Wear Predictions of Metal Matrix Composite in Presence of Greasing Material ()

Shivaji V. Gawal, Vinod B. Tungikar

Mechanical Engineering Department, Pune Vidhyarthi Griha’s College of Engineering and Technology, Pune, India.

Production Engineering Department, Shri Guru Gobind Singhji Institute Of Engineering & Technology, Nanded, India.

**DOI: **10.4236/epe.2012.43024
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Mechanical Engineering Department, Pune Vidhyarthi Griha’s College of Engineering and Technology, Pune, India.

Production Engineering Department, Shri Guru Gobind Singhji Institute Of Engineering & Technology, Nanded, India.

Aluminum-copper titanium di-boride [Al-4Cu_{-x}TiB_{2}] composite (x = 1%, 1.75%, 2.5%) is prepared successfully by centrifugal casting. Samples pin of diameter 8 mm, 10 mm, & 12 mm are prepared with help of special purpose die. An experimental parameter analysis is obtained for various load and speed combinations on pinon wear disc testing machine. A larger volume fraction of particles can be attained near the wear surface via centrifugal casting. The volume fraction of the heavier titanium di-boride is controlled by inertial forces upon centrifugal processing the semisolid composite. Mathematical Regression Analysis is carried out to calculate wear. Greasy material facilitates heat transfer on the counter side material. Comparative study facilitates wear predictions of Al-4Cu_{-x}TiB_{2} metal matrix composite for various practical applications.

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S. Gawal and V. Tungikar, "Wear Predictions of Metal Matrix Composite in Presence of Greasing Material," *Energy and Power Engineering*, Vol. 4 No. 3, 2012, pp. 173-177. doi: 10.4236/epe.2012.43024.

1. Introduction

The difficulties in the development of particulate metal matrix such as poor wettability, inhomogeneous distribution of reinforcement particles, formation of unwanted reaction products at the interface between the matrix and reinforcement, etc. have led to attempts to synthesis new generation in composites. Among the composites metal matrix composites have become popular in the recent years. Functionally graded materials (FGMs) are spatial composites that display discrete or continuously varying composition over a definable geometrical length [1]. A number of processes are proposed to fabricate the FGMs such as adhesive bonding, sintering, thermal spray, reaction infiltration and so on. Centrifugal casting is a special type of casting process in which the melt fills into the mould and solidifies under a centrifugal force field. Major applications of centrifugal walled castings are pipes, piston rings, cylinder liners, rollers, pulleys etc. The features involved in centrifugal casting are fluid flow, thermal properties and solidification. The casting parameters that influence solidification structures includs the mold rotation velocity, the pouring temperature of the molten metal and the alloy composition the mould dimension and the mould preheating temperature etc. [2]. The extent of segregation depends on various above process parameters. The moving direction of the solid particles in the molten matrix is determined by the densities differences between the molten metal and reinforcement particles.High density reinforcement particles move far away in the radial direction compared to the molten matrix particles [3]. Some previous work considers a hollow cylinder rotating vertically around the central axis under the effect of gravity [4]. Experimentation is carried to prepare the samples of 12mm diameter. Various constants are calculated from the wear equation. Mathematical Regression Analysis is used to calculate wear.

2. Experimentation

An Experimental set up used to prepare Al-TiB_{2} metal matrix composite is shown is Figure 1. A 3 HP, 1440 rpm, motor is used to drive the cantilever die with the help of belt drive. A stepped pulley is mounted on one end of shaft and to other end cantilever die is fitted. The speed of the casting is varied using stepped pulley drive. Crucible kept in the furnace for melting the base metal and the reinforcing material is shown in Figure 2. Special purpose die used to prepare samples as per ASTM G99 standards is shown in Figure 3, while Figure 4 shows cut section of special purpose die. Figure 5 shows sample pin.

3. Result and Discussions

Wear and friction testing machine is shown in Figure 6. The wear and friction monitor consist of machine, controller, data acquisition system, sensors and cables. It

Figure 1. Experimental set up showing the drive arrangement for cantilever die.

Figure 2. Furnace with crucible.

Figure 3. Special purpose die.

Figure 4. Cut section of special purpose die.

Figure 5. Sample pin.

Figure 6. Wear and friction test rig.

facilitates study of friction and wear characteristics in sliding contact under desired conditions. Sliding occurs between the stationary pin and a rotating disc. Normal load, rotational speed and wear track diameter can be varied to suit the test conditions. Tangential frictional force and wear are monitored with electronic sensors and recorded on computer .These parameters are available as function of load and speed. Reading of wear are taken after making all the necessary connections and Sliding speed is calculated as follows Sliding speed = π × D × N/60,000.

Cross section Area of rod = π × d^{2}/4 where, D = Diameter of wear track in mm. N = Disc speed in rpm. T = Test duration in sec, d = diameter of specimen rod.

3.1. Calculation of Constants [5,6]

Let, W = Wear in micron, L = Load in N, V = Sliding Speed in m/s, T = Test duration in sec.

K = Proportionality Constant, a, b, & c are the index of load, speed & time respectively,W_{1} and W_{2 }are wears corresponding to loads L_{1}, L_{2}; Sliding speeds V_{1}, V_{2} and testing time T_{1}, T_{2}

Equation of wear is –W = K × L^{a} × V^{b} × T^{c}

3.1.1. Calculation of “a”

Table 1 shows Calculation of “a” by considering V & T Constant i.e. (k × V^{b} × T^{c} = Cont).

W_{1}/W_{2} = (L_{1}/L_{2})^{a}

ln(W_{1}/W_{2}) =a × ln(L_{1}/L_{2})

a = ln(W_{1}/W_{2})/ln(L_{1}/L_{2})

Regression Analysis: WEAR versus LOAD

The regression equation is

WEAR = –1.77 + 0.437 LOAD

Predictor Coef SE Coef T P VIF

Constant –1.7670 0.6073 –2.91 0.101

LOAD 0.43750 0.01685 25.97 0.001 1.000

S = 0.370355 R-Sq = 99.7% R-Sq(adj) = 99.6%

3.1.2. Calculation of “b”

Similarly, b is

b= ln (W_{1}/W_{2}) / ln(V_{1}/V_{2})

where, k × L^{a} × T^{c} = Constant

Table 2 shows calculations of b by considering L &T constant.

Regression Analysis: WEAR versus SLIDING SPEED

The regression equation is

WEAR = –52.0 + 33.4 SLIDING SPEED

Predictor Coef SE Coef T P VIF

Constant –52.0010 0.0022 –23758.06 0.000

SLIDING SPEED 33.4093 0.0005 73904.47 0.000 1.000

S = 0.000423587 R-Sq = 100.0% R-Sq(adj) = 100.0%

3.1.3. Calculations of “c”

c = ln (W_{1}/W_{2})/ln (T_{1}/T_{2}) where k × L^{a} × V^{b} = Constant.

Table 3 shows calculations of “c” by considering L & V Constant.

Regression Analysis: WEAR versus TEST TIME

The regression equation is

WEAR = 11.5 + 0.0800 TEST TIME

Predictor Coef SE Coef T P

Constant 11.5000 0.8660 13.28 0.006

TEST TIME 0.080000 0.003162 25.30 0.002

S = 0.707107 R-Sq = 99.7% R-Sq(adj) = 99.5%

3.1.4. Calculations of “k”

Table 4 shows calculations of “k”.

Conflicts of Interest

The authors declare no conflicts of interest.

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