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SYNTHESIS AND CHARACTERIZATION OF NANO

PARTICLE REINFORCED ALUMINIUM

COMPOSITESF.No.42 – 902/2013(SR)

Major Research Project Report

Submitted to

The University Grants Commission

New Delhi

By

Prof. G. Swami NaiduPrincipal Investigator

Department of Metallurgical Engineering

University College of Engineering,Vizianagaram

JNTU Kakinada

Andhra Pradesh

INDIA

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CONTENTSSL. NO CONTENT PAGE NO

1 1.0 Introduction 022 2.0 Objectives 023 3. 0 research methodology and experimental studies 03

4 3.1 Raw Materials 035 3.2. X-Ray Diffraction Studies 036 3.3 Stir Casting 087 3.4 Homogenization 098 3.5 Hardness and upset tests 099 3.6 Wear Tests 0910 4.0 Results and Discussion 1111 4.1 Effect of composition on wear rate 1112 4.1.1 Wear tests on pure Al 1113 4.1.2 Wear tests on Al+5% red mud (micro) 1214 4.1.3 Wear tests on Al+5% red mud (nano) 1315 4.1.4 Wear tests on Al+10% red mud (micro) 1416 4.1.5 Wear tests on Al+10% red mud (nano) 1517 4.2 Deformation Studies 2118 4.2.1 Effect of deformation on wear rate (Al+5% micro red

mud)21

19 4.2.2 Effect of deformation on wear rate (Al+5% nano red mud) 2720 4.2.3 Effect of deformation on wear rate (Al+10% micro red

mud)32

21 4.2.4 Effect of deformation on wear rate (Al+10% nano red mud)

38

22 4.3 Microstructural observations 4023 4.4. Regression Modelling 4224 4.5. Artificial neural network modelling 4325 4.5.1 Network training and testing 4426 5.0 Conclusions 4727 References 4828 List of Publications 51

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1.0 INTRODUCTION

Conventional monolithic materials have limitations with respect to achievable

combinations of strength, stiffness, and density. In order to overcome these shortcomings

and to meet the ever-increasing engineering demands of modern technology, metal matrix

composites are gaining importance. In recent years, discontinuously reinforced aluminium

metal matrix composites have attracted worldwide attention as a result of their potential to

replace their monolithic counterparts primarily in automobile and energy sector. In the

present work, nano red mud particke reinforced aluminium matrix composites are synthesized

by stir casting successfully. Different weight fractions of red mud was mixed with the matrix

material and tested for wear, mechanical properties and corrosion resistance studies. The

merits of nano red mud reinforcement over micro red mud reinforcement will be determined.

The best fraction of red mud reinforcing phase required in the composites for optimum

properties will be determined. The proposed research work will be undertaken with an

objective to explore the use of red mud as a reinforcing material as a low cost option.

The aluminium metal matrix composites are produced either by casting route or by

powder metallurgy. Stir casting is widely used due to its simplicity, flexibility and

applicability to large quantity production, hence the final cost of the product can also be

minimized.

Redmud is the caustic insoluble waste residue generated by alumina production form

bauxite by Bayer’s Process. The use of nano redmud as reinforcement material for

preparation of MMC has not been explored till date.

The present work is focussing on the utilization of abundant available industrial waste

redmud in useful manner by dispersing it into aluminium matrix to produce composites by

liquid metallurgy route.

2.0 OBJECTIVES

The main objective of this work is to synthesize and characterize MMCs using nano redmud

as reinforcement and aluminium as matrix material by liquid metallurgy route.

o To prepare samples with different weight fractions of both nano and micro

structured redmud particles as reinforcement.

o To determine various mechanical properties of both nano and micro structured

redmud reinforced Aluminium metal matrix composite.

o To conduct the upset tests to give different deformations to the samples.

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o To conduct wear tests on a pin on disc wear testing machine to determine the

wear characteristics of both micro and nano structured redmud reinforced

Aluminium MMC.

3. RESEARCH METHODOLOGY AND EXPERIMENTAL STUDIES:

3.1 Raw Materials:

Red mud is obtained from NALCO, Bhubaneswar and its chemical analysis is

furnished in Table 1.

CONSTITUENT

S

% weight

(wt)

CONSTITUENT

S

% weight

(wt)

Al2O3 15.0 Fe2O3 54.8

TiO2 3.7 SiO2 8.44

Na2O 4.8 CaO 2.5

P2O5 0.67 V2O5 0.38

Ga2O3 0.096 Mn 1.1

Zn 0.018 Mg 0.056

Organic C 0.88 L.O.I Balance

Table 1. Chemical analysis of red mud

The redmud is subjected to sieve analysis using mechanical sieve shaker. Different

particle sizes of 53 microns, 75 microns and 106 microns were obtained. Redmud of particle

size 106 microns is taken for preparation of MMCs with microstructured reinforcement.

Micro structured redmud is reduced to nano structured using High energy Planetary

Ball mill (Model Retsch, PM 100, Germany) at IIT, Chennai in a stainless steel chamber

using tungsten carbide and zirconia balls of 10 mm Ø and 3mm Ø. The milling is carried for

30 hours by maintaining the rotation speed of the planet carrier at 200 rpm. The ball mill is

loaded with ball to powder ratio (BPR) of 10:1. Toluene is used as the medium with an

anionic surface agent to avoid agglomeration. The milled sample powder is taken out at

intervals of 5 hrs and subjected for XRD analysis. .

3.2. X-Ray Diffraction Studies

The fresh as well as milled Red mud is characterized with an X-Ray Diffractometer

(Model: 2036E201; Rigaku, Ultima IV, Japan). JADE software is used to investigate the

structural changes and phase transformations of powders occured during mechanical milling.

Sample preparation of XRD is done as per the standard practice. Sample packing is carried

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out by filling the Red mud on a glass slit of 12 X 12 X 2 mm size. Precautions are taken to

have a tight powder packing on the glass slit and no manual contamination with the powder

specimens. The X-ray diffraction measurements are carried out with the help of a Goniometer

model 2036E201 using Cu Kα radiation (Kα= 1.54056 A0) at an accelerating voltage of 40

kV and a current of 20 mA. In this test the sample is kept in stationary condition, only the

arms of the X- ray tube was rotating in the opposite direction up to 900 of 2θ during the test.

The samples were scanned in the range from 30 to 900 of 2θ with a scan rate of 20 / min. The

analysis is carried out to find out crystallite size, peak height, and amount of induced strains

in the milled Red mud.

The average crystallite size is calculated from the full width at half maximum

(FWHM) of the X-ray diffraction peak using Scherer’s equation (Cullity, 1978).

D = (k λ) / (B cosθ)

Where D is the particle diameter, λ is the X – Ray wavelength, B is the FWHM of the

diffraction peak, θ is the diffraction angle and K is the Scherer’s constant of the order of unity

for usual crystals. The existence of stress in the materials results in lattice distortions of

crystals; consequently, the diffraction peaks of the crystals are broadened. The relationship

between the half width of the broadened diffraction peaks, Bd and the distortion of lattice,

(Δd/d) was described by Yang et al. (2000). The lattice distortion (Δd/d) can be obtained

from the following equation.

(Δd/d) = Bd / (4 tanθ)

Where Bd, is half width of the broadened diffraction peaks, θ is half of the diffraction angle.

During ball milling the intense mechanical deformation experienced by the Red mud leads to

generation of lattice strains, crystal defects. The balance between cold welding and fracturing

operations among the powder particles is expected to affect the structural changes in the

powder. The measurement of crystallite size and lattice strain in the mechanically milled

powders is very important since the phase constitution and transformation characteristics

appear to be critically depending on them (Suryanarayana, 2001). A steady decrease in the

crystallite size is observed and it is found that the crystallite size has been reduced from 400

nm to 40 nm for 30h milling time. The relative lattice strain is increasing with increasing the

duration of milling time. This lattice strain is increased from 0.12 to 0.28 for as received and

30 h milled Red mud respectively.

The X-Ray diffractograms obtained at various time intervals of milling are shown in

Figures 1-6 respectively. The X-ray diffractograms have revealed a small tungsten carbide

contamination in the milled Red mud sample. This entry might be from the tungsten carbide

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balls which were used as milling media during milling; It is also evident that the intensity of

the peaks in the XRD pattern got reduced and the peak broadening increased as the duration

of milling increases. Three major phases were identified for all the milling times; which are

MnSiO2, MgAl.79Fe1.21O4 and Al2Mg O4.

Fig 1. X-Ray Difractogram Obtained before Milling

Fig 2. X-Ray Difractogram Obtained after 6 hrs of Milling

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8

Fig 5. X-Ray Diffractogram obtained after 24 hours of milling

Fig 6. X-Ray Diffractogram obtained after 30 hours of milling

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Diffraction peak positions are accurately measured with XRD, which makes it the

best method for characterizing homogeneous and inhomogeneous strains. Homogeneous or

uniform elastic strain shifts the diffraction peaks positions. From the shift in peak positions,

one can calculate the change in d-spacing, which is the result of the change of lattice

constants under a strain. Inhomogeneous strains vary from crystallite to crystallite or within a

single crystallite and this cause a broadening of the diffraction peaks. The XRD graphs

illustrate that with increasing milling time the peak height intensity shift slightly to the lower

heights and increasing the peak broadening. This is the indication of high energy milling

decreases the crystallinity of the Red mud, thus increasing the amorphous phase in it.

3.3 Stir Casting

Pure aluminium is preheated in muffle furnace at around 2000C for 2 hours to remove

any contamination. The required quantities of redmud (5, 10, 15 percent by weight) is taken

and is preheated in a furnace upto 1000 C. The weighted quantity of pure aluminium is

melted to the desired temperature of 8000C and redmud is then added to the molten metal and

stirred continuously using mechanical stirrer. Argon gas is used to avoid any agglomeration.

The melt with the nano redmud reinforced particulates is poured into cylindrical metal

moulds. Using the similar method samples with the micro redmud reinforced particulates are

also prepared.

After solidification, the castings are taken out form the moulds and are cut to the

required shape and sizes for mechanical testing and for wear testing. To ascertain the

distribution of reinforcement of redmud, the polished samples are inspected under optical

microscope and the microstructures of the samples are shown in figures 7-8, the distribution

of the reinforced redmud particles is uniform in the aluminium metal matrix.

Fig. 7. Optical micrograph (Al + 10% nano structured redmud)

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Fig. 8. Optical micrograph (Al + 15% micro structured redmud)

3.4 Homogenization:

The samples are homogenized at 1000 C for 24 hours in a muffle furnace.

3.5 Hardness and upset tests:

Upset tests are conducted on compression testing machine and deformations of 10%,

20%, 30%, 40% are obtained for all the composites. The hardness tests are conducted

usingVickers hardness tester and the results are furnished in table 2.

Metal/Composite VHN. 10% def 20% def 30%

def

40% def

Al + 5% micro structured Redmud 46.68 52.12 57.42 66.08 69.12



Al + 5% nano structured Redmud 49.8 54.44 65.97 75.20 76.27



Table 2. Hardness measurements

3.6 Wear Tests:

Wear Tests are conducted as per ASTM G-99 Standard under unlubricated condition

in a normal laboratory atmosphere at 50-60% relative humidity and a temperature of 28-350C.

Each test is carried for 5 hrs run. The mass loss in the specimen after each test is estimated

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by measuring the weight of the specimen before and after each test using an electronic

weighing machine having an accuracy up to 0.01 mg. Care has been taken that the specimens

under the test are continuously cleaned with woollen cloth to avoid entrapment of wear debris

and to achieve uniformity in experimental procedure.

The tests have been carried under the following conditions:

The specimens under tests were fixed to the collect. The collect along with the specimen

(Pin) is positioned at a particular track diameter. Load is applied through a dead weight

loading system to press the pin against the disc. Frictional force arises at the contact can be

read out from the controller. The speed of the disc or motor rpm can be varied through the

controller. Each set of test was carried out for a period of 5 hours run. After each one hour

run, the test pieces were removed from the machine and weighed accurately to determine the

loss in weight.

Wear Calculations:

Wear rate was estimated by measuring the mass loss in the specimen after each test and mass

loss, ∆m in the specimen is obtained. Wear rate which relates to the mass loos to sliding

distance (L) is calculated using the expression

Wr = ∆m/L

The Volumetric wear rate Wv of the composite is relate to density (ρ) and the

abrading time (t), was calculated using the expression,

Wv = ∆m/ρt

The friction force is measured for each pass and then averaged over the total number

of passes for each wear test. The average value of co-efficient is calculated using the

expression

µ = Ff / Fn

Where Ff is the average friction force and Fn is the applied load.

For characterisation of the abrasive wear behaviour of the composite, the specific

wear rate is employed. It is defined as the volume loss of the composite per unit sliding

distance and per unit applied normal load. The specific wear rate expressed in terms of the

volumetric wear rate as

Ws = Wv / Vs Fn

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Where Vs is the sliding velocity.

4.0 RESULTS AND DISCUSSION

4.1 Effect of composition on wear rate

4.1.1 Wear tests on pure Al:

Wear tests are conducted on pure aluminium samples using various loads ie., 10N,

20N, 30N at different speeds of 200 RPM, 400 RPM, 600 RPM and a few results are

tabulated below.

Pure Al Load – 10 N Speed – 200 RPM ρ = 2.62x103 Kg/m3

m1

(gm)m2

(gm)∆m(gm) Time Ff

(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.384 1.37 0.014 3600 3.3 0.33 0.942478 0.145722 1.484047 5.6686441.384 1.358 0.026 7200 3.3 0.33 1.884956 0.135314 1.378044 5.2637411.384 1.342 0.042 10800 2.9 0.29 2.827433 0.145722 1.484047 5.6686441.384 1.328 0.056 14400 3.3 0.33 3.769911 0.145722 1.484047 5.6686441.384 1.32 0.064 18000 3 0.3 4.712389 0.133232 1.356843 5.18276

Table 3


m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)Wsx10-13

m3/N-m

1.452 1.421 0.031 3600 4.9 0.49 1.884956 0.161335 3.286105 6.2759981.452 1.399 0.053 7200 3.1 0.31 3.769911 0.137916 2.80909 5.3649661.452 1.386 0.066 10800 4.7 0.47 5.654867 0.114496 2.332075 4.4539341.452 1.362 0.09 14400 4.7 0.47 7.539822 0.117098 2.385076 4.555161.452 1.341 0.111 18000 4.3 0.43 9.424778 0.115537 2.353275 4.494425

Table 4

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m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)Wsx10-13

(m3/N-m)

1.658 1.61 0.048 3600 4.6 0.46 2.827433 0.16654 5.088163 6.47845

1.658 1.566 0.092 7200 4.8 0.48 5.654867 0.159601 4.876156 6.208515

1.658 1.526 0.132 10800 3.9 0.39 8.4823 0.152661 4.664149 5.938579

1.658 1.482 0.176 14400 4.4 0.44 11.30973 0.152661 4.664149 5.938579

1.658 1.434 0.224 18000 4.2 0.42 14.13717 0.155437 4.748952 6.046553Table 5

4.1.2 Wear tests on Al+5% red mud (micro):

Wear tests are also conducted on Al+5% red mud (micro) samples using various loads

ie., 10N, 20N, 30N at different speeds of 200 RPM, 400 RPM, 600 RPM and a few results are

tabulated below.

Al+5% Micro RM Load – 10 N Speed – 200 RPM ρ = 2.42x103 Kg/m3

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.362 1.352 0.01 3600 3.4 0.34 0.942478 0.104087 1.143271 4.3669751.362 1.344 0.018 7200 3.8 0.38 1.884956 0.093679 1.028944 3.9302781.362 1.336 0.026 10800 4.2 0.42 2.827433 0.090209 0.990835 3.7847121.362 1.327 0.035 14400 3.6 0.36 3.769911 0.091076 1.000362 3.8211031.362 1.318 0.044 18000 4.8 0.48 4.712389 0.091597 1.006079 3.842938

Table 6


m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)Wsx10-13

m3/N-m

1.452 1.43 0.022 3600 4.7 0.47 1.884956 0.114496 2.515197 4.8036731.452 1.412 0.04 7200 5.6 0.56 3.769911 0.104087 2.286543 4.3669751.452 1.397 0.055 10800 5.5 0.55 5.654867 0.095413 2.095998 4.003061.452 1.378 0.074 14400 5.6 0.56 7.539822 0.096281 2.115052 4.0394521.452 1.356 0.096 18000 5.9 0.59 9.424778 0.099924 2.195081 4.192296

Table 7

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m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.448 1.412 0.036 3600 5.8 0.58 2.827433 0.124905 4.115777 5.240371.448 1.38 0.068 7200 6.4 0.64 5.654867 0.117966 3.887123 4.9492381.448 1.357 0.091 10800 6.2 0.62 8.4823 0.105244 3.467923 4.4154971.448 1.329 0.119 14400 7.2 0.72 11.30973 0.10322 3.401232 4.3305841.448 1.291 0.157 18000 7.3 0.73 14.13717 0.108945 3.589872 4.570767

Table 8

4.1.3 Wear tests on Al+5% red mud (nano):

Wear tests are also conducted on Al+5% red mud (nano) samples using various loads

ie., 10N, 20N, 30N at different speeds of 200 RPM, 400 RPM, 600 RPM and a few results are

tabulated below.

Al+5% Nano RM Load – 10 N Speed – 200 RPM ρ = 2.37x103 Kg/m3

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.484 1.476 0.008 3600 3.3 0.33 0.942478 0.08327 0.937018 3.5791431.484 1.469 0.015 7200 3.4 0.34 1.884956 0.078065 0.878454 3.3554471.484 1.463 0.021 10800 3.2 0.32 2.827433 0.072861 0.81989 3.131751.484 1.457 0.027 14400 2.8 0.28 3.769911 0.070259 0.790609 3.0199021.484 1.449 0.035 18000 3.3 0.33 4.712389 0.072861 0.81989 3.13175

Table 9


m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)Wsx10-13

m3/N-m

1.682 1.667 0.015 3600 4.7 0.47 1.884956 0.078065 1.756908 3.3554471.682 1.653 0.029 7200 5.5 0.55 3.769911 0.075463 1.698344 3.2435991.682 1.638 0.044 10800 5.6 0.56 5.654867 0.076331 1.717866 3.2808811.682 1.623 0.059 14400 5.5 0.55 7.539822 0.076764 1.727626 3.2995231.682 1.608 0.074 18000 5.3 0.53 9.424778 0.077025 1.733482 3.310708

Table 10


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m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)Wsx10-13

(m3/N-m)

1.494 1.466 0.028 3600 4.6 0.46 2.827433 0.097148 3.279561 4.1756671.494 1.444 0.05 7200 5.2 0.52 5.654867 0.086739 2.92818 3.7282741.494 1.426 0.068 10800 5.4 0.54 8.4823 0.078644 2.654883 3.3803021.494 1.402 0.092 14400 5.6 0.56 11.30973 0.0798 2.693925 3.4300121.494 1.378 0.116 18000 5.8 0.58 14.13717 0.080494 2.717351 3.459839

Table 11

4.1.4 Wear tests on Al+10% red mud (micro):

Wear tests are also conducted on Al+10% red mud (micro) samples using various loads ie.,

10N, 20N, 30N at different speeds of 200 RPM, 400 RPM, 600 RPM and a few results are

tabulated below


m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.416 1.408 0.008 3600 4.2 0.42 0.942478 0.08327 0.906805 3.4637421.416 1.402 0.014 7200 3.8 0.38 1.884956 0.072861 0.793455 3.0307741.416 1.396 0.02 10800 4.6 0.46 2.827433 0.069392 0.755671 2.8864511.416 1.388 0.028 14400 4.4 0.44 3.769911 0.072861 0.793455 3.0307741.416 1.38 0.036 18000 4.3 0.43 4.712389 0.074943 0.816125 3.117367

Table 12


m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)Wsx10-13

m3/N-m

1.452 1.435 0.017 3600 7.2 0.72 1.884956 0.088474 1.926962 3.6802251.452 1.423 0.029 7200 5.8 0.58 3.769911 0.075463 1.643585 3.1390161.452 1.411 0.041 10800 6.4 0.64 5.654867 0.071126 1.549126 2.9586131.452 1.396 0.056 14400 7.4 0.74 7.539822 0.072861 1.58691 3.0307741.452 1.382 0.07 18000 6.8 0.68 9.424778 0.072861 1.58691 3.030774

Table 13


16

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.458 1.432 0.026 3600 5.9 0.59 2.827433 0.090209 2.947118 3.7523871.458 1.41 0.048 7200 6.2 0.62 5.654867 0.08327 2.720416 3.4637421.458 1.388 0.07 10800 6.4 0.64 8.4823 0.080957 2.644849 3.3675271.458 1.362 0.096 14400 7.2 0.72 11.30973 0.08327 2.720416 3.4637421.458 1.342 0.116 18000 6.8 0.68 14.13717 0.080494 2.629736 3.348284

Table 14

4.1.5 Wear tests on Al+10% red mud (nano):

Wear tests are also conducted on Al+10% red mud (nano) samples using various loads ie.,

10N, 20N, 30N at different speeds of 200 RPM, 400 RPM, 600 RPM and a few results are

tabulated below


m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

0.97 0.964 0.006 3600 3.7 0.37 0.942478 0.062452 0.685937 2.6200870.97 0.96 0.01 7200 4.3 0.43 1.884956 0.052044 0.571614 2.1834060.97 0.956 0.014 10800 4.7 0.47 2.827433 0.048574 0.533507 2.0378460.97 0.95 0.02 14400 1.2 0.12 3.769911 0.052044 0.571614 2.1834060.97 0.94 0.03 18000 2.9 0.29 4.712389 0.062452 0.685937 2.620087

Table 15


m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)Wsx10-13

m3/N-m

0.941 0.939 0.002 3600 1.38 0.138 1.884956 0.010409 0.228646 0.4366810.941 0.935 0.006 7200 1.6 0.16 3.769911 0.015613 0.342969 0.6550220.941 0.929 0.012 10800 2.2 0.22 5.654867 0.020817 0.457292 0.8733620.941 0.914 0.027 14400 1.7 0.17 7.539822 0.035129 0.771679 1.4737990.941 0.909 0.032 18000 1.5 0.15 9.424778 0.033308 0.731666 1.39738

Table 16


17

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.201 1.195 0.006 3600 2.5 0.25 2.827433 0.020817 0.685937 0.8733621.201 1.193 0.008 7200 2.1 0.21 5.654867 0.013878 0.457292 0.5822421.201 1.189 0.012 10800 2.3 0.23 8.4823 0.013878 0.457292 0.5822421.201 1.185 0.016 14400 2.2 0.22 11.30973 0.013878 0.457292 0.5822421.201 1.183 0.018 18000 2 0.2 14.13717 0.01249 0.411562 0.524017

Table 17

The plots drawn between wear rate and sliding distance at different loads and sliding speeds

for various materials are shown in Fig. 9-17.

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

Al5% Micro5 % Nano10% Micro10% Nano

Sliding Distance x 103 m

Wea

r rat

e x 10

-6 N

/m

Fig. 9. Plots showing wear rate at 10 N and 200 rpm

Load : 10NSpeed 200 RPM


18

1 2 3 4 5 6 7 8 9 100

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18 Al5% Micro5 % Nano10% Micro10% Nano


Wea

r rat

e x 1

0-6

N/m


2 4 6 8 10 12 14 160

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18



Wea

r rat

e x 1

0-6

N/m




19

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.05

0.1

0.15

0.2

0.25

0.3

0.35



Wea

r rat

e x 1

0-6

N/m


1 2 3 4 5 6 7 8 9 100

0.05

0.1

0.15

0.2

0.25

0.3



Wea

r rat

e x 1

0-6

N/m




20

2 4 6 8 10 12 14 160

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45



Wea

r rat

e x 1

0-6

N/m


0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.1

0.2

0.3

0.4

0.5

0.6Al5% Micro5 % Nano10% Micro10% Nano


Wea

r rat

e x 1

0-6

N/m



21

1 2 3 4 5 6 7 8 9 100

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45



Wea

r rat

e x 1

0-6

N/m


2 4 6 8 10 12 14 160

0.1

0.2

0.3

0.4

0.5



Wea

r rat

e x 10

-6 N

/m




22

From the above plots, it is clear that at constant sliding speed and constant load the wear rate

is decreased with the increased fraction of red mud particles in the composite. From the

above investigations, significant increase in wear resistance is observed with the addition of

nano structured remud particles compared to that of micro structured redmud particles in the

composites. The addition of 5% weight fraction of nano redmud particles have given

improved wear resistance than 10% weight fraction of micro structured redmud particles.

Highly significant improvement in the wear resistance is observed in the case of composites

with 10% weight fraction of nano redmud particles. The surface properties of red mud

changes considerably with increased ball milling and hence the surface energy and inter

atomic bonding will also increase.

4.2 Deformation Studies

4.2.1 Effect of deformation on wear rate (Al+5% micro red mud)

Upset tests are conducted on Al+5% red mud (micro) samples using compression

testing machine and 10%, 20%, 30% and 40% deformations are obtained. Wear tests are

conducted for the above samples with various loads ie., 10N, 20N, 30N at different speeds of

200 RPM, 400 RPM, 600 RPM and a few results are tabulated below.


Deformation – 10%

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.408 1.397 0.011 3600 6.9 0.69 0.942478 0.114496 1.257599 4.8036731.408 1.389 0.019 7200 6.8 0.68 1.884956 0.098883 1.086108 4.1486261.408 1.38 0.028 10800 7.2 0.72 2.827433 0.097148 1.067053 4.0758431.408 1.372 0.036 14400 8.2 0.82 3.769911 0.093679 1.028944 3.9302781.408 1.361 0.047 18000 6.5 0.65 4.712389 0.097842 1.074675 4.104957

Table 18

23


Deformation – 10%

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)Wsx10-13

m3/N-m

1.495 1.472 0.023 3600 7.8 0.78 1.884956 0.1197 2.629524 5.0220211.495 1.454 0.041 7200 5.9 0.59 3.769911 0.10669 2.343706 4.4761491.495 1.44 0.055 10800 7.2 0.72 5.654867 0.095413 2.095998 4.003061.495 1.421 0.074 14400 6.4 0.64 7.539822 0.096281 2.115052 4.0394521.495 1.401 0.094 18000 6.5 0.65 9.424778 0.097842 2.14935 4.104957

Table 19


Deformation – 10%

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.456 1.424 0.032 3600 6.4 0.64 2.827433 0.111026 3.658468 4.6581071.456 1.398 0.058 7200 5.4 0.54 5.654867 0.100618 3.315487 4.2214091.456 1.37 0.086 10800 5.8 0.58 8.4823 0.099461 3.277378 4.1728871.456 1.337 0.119 14400 6.4 0.64 11.30973 0.10322 3.401232 4.3305841.456 1.302 0.154 18000 6.8 0.68 14.13717 0.106863 3.521276 4.483428

Table 20

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0% deformation10% deformation20% deformation30% deformation40% deformation

Sliding distance x 103 m

Wea

r rat

e x 10

-6 N

/m

Fig. 18. Plots showing wear rate at 10 N and 200 rpm (Al + 5% Micro RM)

Al + 5% Micro RMLoad : 10NSpeed : 200 RPM

24

1 2 3 4 5 6 7 8 9 100

0.02

0.04

0.06

0.08

0.1

0.12

0.14



Wea

r rat

e x 10

-6 N

/m


2 4 6 8 10 12 14 160

0.02

0.04

0.06

0.08

0.1

0.12

0.14



Wea

r rat

e x 10

-6 N

/m




25

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.05

0.1

0.15

0.2

0.25

0.3



Wea

r rat

e x 10

-6 N

/m


1 2 3 4 5 6 7 8 9 100

0.05

0.1

0.15

0.2

0.25

0.3



Wea

r rat

e x 1

0-6

N/m




26

2 4 6 8 10 12 14 160

0.05

0.1

0.15

0.2

0.25

0.3

0.35



Wea

r rat

e x 1

0-6

N/m


0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5



Wea

r rat

e x 1

0-6

N/m




27

1 2 3 4 5 6 7 8 9 100

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45



Wea

r rat

e x 10

-6 N

/m


2 4 6 8 10 12 14 160

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5



Wea

r rat

e x 1

0-6

N/m




28

4.2.2 Effect of deformation on wear rate (Al+5% nano red mud)

Upset tests are conducted on Al+5% red mud (nano) samples using compression



200 RPM, 400 RPM, 600 RPM and a few results are tabulated below.


Deformation – 10%

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.482 1.474 0.008 3600 6.9 0.69 0.942478 0.08327 0.937018 3.5791431.482 1.468 0.014 7200 6.8 0.68 1.884956 0.072861 0.81989 3.131751.482 1.462 0.02 10800 7.2 0.72 2.827433 0.069392 0.780848 2.9826191.482 1.455 0.027 14400 8.2 0.82 3.769911 0.070259 0.790609 3.0199021.482 1.447 0.035 18000 6.5 0.65 4.712389 0.072861 0.81989 3.13175

Table 21


Deformation – 10%

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)Wsx10-13

m3/N-m

1.485 1.47 0.015 3600 7.8 0.78 1.884956 0.078065 1.756908 3.3554471.485 1.456 0.029 7200 5.9 0.59 3.769911 0.075463 1.698344 3.2435991.485 1.441 0.044 10800 7.2 0.72 5.654867 0.076331 1.717866 3.2808811.485 1.426 0.059 14400 6.4 0.64 7.539822 0.076764 1.727626 3.2995231.485 1.412 0.073 18000 6.5 0.65 9.424778 0.075984 1.710057 3.265968

Table 22


Deformation – 10%

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.478 1.453 0.025 3600 5.8 0.58 2.827433 0.086739 2.92818 3.7282741.478 1.432 0.046 7200 6.2 0.62 5.654867 0.0798 2.693925 3.4300121.478 1.411 0.067 10800 6.4 0.64 8.4823 0.077487 2.615841 3.3305921.478 1.391 0.087 14400 5.8 0.58 11.30973 0.075463 2.547516 3.2435991.478 1.367 0.111 18000 6.6 0.66 14.13717 0.077025 2.600224 3.310708

Table 23

29

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09



Wea

r rat

e x 1

0-6

N/m

Fig. 27. Plots showing wear rate at 10 N and 200 rpm (Al + 5% Nano RM)

1 2 3 4 5 6 7 8 9 100

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09



Wea

r rat

e x 10

-6 N

/m


Al + 5% Nano RMLoad : 10NSpeed : 200 RPM


30

2 4 6 8 10 12 14 160

0.02

0.04

0.06

0.08

0.1

0.12



Wea

r rat

e x 1

0-6

N/m


0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.05

0.1

0.15

0.2

0.250% deformation10% deformation20% deformation30% deformation40% deformation


Wea

r rat

e x 1

0-6

N/m




31

1 2 3 4 5 6 7 8 9 100

0.05

0.1

0.15

0.2

0.25

0.3



Wea

r rat

e x 10

-6 N

/m


2 4 6 8 10 12 14 160

0.05

0.1

0.15

0.2

0.25

0.3

0.35



Wea

r rat

e x 10

-6 N

/m




32

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.05

0.1

0.15

0.2

0.25

0.3

0.35



Wea

r rat

e x 1

0-6

N/m


1 2 3 4 5 6 7 8 9 100

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4



Wea

r rat

e x 10

-6 N

/m




33

2 4 6 8 10 12 14 160

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4



Wea

r rat

e x 10

-6 N

/m


4.2.3 Effect of deformation on wear rate (Al+10% micro red mud)

Upset tests are conducted on Al+10% red mud (micro) samples using compression



200 RPM, 400 RPM and 600 RPM and the results are tabulated below.


Deformation – 10%

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.408 1.4 0.008 3600 6.9 0.69 0.942478 0.08327 0.906805 3.4637421.408 1.394 0.014 7200 6.8 0.68 1.884956 0.072861 0.793455 3.0307741.408 1.388 0.02 10800 7.2 0.72 2.827433 0.069392 0.755671 2.8864511.408 1.381 0.027 14400 8.2 0.82 3.769911 0.070259 0.765117 2.9225321.408 1.373 0.035 18000 6.5 0.65 4.712389 0.072861 0.793455 3.030774

Table 24


34


Deformation – 10%

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)Wsx10-13

m3/N-m

1.454 1.438 0.016 3600 6.8 0.68 1.884956 0.08327 1.813611 3.4637421.454 1.425 0.029 7200 7.2 0.72 3.769911 0.075463 1.643585 3.1390161.454 1.412 0.042 10800 5.4 0.54 5.654867 0.072861 1.58691 3.0307741.454 1.4 0.054 14400 6.4 0.64 7.539822 0.070259 1.530234 2.9225321.454 1.386 0.068 18000 6.6 0.66 9.424778 0.070779 1.541569 2.94418

Table 25


Deformation – 10%

m1

(gm)m2


(N) µR.D x 103

(m)

Wrx10-6

(N/m)Wvx10-12

(m3/sec)

Wsx10-13

(m3/N-m)

1.485 1.458 0.027 3600 6.4 0.64 2.827433 0.093679 3.060468 3.8967091.485 1.437 0.048 7200 6.2 0.62 5.654867 0.08327 2.720416 3.4637421.485 1.415 0.07 10800 6.3 0.63 8.4823 0.080957 2.644849 3.3675271.485 1.392 0.093 14400 6.8 0.68 11.30973 0.080668 2.635403 3.35551.485 1.368 0.117 18000 5.4 0.54 14.13717 0.081188 2.652406 3.377148

Table 26

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09



Wea

r rat

e x

10-6

N/m



35

1 2 3 4 5 6 7 8 9 100

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1



Wea

r rat

e x 10

-6 N

/m


2 4 6 8 10 12 14 160

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0% deformation10% deformation20% deformation30% deformation


Wea

r rat

e x 1

0-6

N/m




36

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.05

0.1

0.15

0.2

0.25



Wea

r rat

e x 1

0-6

N/m


1 2 3 4 5 6 7 8 9 100

0.05

0.1

0.15

0.2

0.25

0.3



Wea

r rate

x 10

-6 N

/m




37

2 4 6 8 10 12 14 160

0.05

0.1

0.15

0.2

0.25

0.3



Wea

r rat

e x 1

0-6

N/m


0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.05

0.1

0.15

0.2

0.25

0.3

0.35



Wea

r rat

e x 1

0-6

N/m




38

1 2 3 4 5 6 7 8 9 100

0.05

0.1

0.15

0.2

0.25

0.3

0.35



Wea

r rat

e x 1

0-6

N/m


2 4 6 8 10 12 14 160

0.05

0.1

0.15

0.2

0.25

0.3

0.35



Wea

r rat

e x 10

-6 N

/m




39

The deformation studies have shown that, for all the compositions, the wear resistance

is increased with deformation. This could be due to the increased dislocation density with

increased deformation. The strain hardening increases with the increased deformation and

caused for the reduced wear. The decreased wear indicates that the addition of red mud to

aluminium matrix caused for the increase in ductility and no brittleness enhancement.

4.2.4 Effect of deformation on wear rate (Al+10% nano red mud):

The hardness of the pure aluminium as well as Al-Red mud composites was increased with

increase in deformation. This might be due to the existence of strain hardening effects form

matrix materials and also from the rule of mixture of composite strengthening (Callister,

2007). The coefficient of friction in all cases decreases with the increase of normal load. This

decrease in value occurs likely as a result of particulate standing above the surface making

contacting area of the specimen smaller. In addition, this decrease in coefficient of friction

may be attributed to the wear of the matrix from the pin surface leaving the particulates

standing proud. A similar change in coefficients of friction has been observed by M.H.

Korkut (2003) for the newly developed Al 2024 composites. The effect of deformation on

wear rate with sliding distance at various loads and sliding velocities are furnished in Fig 45.

1 2 3 4 5 6 7 8 9 100

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0% 10%

20% 30%

40%


Wear r

ate x 1

0-6 N/

m

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0% 10%

20% 30%

40%


Wear r

ate x 1

0-6 N/

m

40

2 4 6 8 10 12 14 160

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0% 10%

20% 30%

40%


Wear r

ate x 1

0-6 N/

m

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.05

0.1

0.15

0.2

0.25

0% 10%

20% 30%

40%


Wear

rate x

10-6

N/m

1 2 3 4 5 6 7 8 9 100

0.05

0.1

0.15

0.2

0.25

0% 10%

20% 30%

40%


Wear r

ate x 1

0-6 N/

m

2 4 6 8 10 12 14 160

0.05

0.1

0.15

0.2

0.25

0% 10%

20% 30%

40%


Wear r

ate x 1

0-6 N/

m

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.05

0.1

0.15

0.2

0.25

0.3

0% 10%

20% 30%

40%


Wear r

ate x 1

0-6 N/

m

1 2 3 4 5 6 7 8 9 100

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0% 10%

20% 30%

40%


Wear

rate x

10-6

N/m

41

2 4 6 8 10 12 14 160

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0% 10%

20% 30%

40%


Wear r

ate x 1

0-6 N/

m

Fig 45. The effect of deformation on wear rate with sliding distance

4.3 Microstructural observations:

The worn-out surfaces of some selected specimens after the wear test are observed under

optical microscope and are shown in Fig 46 and 47. It is observed that cavities are formed in

the matrix of the composite and have aligned parallel to the direction of sliding during slow

sliding velocity i.e. at 200 RPM. The amount of cavities is reduced with increase in sliding

velocities and minimum cavities are observed with sliding velocity 600RPM. In some

regions, the substructures are aligned parallel to the sliding direction. The worn surfaces at

higher sliding speeds are relatively smoother than at lower sliding speeds. Cracks have

appeared for the same sliding speed with increased load and these might have helped in

chipping of hard red mud particles. With increase in applied load although the amount of

cavitations appears to be low but deep cracks and grooves are clearly visible in the optical

micrographs at higher loads. The optical micrographs depict that when the sample is rubbed,

against steel wheel, at low sliding speed and low applied load hard particles might have

chipped off and the aluminium grains are grown into bigger sizes with increase in applied

load and the aluminium matrix appears to be smeared along the direction of the sliding. From

the micrograph, it is seen that some cracks are originated at the grain boundaries of

aluminium. This might be due to (Savchenko et al., 2002) strain hardening of aluminium

during sliding with the applied load and due to pulling up of hard red mud particles from the

aluminium grain boundaries (Korkut, 2004). With increasing the applied load this effect is

more pronounced. This might have been caused also due to embrittlement of hard particles

during sliding.

42

Fig 46. Optical micrographs of Al-10% nano structured red mud composite after wear tests at

10N load (a) 200 RPM (b) 400 RPM (c) 600 RPM

10

c

a b

10µ

10µ

a

10µ

43

Fig. 47 Optical micrographs of Al-10% nano structured red mud composite after wear tests

at 600 RPM speed (a) 10N (b) 20N (c) 30N

4.4. Regression Modelling

Polynomial additive and multiplicative models were tried and the following model has been

developed using regression analysis.

Vol .Wear=F .V s ( A0+ A1 C+ A2 D+ A3 C2+ A4 D 2) (2)

Where F is the load applied, Vs is the sliding velocity, C is the percent composition of

redmud and D is the percent deformation.

The consistency and fitness with the experimental data was tested using R-Software. The

following empirical values were obtained for the above model.

A0 = 0.6257456, A1 = -0.0548196, A2 = -0.00390050, A3 = 0.00247220, A4 = 0.00006446

The model fitness is measured with R square value and accuracy of forecast is measured with

mean absolute percent error (MAPE). The R square value for the above model is 0.9775 and

MAPE is 12.96%. If MAPE calculated is less than 10%, it is interpreted as excellent accurate

forecasting, between 10-20% good forecasting, between 20-50% acceptable forecasting and

over 50% inaccurate forecasting [20]. An R square value of 0.9 or above is very good, a value

above 0.8 is good, and a value of 0.6 or above may be satisfactory in some applications [21].

10µ

c

10µ

b

44

The R square and MAPE values obtained are in the well acceptable range and hence the

present model can be adapted effectively.

4.5. Artificial neural network modelling

The main components of artificial neuron are, weights, addition function, activation

function and outputs and is represented in figure 48. Input data can be obtained from external

environment or the other artificial neurons. The quantities (w ij) demonstrate the effect of a

data point when it arrives at artificial neural cell. The addition function net ij calculates the net

input on a neural cell.

net j=∑i=1

n

x iW ij−θ i ..... (3)

Where Ɵi is the threshold value of ith process element, xi indicates the i input, wij is the

connection weight from j element to i element.

The artificial neuron output value, which depends on the selected activation function employs

a sigmoid function as the activation function and is calculated using eq.4

y j=f (net j )=1

1+e−net j ..... (4)

The sigmoid function is the most common activation function in the ANN because it

combines nearly linear behaviour, curvilinear behaviour, and nearly constant behaviour

[22,23].

Fig. 48 Mathematical model of neuron cell

45

In the present investigations, volumetric wear of the composite is modelled using ANN.

Deformation, Composition, Load and sliding velocity are taken as the inputs and the

volumetric wear is the output for the model. The experimental data is grouped into training

data and test data. The training data is used for training the ANN and the test data is used for

validating the ANN. Root mean square error and mean absolute percentage error is

calculated using the following equations.

RMSE=√ 1N ∑

i=1

N

(t i−tdi)2 (5)

MAPE= 1N (∑i=1

N [|t i−tdi

t i |])X 100 ...... (6)

Where ti is the experimental value and tdi is the model prediction value and N is the number

of testing data.

The ANN architecture used for modelling is shown in fig. 49 with four input variables, two

hidden layers with 7 and 6 neurons in the two hidden layers respectively and volumetric wear

as output variable.

Fig. 49 Artificial Neural network arcitecture

4.5.1 Network training and testing

A forward feed backward propagation multilayer ANN is used for modelling and the network

training and testing was carried out using MATLAB. The hyperbolic tangent sigmoid

function (tansig) eq no.4 and the linear transfer function (purelin) are used as activation

46

transfer functions. The back propagation function that updates weight and bias values

according to Levenberg-marcendet optimization (trainlm) is used as training algorithm due to

its high accuracy in prediction and fast convergence. The experiments in the present

investigations have yielded 144 results, out of which 124 were used for training the network

and 20 were used to test the ANN model. Different network configurations are evaluated by

varying the number of neurons in the hidden layers between 2 and 20. The mean absolute

percent error (MAPE) and Root mean square error and correlation coefficient were used to

evaluate the performance of ANN model for prediction. The model with 7 and 6 neurons in

the hidden layer resulted in MAPE of 7.30%, correlation coefficient of 0.989 and RMSE of

0.3177 which are in the well acceptable range and hence the model with above combination

of neurons is selected. The regression analysis of the selected ANN model is furnished in

figure 50.

0 1 2 3 4 5 6 7 8 90

1

2

3

4

5

6

7

8

9

10

Experimental value

ANN m

odel va

lue

Fig. 50 Regression analysis of the ANN model

The regression and ANN models were adapted for the experimental results and a comparative

study is made. The error percentage is calculated with respect to experimental results and it is

observed that both the models are in consistent with experimental results. MAPE and RMSE

values reflect that the ANN model is more accurate as compared to regression model. The

results obtained from the comparative study are depicted in table 27 and its graphical

representation is shown in figure 51.

47

Table. 27: Experimental data and predicted values using regression model and ANN model

percent

deformat

ion

%

compos

ition load

sliding

velocity

m/sec

Volumetric wear x 10-12 m3/sec

experime

ntal

value

reg

model

%

error

ann

model

%err

or

10 10 20 0.523599 3.521145 3.060027 13.10 3.563598 -1.21

30 5 20 0.523599 3.607518 3.711845 -2.89 3.423788 5.09

10 15 30 0.523599 4.834279 5.138675 -6.30 4.55278 5.82

0 10 20 0.261799 1.60052 1.70049 -6.25 1.58974 0.67

40 5 20 0.523599 3.654368 3.775931 -3.33 3.623701 0.84

30 10 30 0.261799 1.737708 2.08737 -20.12 1.880595 -8.22

0 5 30 0.523599 5.739232 6.494499 -13.16 5.829162 -1.57

10 15 10 0.785398 2.293472 2.569337 -12.03 2.435157 -6.18

10 5 30 0.261799 2.975031 2.991535 -0.55 2.672682 10.16

0 15 20 0.523599 3.777483 3.766736 0.28 3.868963 -2.42

0 0 10 0.523599 2.353275 3.276396 -39.23 2.251735 4.31

0 10 30 0.785398 7.911143 7.652206 3.27 7.944785 -0.43

40 15 20 0.785398 5.328949 4.819502 9.56 6.221719

-

16.75

20 5 10 0.785398 2.576798 2.837079 -10.10 2.714028 -5.33

30 5 30 0.785398 8.456583 8.351651 1.24 9.188548 -8.66

20 5 30 0.523599 5.996912 5.674159 5.38 5.929547 1.12

40 5 20 0.785398 5.692382 5.663896 0.50 5.414032 4.89

20 10 20 0.785398 5.075936 4.281131 15.66 4.795642 5.52

40 10 10 0.785398 2.034947 2.135434 -4.94 2.176135 -6.94

30 15 20 0.523599 3.327782 3.148916 5.37 3.349029 -0.64

MAPE 12.32091 7.294696

RMSE 0.358549 0.317371

48

0 5 10 15 20 250

1

2

3

4

5

6

7

8

9

10

expt

reg model

ann model

Samples

volu

met

ric w

ear

Fig. 51. Graphical representation of regression and ANN models.

5.0 CONCLUSIONS

Nano structured redmud is successfully synthesised by High energy Ball Milling.

Nano and micro structured redmud particle reinforced alumimium matrix composites

are synthesised successfully by stir casting route.

Improved Hardness is observed in the composites with increased redmud fraction.

The addition of nano redmud particles have shown improved hardness than the micro

structured redmud particle reinforced composites.

The composites with nano structured redmud particle reinforced composites have

shown significant improvement in wear resitance than the micro structured redmud of

same fraction.

About 60% inmprovement in wear resistance is observed between pure aluminium

and the composite with 10% nano redmud particle reinforcement.

Wear resitance has been significantly increased with increased deformation. The

wear loss remained constant for the composites with 30% and 40% deformation with

respect to sliding distance. The optimum results were obtained for the composites

with 10% nano redmud particle reinforcement.

Regression and ANN models have been successfully developed and have shown high

accuracy and consistency. It is also observed that the ANN model is more accurate

than the regression model.

49

The R square value and MAPE value obtained for the regression model are 0.9775

and 12.96% respectively, which are in the well acceptable range and hence the

developed regression model can be adapted effectively.

The ANN model with 7 and 6 neurons in the hidden layer resulted in MAPE of

7.30%, correlation coefficient of 0.989 and RMSE of 0.3177 which are in the well

acceptable range and hence can be adapted for the prediction of wear behaviour,

which considerably saves the project time, effort and cost

Acknowledgements

The authors sincerely express their thanks to the UGC for extending the financial support for

carrying the research work.

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List of Publications

1. G. Satyanarayana; G. Swami Naidu; N. Hari Babu, The effect of deformation on

wear behaviour of Al-5% red mud particle reinforced nano composites synthesised by

stir casting, Int. J. of Materials Engineering Innovation, Inderscience Publishers, 2016

Vol.7, No.2, pp.115 - 129

2. Satyanarayana G, Narayana Rao K, Swami Naidu G and Bhargava N R M R, “Nano

structured Red mud – Synthesis and XRD studies”, International Journal of

Mechanical Engineering and Material Sciences, Serials Publications, ISSN 0974-

584X, Volume 7, No. 1, pp.63-65

3. Satyanarayana G., Swami Naidu G., Hari Babu N., “Deformation behaviour of al-10

wt% red mud particle reinforced nano Composites – wear studies”, International

conference on Material Processing Technology MPT-2016, Organized at Pune, during

5-7, January 2016, pp. 13-20

http://www.inderscience.com/filter.php?aid=79555



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