INVESTIGATION ON WEAR BEHAVIOUR OF LM13/SiC …LM 13 aluminium alloy is used as metal matrix and its chemical composition is given in Table 3. The reinforcement, SiC particulates details

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 6, June 2017, pp. 530–543, Article ID: IJMET_08_06_056

Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=6

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication Scopus Indexed

INVESTIGATION ON WEAR BEHAVIOUR OF

LM13/SiC ALUMINIUM METAL MATRIX

COMPOSITES BY RESPONSE SURFACE

METHODOLOGY

J. Eric David Praveen, D.S. Robinson Smart, R. Babu and A. Vijin Prabhu

Department of Mechanical Engineering, Karunya University,

Coimbatore, Tamil Nadu, India

ABSTRACT

The present study focuses on the influence of Silicon carbide (SiC) particulates on

the wear behaviour aluminium alloy metal matrix composites using a statistical

technique called Response Surface Methodology (RSM). LM13 aluminium alloy is

reinforced with SiC particulates using stir casting (compocasting) technique. The

weight fraction of particles in the Aluminium Metal Matrix Composite (AMMCs) was

varied from 0 to 12 wt. % in steps of 3wt. %. The wear experiments were conducted

using four factors and five levels central composite rotatable design (CCD). Dry

sliding wear tests were conducted by pin on disc apparatus to study the influence of

sliding speed, sliding distance, normal load and reinforcement wt% on the wear rate

of composite specimens. The results were analyzed using analysis of variance

(ANOVA) for identifying the significant factors affecting the performance at 95%

confidence interval. The empirical quadratic model relationships were established

using RSM to predict the influence of wear parameters on the performance parameter

(wear rate) with reasonably good accuracy. RSM was used to optimize the wear

parameters for a minimum wear rate. Results showed that normal load is the most

influencing factor which increases the wear rate and sliding speed is a least factor

which affects the wear rate. The statistical analysis is carried out using Design Expert

10 software.

Key words: Aluminium matrix composites, silicon carbide, compocasting, wear,

response surface methodology, optimization.

Cite this Article: J. Eric David Praveen, D.S. Robinson Smart, R. Babu and A. Vijin

Prabhu. Investigation on Wear Behaviour of LM13/SiC Aluminium Metal Matrix

Composites by Response Surface Methodology. International Journal of Mechanical

Engineering and Technology, 8(6), 2017, pp. 530–543.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=6



1. INTRODUCTION

Fuel economy is of great importance in the present automotive industry. Frictional losses in

heavy duty diesel engines (HDDE) lead to 2-2.5% of fuel consumption in the normal driving

condition. Half of these frictional losses are mainly due to the piston and piston rings [1].

Every automotive industry focuses on to manufacture parts with light weight, excellent

tribological properties and better performance. A special consideration is given during

manufacturing in parts such as pistons, piston rings, bearings, bushes and brake system

components where wear resistance is highly essential. AMMCs is considered as a potential

alternative material with conventional monolithic aluminium alloys in many applications

owing to its high specific strength and stiffness, low density, low thermal expansion

coefficient and high wear resistance. AMMCs are used in numerous industries that are not

limited to aerospace, automotive, defense, naval, electronic packaging, thermal and sports [2–

5]. AMMCs reinforced with SiC particulates exhibit higher modulus, strength and wear

resistance compared to conventional alloys. Among various AMMCs manufacturing

techniques liquid metallurgy is a commonly preferred method due to its simplicity, flexibility

and applicability for producing large quantities [6]. LM13 aluminium alloy containing 12%

silicon is widely used in manufacturing internal combustion engines parts such as pistons,

cylinder blocks, cylinder heads due to its high resistance to wear, corrosion & thermal

conductivities [7]. V.D. Londhe et al. have observed the wear increases with increase in

normal load at ambient temperature and the wear decreases with increase in normal load at

elevated temperature (125°C) of LM 13-SiC10% composite [8]. N.Radhika et al. have

investigated wear characteristics of LM 13/B4C Composites and concluded the wear increases

with increasing in normal load and decreases with increase in sliding velocity, sliding distance

and wt% of reinforcement [9]. S.Das has conducted a sliding wear, abrasive wear, erosion-

corrosion wear study on LM13-SiC composites and concluded the wear rate increases beyond

the seizure pressure and addition of SiC particles leads to improvement in seizure pressure

with that of unreinforced aluminium alloy. The wear rate of composites decreased with

increase in wt% of SiC particles. The alloy exhibited greater wear rate compared to the

composite in acidic medium and sand [10]. Garcia et al. have observed that the specific wear

rate of AA6061-SiC composite decreases with increase in volume fraction and size of

reinforcement [11]. Sahin et al. have observed a significant increase in wear resistance in

composites upto 10% addition of SiC and a saturated wear resistance beyond 10% to 55% SiC

addition produced by vacuum infiltration method. Among various other ceramics, Silicon

Carbide (SiC) which exhibits high elastic modulus (410 GPa), low density (3.2 g/cm3) and

high Vickers hardness (2600 HV) is very attractive for various automobile applications. SiC

is known to have better chemical compatibility with aluminium because it doesn’t forms any

inter-metallic phases during its interaction with the Al matrix, so it is a very common type of

reinforcement used in Al-MMCs [12]. The mechanical strength and wear resistance of

composites increases by adding SiC particulates to the matrix alloy. Prashant et al. conducted

experiments on Al 6061-SiC composite and concluded the hardness of metal matrix

composite increases with increase in reinforcement content and the wear rate of the Al6061-

SiC composite decreased with increasing SiC content [13]. Y.Sahin studied tribological

behaviour of 10 wt. % of SiCp with average particle size of 100 μm reinforced with Al 2014

alloy using orthogonal arrays and analysis of variance (ANOVA).The results indicated that,

the reinforcement exhibited greater effect on abrasive wear followed by applied load, whereas

sliding distance had the least effect [14]. Ravikiran et al. carried out the effect of sliding speed

on wear behaviour of A356 aluminium reinforced with 30 wt. % SiCp. The wear rate reduced

continuously with increase in speed [15]. The influence of input variables on responses in an

experiment is determined using statistical modelling technique. The response surface

methodology (RSM) is an empirical modeling approach for defining the relationship between

Investigation on Wear Behaviour of LM13/SiC Aluminium Metal Matrix Composites by

Response Surface Methodology


various process parameters and responses with the various desired criteria and searching the

significance of these process parameters on the coupled responses. It is a sequential

experimentation strategy for ramping up and optimizing the empirical model. Response

surface methodology (RSM) uses various statistical, graphical, and mathematical techniques

to acquire, improve, or optimize a process [16, 17]. RSM is being extensively used to analyze

engineering problems where input variables influences performance of response variables. It

provides quantitative data of possible interactions between input and response factors which

are difficult to obtain using other optimization techniques [18-23]. However various

tribological studies have been conducted on unlubricated metal matrix composites (MMC)

using RSM, parametric studies on tribological behaviour of piston alloy does not exist. An

attempt is made here to study the effect of SiC on the wear behaviour of LM13 aluminium

alloy (piston alloy) using statistical tools. The Design of experiments (DoE), analysis of

variance, and regression analysis, prediction and optimization are carried out through

Response Surface Methodology (RSM) using Design Expert 10 software.

1.1. Aim of Research

Improvement in the fuel economy and emission is a major challenge to the present automotive

industry. The vehicle weight and frictional losses are the two major factors which affects the

fuel economy. The friction encountered within the I.C Engine itself contributes about 40% of

power loss from the produced power as well wear of the piston rings and liner. Even a small

improvement in engine would reflect in the efficiency, fuel economy, emission levels which

can have a major effect on the worldwide fuel economy and the environment in the long-term.

One such attempt is carried out to find the wear behaviour of eutectic aluminium alloy (LM

13) as metal matrix reinforced with Silicon Carbide particulates.

2. EXPERIMENTAL SETUP

2.1. Development of Design Matrix

The most popular design in response surface methodology is central composite design (CCD).

The various factors and their levels which are used for analysis and construction of design

matrix are given in Table 1. The design matrix was developed using rotatable central

composite design with 31 set of trial runs, input and response results are shown in Table 2.

The literatures of the design matrix are available elsewhere [24, 25].

2.2. Production of Composite by Compocasting Process

Al–SiC composites were fabricated using compocasting process [26, 27]. LM 13 aluminium

alloy is used as metal matrix and its chemical composition is given in Table 3. The

reinforcement, SiC particulates details is given in Table 4. In the first step 1 kg of aluminium

alloy was measured and melted at 800◦C in a graphite crucible using a stir casting furnace.

According to the wt% SiC particulates were measured and preheated at a temperature of

400◦C for about 30 min in the preheating furnace. The preheating was done to remove the

surface impurities and reduce the oxide formation

Table 1 Factors and their levels in CCD experimental deign

S.No. Parameters Notations Unit Levels

-2 -1 0 1 2

1 Sliding Velocity V m/s 0.4 0.8 1.2 1.6 2

2 Sliding Distance D m 400 800 1200 1600 2000

3 Normal Load F N 10 20 30 40 50

4 Silicon Carbide S wt % 0 3 6 9 12



Table 2 Design matrix with its experimental results

Trial

Run

Sliding wear parameters Response

parameter

Sliding Velocity

(V)

Sliding

Distance

(D)

Normal

Load

(F)

Reinforcement

(SiC )

(S)

Wear Rate

(W)

X 10-5

mm3/m

01 0.8 800 20 3 848

02 1.6 800 20 3 732

03 0.8 1600 20 3 643

04 1.6 1600 20 3 542

05 0.8 800 40 3 1509

06 1.6 800 40 3 1216

07 0.8 1600 40 3 1218

08 1.6 1600 40 3 938

09 0.8 800 20 9 650

10 1.6 800 20 9 520

11 0.8 1600 20 9 461

12 1.6 1600 20 9 359

13 0.8 800 40 9 1295

14 1.6 800 40 9 1013

15 0.8 1600 40 9 985

16 1.6 1600 40 9 731

17 0.4 1200 30 6 856

18 2 1200 30 6 474

19 1.2 400 30 6 844

20 1.2 2000 30 6 455

21 1.2 1200 10 6 355

22 1.2 1200 50 6 1260

23 1.2 1200 30 0 1795

24 1.2 1200 30 12 366

25 1.2 1200 30 6 751

26 1.2 1200 30 6 706

27 1.2 1200 30 6 864

28 1.2 1200 30 6 810

29 1.2 1200 30 6 776

30 1.2 1200 30 6 750

31 1.2 1200 30 6 819

by absorption of gases. Once the metal is melt it was continuously stirred at 600–800 rpm

to create a vortex with the help of a mechanical stirrer for 10 mins during which a

hexochloroethane tablet (C2Cl6) was added to the melt to degas and liberate any unwanted

gases generated during the melting of the aluminium. The preheated SiC particles were added

slowly and continuously into the vortex of the molten metal. To improve wettability 1 wt. %

of Mg was added to the molten metal [28]. The stirrer was frequently moved vertically up and

down within the mixture to ensure uniform distribution of the added particles. After all the

SiC particulates were added into the molten metal the temperature of the furnace was set to

550◦C and the composite mixture was allowed to attain the solidus state in the crucible. In the

second step the slurry mixture was reheated and melted again at 750◦C. The molten metal was

again stirred at 300 rpm for about 2 mins. Finally it was cast into a 100 x 100 x 10 mm

preheated m.s mould. The composite was allowed to solidify in the atmospheric air and was

removed from the mould after solidification. The AMMCs with different weight percentage

(3, 6, 9 and 12 wt. %) of SiC particulates were produced by the same procedure.




Table 3 Chemical Composition of LM 13 aluminium alloy

Eleme

nt Al Si Fe Cu Mn Mg Ni Ga Zn Ti Pb Sn

Conten

t %

83.4

2

13.0

2

0.31

0

1.04

7

0.25

2

0.86

5

0.93

8

0.01

4

0.006

8

0.06

8

<0.00

5

<0.004

9

Table 4 Details of Reinforcements

Reinforcements Hardness (GPa) Grain size (µm) Density(g/cm3)

SiC 24.5-2 25 3.22

2.3. Micro-hardness Measurement

The micro-hardness (HV) of the composites was measured as per ASTM A-370 using Vickers

hardness tester (MITUTOYO-HM-114).The test was conducted on the polished specimens at

a load of 500 g applied for duration of 15 s at 5 different locations on all the specimens.

2.4. Macro-HARDNESS Measurement

The macro-hardness (HRC) of the composites was measured using Rockwell hardness tester

(model RAB-250). The test was conducted according to ASTM E-18 standard at a load of 100

kg applied for duration of 15 s at 5 different locations.

2.5. Wear Test

The wear test specimens 10 mm x 10 mm x 50 mm are obtained from the cast composites by

machining. The end surface of the specimens are cleaned and polished with 600 grade

followed by 1000 grade abrasive paper. The dry sliding wear test was conducted on pin-on-

disc wear apparatus (DUCOM TR20-LE) at room temperature according to ASTM G9905

standard. A grey cast iron disc with micro hardness (HV) and surface roughness 0.0001

microns was used to conduct the test. A computer aided data acquisition records the height

loss. The height loss is multiplied with the area of cross section of the composite pin to obtain

volume loss. Finally the volume loss is divided by sliding distance to obtain wear rate. The

experiments were carried out based on design matrix and the wear rate is computed for 31

trial runs which is presented in table 2. The wear surface of selected specimens was observed

using scanning electron microscope.

3. RESPONSE SURFACE MODEL FOR PREDICTION OF WEAR RATE

In the traditional experimental design one factor is varied at a time by keeping the other

factors constant to find the output response which results in more number of experiments for

various factors and levels. It is also very difficult to find the combined effect of the input

factors on the response in the experiment. Response surface methodology (RSM) is one of the

popular design which simplifies and reduces the number of experiments as well as it helps to

find the combined effect of input parameters on the output responses. In this study a RSM

with full factorial design of experiments consisting of four factors (sliding velocity, sliding

distance, normal load and reinforcement content) and five levels (-2,-1,0,+1,+2) was used. In

RSM the input process parameters are represented in the form of response in the quantitative

form as:

Y = f (X1, X2, X3, . . ., Xn) ± ε (1)

Where Y is output or response factor, f is response function and ε is experimentation error

respectively. X1, X2, X3. . . Xn are independent input process parameters.



The response function f is formulated by independent input process parameters by

applying a lower order polynomial equation. If the model is suitable the response of the model

can be represented by a linear function in terms of independent process parameters, then the

response represented in Eq.(1) can be written as

Y= C0 + C1X1 + C2X2 + . . .Cn Xn ± ε (2)

However, there are possibilities of curvature to be formed in the developed response

system. In this case a higher order polynomial, i.e., quadratic equation will be used to

represent the response equation and following equation may be used.

n n

Y = C0 + ∑ Ci Xn + ∑ di Xi2± ε (3)

i=1 i=1

As the wear rate of the composite is a function of sliding velocity, sliding distance, normal

load and wt % of SiC particulates, Eq.1 is rewritten as

W = f (V, D, F, S) (4)

The second order polynomial regression Eq.3 is rewritten with the four input factors is

expressed as:

W = C0+C1V + C2D C3F+ C4S + C12VD + C13VF + C14VS + C23DF + C24DS + C34FSC11V2

+ C22D2+C33F

2+C44S

2+ (5)

The experiment results were statistically analyzed by RSM technique using Design Expert

10 software which is widely used in many engineering research fields. Analysis of variance

(ANOVA) is performed to check the statistical significance of the quadratic model of wear

rate is presented in Table 5.

Table 5 ANOVA table for Wear Rate

Source Sum of

squares

Degree of

freedom Mean square F-value Prob.>F

Model 3.074 x 106 14 2.196 x 10

5 5.44 0.0009 Significant

V-Sliding velocity 2.838 x 106 1 2.838 x 10

5 7.03 0.0174

D-Sliding distance 3.680 x 105 1 3.680 x 105 9.11 0.0082

F-Normal load 1.340 x 106 1 1.340 x 10

5 33.20 <0.0001

S- Silicon carbide 7.357 x 106 1 7.357 x 10

5 18.22 0.0006

VD 1444 1 1444 0.036 0.8524

VF 21904 1 21904 0.54 0.4721

VS 132.25 1 132.25 3.275 x 10-3

0.9551

DF 7569 1 7569 0.19 0.6708

DS 132.25 1 132.25 3.275 x 10-3

0.9551

FS 1406.25 1 1406.25 0.035 0.8543

V2 618.79 1 618.79 0.015 0.9030

D2 2079.08 1 2079.08 0.051 0.8234

F2 27433.04 1 27433.04 0.68 0.4219

S2 2.815 x 106 1 2.815 x 106 6.97 0.0178

Residual 6.461 x 105 16 40381.77

Lack of fit 4.496 x 105 10 44959.95 1.37 0.3623 Not significant

Pure Error 1.965 x 105 6 32751.48

Cor. Total 3.720 x 106 30

Std. Dev. 200.95 R-Squared 0.9263

Mean 812.45 Adj R-

Squared 0.8744

The results are analyzed with confidence level 95% or p-value of 0.05. This implies that

any factor with p-value equal to or less than 0.05 is significant and greater than 0.05 is termed

non-significant. ANOVA shows the “Model” as “Significant” while the “Lack of fit” is “Not

significant”, which are desirable for a model. The “Prob. > F” column indicates the




significance and non-significance of the factors and its interactions. The Model F-value is

5.44 and Prob.>F value is 0.0009 implies the model is significant and is important. There is

just a 0.01% chance that an F-value could occur due to interference or noise. Values greater

than 0.1000 indicate the model terms are not significant. Table 5 also shows the other

adequacy measures of R2, Adj R

2, and Pred R

2 are nearer to 1 which means the regression

model indicates the goodness of fit between input variables (wear parameters) and output

response (wear rate). The regression equation is obtained from the Design Expert software in

terms of actual factors is used to predict the wear rate with a reasonable accuracy is given in

Eq.6.

Wear Rate (W) = 1083.81845 + 18.52678 * V - 0.07543 * D + 24.54940 * F - 175.53373

* S + 0.05937 * V * D - 9.25000 * V * F - 2.39583 * V * S - 5.43750 * 10-3

* D * F-2.39583

* 10-3

* D * S - 0.31249 * F * S - 29.07366 * V2

- 5.32924 * 10-5

D2

+ 0.30973 * F2

+11.02480 * S2

(6)

Figure 1 (a) Comparison of Predicted vs. Actual values of wear rate

Design-Expert® Softwarewear rate

Color points by value ofwear rate :

1795

328

Actual

Pre

dic

ted

Predicted vs. Actual

0

500

1000

1500

2000

0 500 1000 1500 2000



Figure 1 (b) Residuals vs. Predicted values of wear rate

Fig.1 (a) shows the predicted vs. actual values of wear rate which are very useful in

validating the experimental data. The values fall well near the straight line which indicates the

experimental data are acceptable. Fig.1 (b) shows the residuals vs. predicted values of wear

rate. There is no specific pattern formed in the plot and the residuals are falling in near the

straight line, which indicates that the errors are normally distributed. Hence the model formed

by RSM can be applied to predict the wear rate within the defined range of input parameters.

4. RESULTS AND DISCUSSION

4.1. Effect of Reinforcements on Micro and Macro Hardness

The results of the micro-hardness (HV) and macro hardness (BHN) tests conducted on the

specimens with different wt % of SiC composites is shown in Table.6. It is very evident as the

wt. % of SiC increases, the micro and macro hardness of the composite increases. The

increase in the hardness shows a linear trend. It is experimentally proven fact that whenever a

hard reinforcement is reinforced with a ductile matrix, the hardness of the matrix material is

enhanced. [29].

Table 6 Details of LM13-SiC composites

Design-Expert® Softwarewear rate

Color points by value ofwear rate :

1795

328

Predicted

Exte

rnally

Stu

dentized R

esid

uals

Residuals vs. Predicted

-6.00

-4.00

-2.00

0.00

2.00

4.00

200 400 600 800 1000 1200 1400 1600

3.83802

-3.83802

0

wt % of SiC 0 3 6 9 12

Micro Hardness (HV) 130 135.2 143 151.4 160

Macro Hardness (HRC) 105 109 115.2 122.4 130




4.2. Effect of Variables on Wear Rate

The change in wear rate with respect to sliding velocity, sliding distance normal load and

silicon carbide wt% is shown in Fig.2. It is very evident, as the sliding velocity and sliding

distance increases the wear rate of the composite decreases. The wear rate increases with

increase in normal load. This is due to the vertical force which is acting on the pin makes the

pin to rub heavily on the rotating disc results in excessive wear. As the wt% of silicon carbide

increases the wear rate decreases linearly. It can be seen the aluminium alloy undergoes a

severe wear with that of the unreinforced alloy. The interaction effects of the variables are

presented in Fig. (3-5).The same effects can be observed in the interaction plots. From Fig.3

the wear rate which was high at the lower sliding velocity and sliding distance tend to

decrease with increase sliding velocity and sliding distance. This may be due to the fact as the

sliding speed increases the temperature of the rubbing surface increases leads to oxidation

which makes the rubbing surface hard results in low wear of the composites. Also at high

sliding speeds the interaction time between the sliding surfaces decreased which results in low

wear. In addition to this the sliding of pin over long distances causes hardening of the surface

layer composition of the waste debris and reduces wear. The effect of sliding velocity and

normal load on wear rate is shown in Fig.4. The wear rate is less at low level sliding speed

and normal load. The wear rate shows a linear trend with increase in the sliding speed but

whereas during the increase in normal load the wear rate also increases. This is due to the fact

when the load increases the pressure on the sliding surfaces increases which resulted in

increased wear at high loads. Fig.5 shows the interaction effect of sliding speed and silicon

carbide wt% on wear rate. The wear rate is high at low sliding speed and unreinforced

aluminium alloy. As the sliding speed increases the wear rate of the composites decreases.

This is mainly due to increase in temperature which led to softening of the matrix and

composite pin surface. As the silicon carbide content in the aluminium alloy increases the

wear rate of the composite decreases. This is mainly due to the mechanically mixed layer

(MML) formed on the worn surface of the composite which controls the wear properties of

the composites.

Figure 2 Variation of wear rate

Design-Expert® SoftwareFactor Coding: Actualwear rate (mm3/m)

Actual FactorsA: Sliding Velocity = 1.2B: Sliding Distance = 1200C: Normal Load = 30D: Silicon Carbide = 6

-1.000 -0.500 0.000 0.500 1.000

-500

0

500

1000

1500

2000

A

A

B

BC

C

D

D

Perturbation

Deviation from Reference Point (Coded Units)

wear

rate

(m

m3/m

)



Figure 3 Effect of sliding velocity and sliding distance interaction on wear rate

Figure 4 Effect of sliding velocity and normal load interaction on wear rate


Design points above predicted valueDesign points below predicted value1795

328

X1 = A: Sliding VelocityX2 = B: Sliding Distance

Actual FactorsC: Normal Load = 30D: Silicon Carbide = 6

400

800

1200

1600

2000

0.4

0.8

1.2

1.6

2

0

500

1000

1500

2000

wear

rate

(m

m3/m

)

A: Sliding Velocity (m/s)B: Sliding Distance (m)



328

X1 = A: Sliding VelocityX2 = C: Normal Load

Actual FactorsB: Sliding Distance = 1200D: Silicon Carbide = 6

10

20

30

40

50

0.4

0.8

1.2

1.6

2

0

500

1000

1500

2000

wear

rate

(m

m3/m

)

A: Sliding Velocity (m/s)C: Normal Load (N)




Figure 5 Effect of sliding velocity and silicon carbide (wt %) interaction on wear rate

4.3. Optimization of Wear Parameters

The objective of optimization is to provide the optimum wear parameters which give the

minimum wear rate. This analysis is based on “smaller is better” concept. It means low wear

rate is considered as optimum [30]. The input wear parameters were selected based on the

desirability values. The desirability values close to 1 unit were selected as the most effective

parameters value with respect to wear rate. The ramp function and the desirability bar graphs

are shown in Figs. 6 and 7, respectively. The optimal solution for the wear rate is shown in

Fig. 6. It is seen from Fig. 6 that the optimal values of input parameters are sliding velocity is

1.45938, sliding distance is 1674.11, normal load is 18.5766 and silicon carbide wt% is

7.72246. It is seen from Figs. 6 and 7 that approximately 100% of desirability is achieved for

the output response. The bar graphs show the overall desirability function of the responses.

Desirability varies from 0 to 1 depends on the nearness of the response toward the objective.

The bar graph clearly indicates how well the each variable satisfies the criterion, a value close

to one is considered to be proficient.

Figure 6 Ramp function graph of desirability of wear rate



328

X1 = A: Sliding VelocityX2 = D: Silicon Carbide

Actual FactorsB: Sliding Distance = 1200C: Normal Load = 30

0

3

6

9

12

0.4

0.8

1.2

1.6

2

0

500

1000

1500

2000

wear ra

te (m

m3/m

)

A: Sliding Velocity (m/s)D: Silicon Carbide (Wt %)



Figure 7 Desirability bar graph of wear rate

4.4. Validity of the Wear Model and Confirmation of Experiments

The validity of the dry sliding wear model was evaluated by conducting dry sliding wear test

on composites at different values of the experimental factors such as sliding speed (V), sliding

distance (D), normal load (F) and silicon carbide wt% (S). As the equation of response for the

model is derived from quadratic regression, confirmation test must be conducted in order to

confirm their validity. The independent variable selected for the confirmation experiments

must lie within the ranges for which equations were derived. The data of the confirmation

experiments and their comparison with the predicted values for wear rate are listed in Table 6.

From the table it is inferred that the error between experimental and predicted values is within

±5% for the response. All the experimental values of each run are within the 95% prediction

interval. This shows the quadratic model obtained is accurate which confirms the

experimental conclusion.

Table 7 Confirmatory test and comparison of experimental and RSM model

Exp.No.

Sliding wear parameters Response parameter

Sliding

Velocity

(V)

Sliding

Distance

(D)

Normal

Load

(F)

Reinforcement (SiC )

(S)

Wear Rate (W)

X 10-5 mm3/m

Exp. RSM Error

(%)

1 1.5 1675 20 8 359 360.535 1.5

5. CONCLUSIONS

In the present experimental work LM13/SiC metal matrix composites were successfully

fabricated by compocasting process and dry sliding wear test was conducted on pin-on-disc

apparatus and then following conclusions are drawn.

1. The micro and macro hardness of composite increased when compared with base metal

matrix. The hardness of the composite increased with increase of SiC wt%.




2. The wear resistance of the composite increased when compared to conventional metal

matrix. The wear resistance of the composite increased with the increase of hardness of the

composite.

3. The wear rate of composites decreased with increase in sliding speed, sliding distance and

reinforcement wt % and decreased with increase in normal load.

4. The wear resistance of developed composites was higher than that of cast metal matrix.

This is due to the formation of MML on the worn surface of the composite which played a

key role played in controlling the wear properties of the composites.

5. The ANOVA indicated that normal load is the most influential factor followed by

reinforcement wt %, sliding distance and load on the wear rate of composites.

6. Optimization by RSM method for a minimum wear rate of 360.535, the optimal value of

wear parameters sliding velocity is 1.45938, sliding distance is 1674.11, normal load is

18.5766 and silicon carbide wt % is 7.72246 respectively.

7. The confirmation experiments showed that the error between experimental and predicted

value of wear rate lies within the range of ±5% for the response.

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INVESTIGATION ON WEAR BEHAVIOUR OF LM13/SiC …LM 13 aluminium alloy is used as metal matrix and its chemical composition is given in Table 3. The reinforcement, SiC particulates details