate.

20

10

0

900600300

352311

20

10

0

642

20

10

0

% Filler

Load

Distance

246

% Filler

112335

Load

300600900

Distance

Interaction Plot for SN ratiosData Means

Signal-to-noise: Smaller is better

Figure 3. Interaction of control factors on wear rate.

19

3.2 Confirmation test

The confirmation experiment is the final step in the design of

experiments process. The confirmation experiment is

conducted to validate the inference drawn during the analysis

phase. The confirmation experiment is performed by

considering the new set of factor setting A1 , B1, C1 to predict the

wear rate.

The predicted value of the S/N ratio at the optimum level η is

calculated as [15]:

η = ηm + ∑ ηı ηm) (2)

Where ηm is the total mean S/N ratio, η  is the mean S/N ratio

at the optimal level, and o is the number of main design

parameters that significantly affect the wear performance of the

composites. A new combination of factor levels A1 B1 and C1

are used to predict the S/N ratio of wear predictive equation

and is found to be 21.732. Table 6 shows the comparison of the

predicted S/N ratio with the actual (experimental) S/N ratio

using the optimal parameters and there seems to be quite a

good agreement between the two.

The resulting equation seems to be capable of predicting the

wear rate to the acceptable level of accuracy. An error of 1.5%

for the S/N ratio of the wear rate is observed. However if

number of observations of performance characteristics are

increased further these errors can be reduced. This validates the

20

statistical approach used for predicting the measures of

performance based on knowledge of the input parameters.

Table 6. Results of the confirmation experiments

Optimal control parameters

Prediction Experimental

Level A1,B1,C1 A1,B1,C1

S/N Ratio 21.732 22.0695

% Error 1.5%

3.3 Regression analysis

In order to establish the correlation between the wear

parameters like filler content, normal load and sliding distance

with the wear rate, linear regression model was used. The

generalized linear regression equation for the experiment can

be written as,

Y = a0 + a1 x A + a2 x B + a3 x C. (3)

The factorial design of experiments and the values of the

response variables corresponding to each set of trials are

represented in the above equation for the specimen. Where Y is

the wear weight loss. The variables A, B and C are the filler

content, normal load and sliding distance respectively. The a1,

a2 and a3 are the coefficients of the independent variables A, B

and C respectively. After calculating each of the coefficients of

equation, the final linear regression equation for the wear rate

of the composites are calculated. These constants are calculated

by using linear regression analysis method using the software

21

MINITAB 16. These coefficients are substituted in the equation

(3) and following relation is obtained as shown in equation (4).

Wear = - 0.401 + 0.0234 A+ 0.0206 B + 0.000414 C (4)

R2= 92.1%

The higher correlation coefficient (R2) confirms the suitability

of the used equations and correctness of the calculated

constants.

IV. CONCLUSION

In this study, the effect of graphite filler on tribologiical

properties of carbon reinforced epoxy resin composites has

been examined. According to obtained results, it can be

concluded that:

1. Design of experiments approach by Taguchi method

enabled us to analyze successfully the wear behavior of

the composites with filler material, load, and sliding

distance as test variables.

2. Factorial design of the experiment can be successfully

employed to describe the wear behavior of the samples

and to develop linear equations for predicting wear rate

with in selected experimental conditions.

3. Among the control factors, normal load has the highest

physical properties as well as statistical influence on the

abrasive wear of the composites (p=0.000), followed by

sliding distance (p= 0.000) and filler content (p=0.031).

The interaction of sliding distance and normal load

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shows significant effect on the wear rate and other

interactions will influence very less.

4. According to main effects plot, factors combination of

A1, B1 and C1 gives minimum wear rate. That is, 2%

graphite filler, with 11 N load and 300m sliding

distance will lead to a minimum wear rate.

5. Based on the interaction plot, it is observed that the

interaction A x B, i.e., percent filler and normal load

shows significant effect on the wear rate in all three

cases.

6. The coefficient of regression obtained with the

regression value (R2) of 92.1% shows that the

satisfactory correlation was obtained.

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