Investigation Of Mechanical Properties And Electric Discharge Machining Of Aluminum Based Hybrid Composite Manufactured By Stir Cast

Composite materials play an important role in the field of engineering as well as advance manufacturing in response to unprecedented demands from technology due to rapidly advancing activities in aircrafts, aerospace and automotive industries. These materials have low specific gravity that makes their properties particularly superior in strength and modulus to many traditional engineering materials such as metals.
As above advantages we fabricated Al based metal matrix composite with chemical compositions (Si-.18 %, Fe-.60%, Cu-.075%, Mn-.030%, Zn-.060%,Ti-.010%, Cr-.005%) with two SiC (68 ??m size ) & Graphite (73 ??m size) reinforcements by liquid stir casting method.
Electric discharge machining is the most widely-used non-conventional machining process.EDM has been a mainstay of manufacturing for more than six decades, providing unique capabilities to machine 'difficult-to-machine' materials with desire shape, size, and required dimensional accuracy. It is essential especially in the machining of super tough, hard and electrically conductive materials
In present study we apply central composite design to optimize the number of experiments and try to optimize the responses like (MRR, TWR, SR) using Response surface methodology and produced mathematical model. Adequacy of mathematical model is checking using ANOVA with 95% of significant level.
15wt%SiC + 5wt%Gr hybrid composites material exhibit relatively higher hardness compared to others Al based hybrid composite materials i.e.5wt%SiC, 5wt%SiC + 5wt%Gr, 10wt%SiC + 5wt%Gr.The maximum tensile strength &density is found for 15wt%SiC + 5wt%Gr hybrid composite. The porosities of the obtained cast composite have found to be increased with the increase of SiC particulates. This is due to the vortex found because of the stirring action, which enhances the dissolution of gases and causes more bubbles to be formed inside the melt which decreases the hardness of Al-Gr Composites. The addition of 5wt% of graphite as a secondary reinforcement reduces the tensile strength slightly. This may due to the HCP structure of graphite which allows slipping of different layers at relatively low forces.
The MRR tends to increase significantly with the increase in Ip for any value of Ton. However, for any peak current value, MRR tends to increase first with increase Ton up to 40 ??s and then decreased with increased value of Ton. Hence, maximum MRR is obtained at high peak current. This is due to their dominant control over the input energy, i.e. with the increase in Ip generates strong spark.
It can be seen from the figure, the MRR tends to increase significantly with increasing Ip. The MRR increases with % Contribution up to 25 Amp. From 25 amp to 35 amp MRR going to decrease with increase in % Contribution
The TWR increases significantly with the increase in Ip. However, the TWR tends to increase with increase in Ton. Hence, maximum TWR is obtained at high peak current & Ton. Also below the 40 pulse on time & 15 Amp current ranges, TWR is slide increased significantly with decreased Ton & Ip.

The TWR increases with % Contribution up to 25 amp. From 25 amp to 35 amp MRR going to increased with decreased in % Contribution.
The SR increases significantly with the increase in Ip. However, the SR tends to increase with increase in Ton.
The SR tends to increase significantly with increasing Ip. The SR decreased with % Contribution increased. It is because, if % Contribution increased, the hardness properties of material as become increased.

Chapter-1 Introduction
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Chapter
1
INTRODUCTION
1.1 Background
Composite materials play an important role in the field of engineering as well
as advance manufacturing in response to unprecedented demands from technology
due to rapidly advancing activities in aircrafts, aerospace and automotive industries.
These materials have low specific gravity that makes their properties particularly
superior in strength and modulus to many traditional engineering materials such as
metals. As a result of intensive studies into the fundamental nature of materials and
better understanding of their structure property relationship, it has become possible to
develop new composite materials with improved physical and mechanical properties.
These new materials include high performance composites such as reinforced
composites. Continuous advancements have led to the use of composite materials in
more and more diversified applications. The importance of composites as engineering
materials is reflected by the fact that out of over 1600 engineering materials available
in the market today more than 200 are composite [1]. Processing of composite
materials, to required shape and size for their real application, demands for novel
machining method. Many conventional and non-conventional methods have been
identified to process particular composite. Among all EDM has been widely accepted
by the metal cutting industries for the machining of heat treated tool steel, high
strength alloys and carbides. EDM is also capable of machining ultra hard tool
materials such as polycrystalline diamond, CVD diamond; PVD coated cemented
carbide and certain ceramics.
1.2 Electric Discharge Machining (EDM)
EDM has been a mainstay of manufacturing for more than six decades,
providing unique capabilities to machine 'difficult-to-machine' materials with desire
shape, size, and required dimensional accuracy. Its distinctive attribute of using
thermal energy to machine electrically conductive materials, regardless of hardness,
Chapter-1 Introduction
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has been an advantage in the manufacturing of mould, die, surgical, automotive and
aeronautic components. It is essential especially in the machining of super tough, hard
and electrically conductive materials such as the new space age alloys. It is better than
other machining processes in terms of precision, quality characteristics and the fact
that hardness and stiffness of a work piece material is not important for the material
removal. Though EDM has become an established technology, and commonly used in
manufacturing of mechanical works, yet its low efficiency and poor surface finish
have been the vital matter of concern. Hence, the investigations and improvements of
the process are still going on, since no such process exists, which could successfully
replace the EDM.
The working principle of EDM process can be understood from Figure 1. Dielectric
flows through the gap between the electrodes (usually with the tool as the
cathode and the work piece as the anode), which are connected to a pulsed directcurrent
(DC) power supply. This produces sparks between the electrodes which melt
and sometimes vaporize material from both the tool and the work piece. Figure 1 also
shows an inter-electrode gap between the tool and the work piece in which dielectric
is flushed at high pressure. Once the power supply is on, the capacitor keeps charging
until the breakdown voltage (Vb) is attained and then sparking takes place at a point of
least electrical resistance. After each discharge, the capacitor recharges and the spark
energy is shared mainly by work-piece, tool, dielectric and debris (removed material)
[2].
Figure1.1: Electric discharge machining using relaxation circuit.
Chapter-1 Introduction
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1.3 Mechanism of Material Removal in EDM
Electrical discharge machining is the most widely-used non-conventional
machining process. Despite the fact that the mechanism of material removal of EDM
process is not yet completely understood and is still debatable, the most widely
established principle is the conversion of electrical energy into thermal energy
through a series of discrete electrical discharges occurring between the electrode and
work piece immersed inside a dielectric medium and separated by a small gap.
Material is removed from the work piece by localized melting and even vaporization
of material. The sparks are created in between two electrodes in presence of dielectric
liquid. The spark radius is usually very small (100'200 ??m) [3]. However the spark
energy density is very high, hence the electrode's material melts and vaporizes in the
localized area. The craters formed in this way spread over the entire surface of the
work-piece under the tool. The cavity produced in the work piece is approximately the
replica of the tool. However, tool wear should be minimized by selecting optimum
machining parameters and appropriate polarity. The material eroded from the
electrodes is known as debris, which is a mixture of irregularly shaped particles and
spherical particles. A very small gap (usually ' 100 ??m) between the two electrodes is
maintained with the help of a servo system.
To enhance the capabilities of EDM process, hybrid EDM processes have
been developed. For example, electric discharge diamond grinding (EDDG ' EDM +
grinding) [4] and ultrasonic-assisted EDM [5] are two such hybrid EDM processes.
EDDG of very hard materials such as WC is advantageous because electric discharges
thermally soften work material, thus facilitating grinding. Continuous in-process
dressing of grinding wheel gives stable grinding performance [2]. It is observed that
specific energy in EDDG decreases with increasing pulse current.
1.4 EDM process parameters
As per the discharge phenomena explained earlier, some of the important
process parameters which influence the responses are:
Discharge current (Ip): It is the most important machining parameter in EDM
because it relates to power consumption of power while machining. The current
increases until it reaches a preset level which is expressed as discharge current.
Chapter-1 Introduction

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Discharge voltage (V): It is the open circuit voltage which is applied between the
electrodes. The discharge voltage de-ionizes the dielectric medium, which depends
upon the electrode gap and the strength of the dielectric, prior to the flow of current.
Once the current flow starts, the open circuit voltage drops and stabilizes the electrode
gap. It is a vital factor that influences the spark energy.
Pulse-on time (Ton): It is the time during which actual machining takes place and it is
measured in ??s. In each discharge cycle, there is a pulse on time and pause time/Pulse
off time, and the voltage between the electrode and workpiece is applied during Ton
duration. The longer the pulse duration higher will be the spark energy that creates
wider and deeper crated.
Pulse-off time or pause time (Toff): In a cycle, there is a pulse off time or pause time
during which the supply voltage is cut off as a consequence the Ip diminishes to zero.
It is also the duration of time after which the next spark is generated and is expressed
in ??s analogous to Ton.
Since, the dielectric must de-ionized after sparking and regain its strength, it required
some time and moreover the flushing of debris also takes place during the Toff time.
Duty cycle (??): It is the ratio of pulse on-time and the pulse period. It is expressed in
%. Duty cycle is defined in the following equation.
Flushing Pressure (fp): Flushing is an important factor in EDM because debris must
be removed for efficient cutting, moreover it brings fresh dielectric in the inter
electrode gap. Flushing is difficult if the cavity is deeper, inefficient flushing may
initiate arcing and may create unwanted cavities which can destroys the work piece.
There are several methods generally used to flush the EDM gap like jet or side
flushing, pressure flushing, vacuum flushing and pulse flushing.
Polarity: Polarity refers to the potential of the work piece with respect to tool i.e. in
straight or positive polarity the work piece is positive, whereas in reverse polarity
work piece is negative. Varying the polarity can have dramatic effect, normally
electrode with positive polarity wear less, whereas with negative polarity cut faster.
Chapter-1 Introduction
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1.5 Process Capabilities and Applications
EDM can be used only for electrically conductive materials, and its
performance is not substantially affected by mechanical, physical and metallurgical
properties of work piece material. It can perform various kinds of operations such as
drilling, cutting, 3D shaping and sizing (wire EDM) and spark-assisted grinding
(EDDG). It gives good repeatability and accuracy of the order of 25'125 ??m. The
tolerances that can be achieved are ??2.5 ??m. This has been used to produce as good as
100:1 aspect ratio holes. Under normal conditions, the volumetric material removal
rate (MRR) is in the range of 0.1'10 mm3/min. The surface finish produced during
EDM is usually in the range 0.8'3 ??m, depending upon the machining conditions
used. The machined surface normally has a recast layer which should be removed
before fitting the part into the assembly or sub-assembly. It is commonly used for
making hardened steel dies and moulds and has numerous applications in various
types of industries [2].
1.6 Composites
Many of our modern technologies require materials with unusual
combinations of properties that cannot be met by the conventional metal alloys,
ceramics, and polymeric materials. This is especially true for materials that are needed
for aerospace, underwater, and transportation applications. For example, aircraft
engineers are increasingly searching for structural materials that have low densities,
are strong, stiff, and abrasion and impact resistant, and are not easily corroded. This is
a rather formidable combination of characteristics. Frequently, strong materials are
relatively dense; also, increasing the strength or stiffness generally results in a
decrease in impact strength [6].
The typical composite materials are engineered or naturally occurring
materials made from two or more constituent materials with significantly different
physical or chemical properties which remain separate and distinct at the macroscopic
or microscopic scale within the finished structure. The constituents retain their
identities, that is, they do not dissolve or merge completely into one another although
they act in concert. The individual materials that make up composites are called
constituents. Most composites have two constituent materials: a binder or matrix
Chapter-1 Introduction
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(polymers, metals, or ceramics) and reinforcement (fibers, particles, flakes, and/or
fillers).
The reinforcement is usually much stronger and stiffer than the matrix, and
gives the composite its good properties. The matrix holds the reinforcements in an
orderly pattern. Because the reinforcements are usually discontinuous, the matrix also
helps to transfer load among the reinforcements.
Material property combinations and ranges have been, and are yet being,
extended by the development of composite materials. Generally speaking, a
composite is considered to be any multiphase material that exhibits a significant
proportion of the properties of both constituent phases such that a better combination
of properties is realized. According to this principle of combined action, better
property combinations are fashioned by the judicious combination of two or more
distinct materials. Property trade-offs are also made for many composites.
Composites of sorts include multiphase metal alloys, ceramics, and polymers.
For example, pearlitic steels have a microstructure consisting of alternating layers of ??
ferrite and cementite. The ferrite phase is soft and ductile, whereas cementite is hard
and very brittle. The combined mechanical characteristics of the pearlite (reasonably
high ductility and strength) are superior to those of either of the constituent phases.
There are also a number of composites that occur in nature. For example, wood
consists of strong and flexible cellulose fibers surrounded and held together by a
stiffer material called lignin. Also, bone is a composite of the strong yet soft protein
collagen and the hard, brittle mineral apatite.
A composite, in the present context, is a multiphase material that is artificially
made, as opposed to one that occurs or forms naturally. In addition, the constituent
phases must be chemically dissimilar and separated by a distinct interface. Thus, most
metallic alloys and many ceramics do not fit this definition because their multiple
phases are formed as a consequence of natural phenomena. In designing composite
materials, scientists and engineers have ingeniously combined various metals,
ceramics, and polymers to produce a new generation of extraordinary materials. Most
composites have been created to improve combinations of mechanical characteristics
such as stiffness, toughness, and ambient and high-temperature strength.
Chapter-1 Introduction
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Figure 1.2: Schematic representations of the various geometrical and spatial
characteristics of particles of dispersed phase that may influence the properties
of composites: (a) concentration (b) size (c) shape (d) distribution and (e)
orientation
Many composite materials are composed of just two phases; one is termed the
matrix, which is continuous and surrounds the other phase, often called the dispersed
phase. The properties of composites are a function of the properties of the constituent
phases, their relative amounts, and the geometry of the dispersed phase. ''Dispersed
phase geometry'' in this context means the shape of the particles and the particle size,
distribution, and orientation; these characteristics are represented in Figure 2.
One simple scheme for the classification of composite materials is shown in
Figure 3, which consists of three main divisions'particle-reinforced, fiberreinforced,
and structural composites; also, at least two subdivisions exist for each.
The dispersed phase for particle-reinforced composites is equiaxed (i.e., particle
dimensions are approximately the same in all directions); for fiber-reinforced
composites, the dispersed phase has the geometry of a fiber (i.e., a large length-todiameter
ratio). Structural composites are combinations of composites and
homogenous materials.
Chapter-1 Introduction
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Composites
Particle-Reinforced Fiber-Reinforced Structural
Large- Dispersion- Continuous Discontinuous Laminates Sandwich
Particle strengthen (aligned) (short) panels
Aligned Randomly
Oriented
Figure 1.3: A classification scheme for the various composite types
1.7 Metal Matrix Composites
As the name implies, for metal-matrix composites (MMCs), the matrix is a
ductile metal. These materials may be utilized at higher service temperatures than
their base metal counterparts; furthermore, the reinforcement may improve specific
stiffness, specific strength, abrasion resistance, creep resistance, thermal conductivity,
and dimensional stability. Some of the advantages of these materials over the
polymer- matrix composites include higher operating temperatures, non-flammability,
and greater resistance to degradation by organic fluids. Metal-matrix composites are
much more expensive than PMCs, and, therefore, their (MMC) use is somewhat
restricted.
The super alloys, as well as alloys of aluminum, magnesium, titanium, cobalt,
silver, nickel, and copper, are employed as matrix materials. The reinforcement may
be in the form of particulates, both continuous and discontinuous fibers, and whiskers;
concentrations normally range between 10 and 60 vol%. Continuous fiber materials
include carbon, silicon carbide, boron, alumina, and the refractory metals. On the
other hand, discontinuous reinforcements consist primarily of silicon carbide
whiskers, chopped fibers of alumina and carbon, and particulates of silicon carbide
and alumina. In a sense, the cermets fall within this MMC scheme. Figure 4 presents
the properties of several common metal matrix, continuous and aligned fiberreinforced
composites [6].
Chapter-1 Introduction
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Table 1.1: Properties of Several Metal-Matrix Composites Reinforced with
Continuous and Aligned Fibers
Fiber Matrix Fibre Content
(vol %)
Density (g/cm3) Longitudinal
Tensile
Modulus
(GPa)
Longitudinal
Tensile
Strength
(MPa)
Carbon
Boron
SiC
Alumina
Carbon
Borsic
6061 Al
6061 Al
6061 Al
380.0Al
AZ31 Mg
Ti
41
48
50
24
38
45
2.44
-
2.93
-
1.83
3.68
320
207
230
120
300
220
620
1515
1480
340
510
1270
1.8 Ceramic'Matrix Composites
Ceramic materials are inherently resilient to oxidation and deterioration at
elevated temperatures; were it not for their disposition to brittle fracture, some of
these materials would be ideal candidates for use in high temperature and severestress
applications, specifically for components in automobile and aircraft gas turbine
engines.
1.9 Applications
Composites are increasingly used in the aerospace industry, automotive
industry, sporting goods industry, marine applications, consumer goods, construction
and civil structures, industrial applications
Recently, some of the automobile manufacturers have introduced engine
components consisting of an aluminum-alloy matrix that is reinforced with alumina
and carbon fibers; this MMC is light in weight and resists wear and thermal distortion.
Aerospace structural applications include advanced aluminum alloy metal-matrix
composites; boron fibers are used as the reinforcement for the Space Shuttle Orbiter,
and continuous graphite fibers for the Hubble Telescope.
The high-temperature creep and rupture properties of some of the super alloys
(Ni- and Co-based alloys) may be enhanced by fiber reinforcement using refractory
metals such as tungsten. Excellent high-temperature oxidation resistance and impact
strength are also maintained. Designs incorporating these composites permit higher
operating temperatures and better efficiencies for turbine engines.
Light alloy composite materials have, in automotive engineering, a high
application potential in the engine area (oscillating construction units: valve train,
Chapter-1 Introduction
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piston rod, piston and piston pin; covers: cylinder head, crankshaft main bearing;
engine block: part-strengthened cylinder blocks
Chapter-2 Literature Review
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Chapter
2
LITERATURE REVIEW
2.1 Literature Review
The aims of this chapter are to exhibit and highlight work carried out by
various researchers and also to develop a basic knowledge to production of metal
matrix composite and electric discharge machining of composite materials and to
identify the research gap.
2.2 Composite Material Development and Testing.
Many researchers have tried to develop variety of composite materials during
past decade. Some of them have been reviewed here for the purpose of understanding
the technology and knowing various methods to fabricate composite materials.
Kaczmar et al. [7] discussed production methods and properties of metal
matrix composite materials reinforced with dispersion particles, non continuous
(short) and continuous (long) fibers. The most widely applied methods for the
production of composite materials and composite parts are based on casting
techniques such as the squeeze casting of porous ceramic preforms with liquid metal
alloys and powder metallurgy methods. Various MMC based on Magnesium,
Aluminum, Copper, Titanium, Nickel and their alloy as matrix material have been
discussed. On account of the excellent physical, mechanical and development
properties of composite materials, they are applied widely in aircraft technology and
electronic engineering, and recently in passenger-car technology also.
Basavarajappa et al. [8] prepared aluminum metal matrix composites
reinforced with SiC and graphite (Gr) particles by liquid metallurgy route. Dry sliding
wear behavior of the composite was tested and compared with Al/SiCp composite. A
plan of experiments based on Taguchi technique was used to acquire the data in a
controlled way. An orthogonal array and analysis of variance was employed to
investigate the influence of wear parameters like as normal load, sliding speed and
Chapter-2 Literature Review

Page | 12
sliding distance on dry sliding wear of the composites. The objective was to
investigate which design parameter significantly affects the dry sliding wear. The
incorporation of graphite particles in the aluminum matrix as a secondary
reinforcement increases the wear resistance of the material.
Rajan et al. [9] investigated the effect of three different stir casting routes on
the structure and properties of fine fly ash particles (13 ??m average particle size)
reinforced Al'7Si'0.35Mg alloy composite. In liquid metal stir casting, the
incorporation of fly ash particle into melt and pouring of composite melt into the
mould are carried out in a fully liquid state (i.e., above liquidus temperature of the
matrix alloy). In the case of compo casting process, both the incorporation and
pouring steps are carried out in a semisolid state (a temperature in between the solidus
(840 oK) and liquidus (888 oK) temperatures). In case of modified compo casting
process, particle addition and casting are carried out in between liquidus and solidus
temperatures and above liquidus temperature respectively. Among three, the modified
compo casting has resulted in a well-dispersed and relatively agglomerate and
porosity free fly ash particle dispersed composites. Interfacial reactions between fly
ash particle and matrix leading to formation of MgAl2O4 spinel and iron intermetallics
are more in liquid metal stir cast composites than in compo cast composites.
Kwak and Kim. [10] presented the fabrication by the pressure less infiltration
process under the nitrogen gas atmosphere, and the grinding of the aluminum-based
MMCs reinforced with SiC particles. The working conditions such as the contents of
the SiC and the Mg were examined how to influence the fabricating process of the
MMCs. The effect of the grinding parameters on the surface roughness and the
grinding forces was evaluated, and then the grinding parameters were optimized using
the S/N ratio. The second-order response surface models were developed and the
usefulness of the developed models was verified.
Taha et al. [11] studied the workability of aluminium'SiC and Al2O3-
reinforced metal matrix composites (APMMCs) prepared by stir-casting, squeezecasting
and powder metallurgy techniques by using up-set test. Different parameters
of APMMC were considered such as type of Al matrix alloy, type of particulate
reinforcement and particulate volume fraction and size. The up-set test was conducted
Chapter-2 Literature Review
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on cylindrical specimens with 8 mm diameter and 8 mm height, successively as
repeated compressions with intermediate heat treatment (IHT). Stir casting resulted in
a slightly better workability than that obtained in case of powder metallurgy, while
squeeze casting showed promising behavior in the case of composites with highparticulate
Vf. The highest workability index (WI), was obtained in case of APMMC
with wrought Al matrix and SiC reinforcement. Some rolling experiments were
additionally conducted on such composite, where WI estimated was compared to that
of the up-set tests.
Nofer et al. [12] used hot press method to fabricate Al/Al3Ti composite. In
situ reacted Al3Ti compound is formed through Al and TiO2 powder blend in order to
enhance mechanical properties and mainly wear resistance. After homogeneous
blending of Al and TiO2, hot pressing was performed at 580 oC at different durations
and pressures. Scanning electron microscopy was used for micro structural survey.
Density measurements using Archmede's method and Brinell hardness test were
carried out for all the samples. By increasing the hot pressing pressure, both density
and hardness increased significantly. On the other hand, by increasing the time,
hardness increased and density decreased due to the formation of Al3Ti. Samples
produced at higher pressures and longer durations had higher wear resistance at longer
wear distances. The three observed dominant types of wear mechanisms were
abrasive, delamination, and adhesive.
Table 2.1 presents a summary of the major studies conducted in the area of composite
material development and testing and the method adopted in each study.
Table2.1 Research efforts in Composite material development and Testing
Author(Year) Manufacturing
Method
Composite Material Test performed
Matrix Reinforcement
Kaczmar et al.
(2000)
Production methods of various MMC based on Magnesium, Aluminum,
Copper, Titanium, Nickel and their alloy as matrix material have been
reviewed.
Basavarajappa
et al. (2007)
liquid metallurgy
route
Al SiC and Gr
(Graphite)
Sliding Wear
Rajan et al.
(2007)
stir casting Al'
7Si'
0.35Mg
fine fly ash
particles
Casting made in fully liquid
state, semisolidstate, and in
between the liquidus and
solidus temperatures and above
the liquidus temperature
Chapter-2 Literature Review
Page | 14
The summary of the literature indicates that there are many processes using
which composite material can be fabricated or manufactured. Among these methods
Squeeze casting, Liquid metallurgy route, Stir casting, Pressure-less infiltration,
Powder metallurgy, Hot Pressing, and Centrifugal casting are widely used by
investigators. Composite materials reinforced by dispersion particles, platelets, noncontinuous
(short) fibres and continuous (long) fibres as well as composite materials
with hybrid reinforcement composed of particles and fibres are produced. The major
properties tested of fabricated composite material from application point of view are
Sliding wear, Fatigue stress, S/N ratio, Wear, Workability, Density, Hardness, etc.
2.3 Modelling of MRR, EWR and Surface Roughness in EDM.
Few researchers have tried to develop mathematical models for various
parameters of electric discharge machining process for composite materials. A review
of such modeling research has been presented here for the purpose of understanding
the methodology and different techniques to model a particular process.
Karthikeyan et al. [13] developed mathematical models for optimizing EDM
characteristics such as the MRR, the tool wear rate (TWR) and the surface roughness
(CLA value). The process parameters taken in to consideration were the current, the
pulse duration and the percent volume fraction of SiC (25 ??m size) present in LM25
aluminum matrix. A three level full factorial design was chosen for experimentation
and mathematical models with linear, quadratic and interactive effects of the
parameters chosen were developed. The MRR was found to decrease with an increase
in the percent volume of SiC, whereas the TWR and the surface roughness increase
with an increase in the volume of SiC.
Kwak and

Kim. (2008)
Pressure-less
infiltration
process
AC8A
Al
SiC, Mg Grinding force and SR ,
Hardness, RSM model.
Taha et al.
(2008)
stir-casting,
squeeze-casting,
powder
metallurgy
Al SiC and Al2O3
Particle
Workability
Nofer et al.
(2009)
Hot Pressing Al Al3Ti produced
via hot pressing
of (Al + TiO2)
Density, hardness, Wear
Chapter-2 Literature Review
Page | 15
Dhar et al. [14] developed mathematical modeling of cast Al'4Cu'6Si alloy'
10 wt.% SiCp composites. The objective of the work was to evaluate effect of current,
pulse on-time and air gap voltage on material removal rate, tool wear rate, and radial
overcut. The mathematical model developed can be used to predict the optimal
conditions suitable for machining of the work samples. Linear programming was used
to find the optimum conditions for maximum MRR with reduced TWR and radial
overcut. All the three performance measures increased significantly in a nonlinear
fashion with increase in current. The material removal rate and radial over cut were
found to increase with increase in pulse duration. Gap voltage was found to have
little, but some effect on the three responses.
Patel et al. [15] determined optimum parametric combination using surface
roughness prediction model for EDM of Al2O3/SiCw/TiC ceramic composite. The
machining performance during EDM of Al2O3/SiCw/TiC specimen of 20??20 mm and
5 mm thickness ceramic composite has been carried out. EDM parameters considered
are Discharge current, Pulse-on time, Duty cycle and Gap voltage. The pulse-on time
influences the surface roughness more predominantly than other factors. Surface
roughness increases first and then decreases with the increase in the duty cycle. Gap
voltage does not affect the quality of the surface appreciably. The experimental results
were used to develop the mathematical model using RSM and optimum parameters
for minimum surface roughness were identified.
Shandilya et al. [16] carried out investigation of the wire electric-discharge
cutting (WEDC) of 10% SiCp/ Aluminum 6061 metal matrix composite (MMC).
Response surface methodology (RSM) has been used to plan and analyze the
experiments. WEDC parameters namely servo voltage, pulse-on time, pulse-off time
and wire feed rate were varied to study their effect on the quality of cut in SiCp/6061
aluminum MMC using surface roughness as response parameter. The mathematical
relationship between WEDC input process parameters and surface roughness was
established to determine the value of surface roughness mathematically. As per
ANOVA results, voltage is the most significant parameters on surface roughness
where as pulse-on time and pulse-off time are less significant. Wire feed rate has
insignificant effect on average cutting speed. For minimum surface roughness 71.01
Chapter-2 Literature Review
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V as voltage, 1.00 ??s as pulse-on time, 6.04 ??s as pulse off time and 5.17 m/min wire
feed rate are found to be optimum.
Gopalakannan et al. [17] prepared metal matrix composite (MMC) of
aluminium 7075 reinforced with 10 wt% of SiC particles by stir casting method and
processed the same with electrical discharge machining with copper electrode. They
adopted face centered central composite design of response surface methodology.
Analysis of variance was applied to investigate the influence of process parameters
and their interactions viz., pulse current, gap voltage, pulse on time and pulse off time
on material removal rate (MRR), electrode wear ratio (EWR) and surface roughness
(SR). They identified the significant process parameters that affect the output
characteristics. Also a mathematical model has been formulated by applying response
surface method in order to estimate the machining characteristics such as MRR, EWR
and SR.
Table 2.2 presents a summary of the major studies conducted in the area of Modeling
of MRR, EWR and surface roughness in EDM.
Table2.2 Research efforts in Modeling of EDM process for Composite material
It reveals from the literature review that investigators attempted modeling of
EDM process for different composite materials through many methods like semiempirical
equations using the commercial software like STATISTICA, MATLAB,
Design Expert 8.0, FEA, Gauss elimination method, Artificial neural network (ANN),
Response surface method (RSM), Nonlinear regression analysis method, Taguchi
techniques; Genetic algorithm (GA).
Author(Year) Machining Composite Electrode Output parameter
Karthikeyan et al.
(1999)
EDM Al/SiC Copper Modeling for MRR,
EWR, and SR
Dhar et al. (2007) EDM Al'4Cu'6Si
alloy'10 wt.%
SiCP
Brass MRR, TWR, and
ROC. Mathematical
model
Patel et al. (2009) EDM Al2O3/SiCw/TiC Copper Modeling for SR and
Optimization
Shandilya et al.
(2011)
WEDM Al'10%SiCP Brass wire Modeling for SR
Gopalakannan et
al. (2012)
EDM 10 %
SiC/7075Al
Copper Modeling by RSM for
MRR, TWR,SR
Chapter-2 Literature Review
Page | 17
2.4 Optimization of EDM Process Parameters for Multiple Performance
Characteristics
Many researchers have experimentally tried to optimize different EDM
process parameters in various ways. Several of these attempts have been reviewed
here for the purpose of understanding the technology and knowing various methods to
optimize EDM process parameters for composite materials.
Guo et al. [18] investigated into shaping particles reinforced material by wire-
EDM with high-traveling speed. Selected for experimentation was 6061 alloy with
20% Al2O3 particle reinforcement. The electrical parameters were found to have little
influence on surface roughness. The experiment resulted in coarse surface irrespective
of high energy or low energy is used. The selection of electrical parameters had an
important effect on cutting rate. Use of low energy resulted in wire breakage due to
blind feeding especially for low pulse duration and low machining voltage. It was
found that at high pulse duration, high voltage, large machining current, and at proper
pulse interval high machining efficiency can be attained.
Kansal [19] established optimum process conditions for Powder Mixed
Electric Discharge Machining (PMEDM) of Al 10%SiCP Metal Matrix Composites
(MMC) by an experimental investigation using Response Surface Methodology
(RSM). Aluminium powder was suspended into the dielectric fluid of Electric
Discharge Machining (EDM). A modified powder mixed dielectric circulation system
was developed in the laboratory for experimentation. Relationships are developed
between various input process parameters (concentration of the added aluminium
powder, peak current and pulse duration) and output characteristics (Machining Rate
(MR), Surface Roughness (SR)). The obtained result allowed how to find the most
important parameters and determine the optimal values that maximize the MR and
minimize the SR. The recommended optimal process conditions have been verified by
conducting confirmation experiments.
Lin et al. [20] carried out investigation of machining performance of
conductive ceramics (Al2O3 + 30 vol% TiC) using EDM has been carried out. The
EDM machining parameters such as machining polarity, peak current, auxiliary
current with high voltage, pulse duration, no load voltage, and servo reference voltage
Chapter-2 Literature Review
Page | 18
were chosen to explore the effects on MRR, EWR and SR. MRR found to be
increased with peak current and pulse duration. EWR increased with peak current, and
declined with pulse duration. Machining polarity (P) and peak current significantly
affected EWR. SR enlarged with peak current and pulse duration. Experimental
results showed EDM is a feasible process to shape conductive ceramics, and
relationships between machining characteristics and parameters were examined.
Moreover, machining parameter optimal combination levels were also determined.
Kathiresan and Sornakumar [21] developed aluminum alloy-silicon carbide
composites using a new combination of vortex method and pressure die casting
technique. Studies were conducted on the aluminum alloy-silicon carbide composite
work piece using a copper electrode in an EDM. The MRR and SR of the work piece
increases with an increase in the current. The MRR decreases with increase in the
percent weight of silicon carbide. The surface finish of the machined work piece
improves with percent weight of silicon carbide.
Iosub et al. [22] investigated the influence of the most relevant parameters
of Electrical Discharge Machining over material removal rate, electrode wear
and machined surface quality of a hybrid metal matrix composite material
(Al/SiC). The material used in this study is aluminum matrix composite reinforced
with 7 % SiC and 3.5 % graphite. The hybrid composite was machined using 27 brass
tools with diameter = 3.97 mm. Different pulse on-times (ton), pulse off time (toff) and
peak current values (Ip) was used for each electrode. For the experiments, a full
factorial design was used. Regression analysis was applied for developing a
mathematical model. The hybrid SiC/Al composite material can easily be
machined by EDM and a good surface quality can be obtained by controlling
the machining parameters.
Nanimina et al. [23] 30% Al2O3 reinforced aluminum metal matrix composite
can be machined using EDM to obtain acceptable result in terms of MRR and TWR.
A high value of peak current and ON-time increase rapidly MRR of Al 6061 rather
than AMMC while it decreases with increasing of OFF-time. Tool wears more at low
peak current and ON-time than OFF-time.
Chapter-2 Literature Review
Page | 19

Table 2.3 presents a summary of the major studies conducted in the area of
Optimization of EDM process parameters for multiple performance characteristics
using GRA, RSM.
Table 2.3 Research efforts in Optimization of EDM parameters for Composites
Summarizing literature review, we found that most of investigators have tried
experimental optimization of different EDM process parameters in various ways.
They are mainly discharge voltage, peak current, pulse duration (pulse on) and pulse
interval (pulse off), electrode gap, polarity, and pulse wave form. Also some
investigators have considered non-electrical parameters like flushing of dielectric
fluid, workpiece rotation, and electrode rotation which are also critical for optimizing
performance measures. The major output performance considered are material
removal rate, surface roughness and tool wear rate. Most work reported is for sinking
EDM and wire EDM. Very few researchers have tried powder mixed EDM, micro
EDM and dry EDM. Some investigators have adopted hybrid EDM also. Modification
in electrode shapes, geometry and relative motion have also been tried by few
researchers.
2.5 Experimental Investigations to Study Surface Integrity and Material
Removal Mechanisms.
Few researchers have experimentally tried to discover various aspects related
to surface quality of EDM processed composites. Such surface integrity research have
been reviewed here for the purpose of understanding the technology and knowing
Author(Year) Machining Composite Electrode Output
parameter
Guo et al (2002) WEDM 6061 alloy/ Al2O3 -- Cutting rate, SR
Kansal (2006). PM-EDM Al'10%SiCP copper MRR, SR
Lin et al. (2009) EDM Al2O3''TiC Copper MRR, EWR, and
SR
Kathiresan and
Sornakumar (2010)
EDM Al/SiCp Copper MRR, SR
Iosub et al. (2010) EDM Al/SiC, 7 % SiC
and 3.5 %
graphite
Brass MRR, SR
Nanimina et al.
(2011)
EDM Al2/6061Al Hollow
Copper
MRR, TWR.
Chapter-2 Literature Review
Page | 20
various surface related parameters and their diagnosis methods for EDM processing
of composite materials.
Ramulu and Taya [24] worked out machinability of 15 vol.% and 25 vol.%
SiC whisker/2124 aluminum matrix (SiCw/Al) composites for EDM has been work
out. The material samples were cut at coarse, medium, and fine conditions using
copper and brass tools. It was found that material removal rate increases with increase
in power of electrode. MRR in 15 vol.% SiCw/2124 Al is greater than 25 vol.%
SiCw/2124 Al. Material removal rate obtained by using copper electrode is 5-10% less
than that of obtained when using brass electrode. Machining time appears to be higher
in 25 vol.% SiCw/Al than in 15% SiCw/Al composite. The micro-hardness tests on
SiCw/Al composite have revealed that the machining causes surface softening at
slower cutting speed. It was also found that higher cutting speed results in microdamage
in the surface and sub-surface area.
Muller and Monaghan [25] studied the machinability of silicon carbide
particle reinforced aluminium alloy matrix composites using non-conventional
machining processes such as electro discharge machining (EDM) and laser cutting.
The different removal mechanisms of the different processes when machining the
composite were investigated. The surface condition and sub-surface damage of the
material for the different machining processes have been examined and compared.
Both EDM and laser are suitable processes for machining PRMMCs, laser offers
significant advantages in terms of removal rate. EDM however induce less thermal
damage than that was observed using the laser.
Zhang et al. [26] used a hot pressed aluminium oxide based ceramic
composite, SG4 for EDM. They found that longer pulse-on time results in higher
surface roughness and generation of a thicker re-solidified layer with micro-cracks on
the subsurface. They suggested that the product of thermal conductivity and fusion
temperature could be considered as an indicator of EDM machinability. Flexural
strength is used for evaluating the effect of two machining processes on surfaces of
machined specimens.
Singh et al. [27] investigated the effect of current, pulse on-time and flushing
pressure on metal removal rate, tool wear rate, taper, radial overcut, and surface
Chapter-2 Literature Review
Page | 21
roughness of machined material. Many conclusions were drawn by experimentation.
MRR was found higher for larger current and pulse on-time settings at the expense of
taper, radial overcut, and surface finish. Electrode wear was also found to be higher,
even larger than the material removal rate for larger current settings. The dimensional
accuracy is affected at higher current and pulse on-time ratings. Both material
removal rate and electrode wear are considerably influenced by flushing pressure.
Patel et al. [28] attempted a detailed investigation of machining
characteristics, surface integrity and material removal mechanisms of advanced
ceramic composite Al2O3'SiCw'TiC with EDM. The surface and subsurface damages
have also been assessed and characterized using scanning electron microscopy
(SEM). The results provide valuable insight into the dependence of damage and the
mechanisms of material removal on EDM conditions.
Table 2.4 presents a summary of the major studies conducted in the area of surface
integrity and material removal mechanism in EDM process parameters for composite
materials.
Table 2.4 Research efforts in Surface Integrity and Material removal mechanism
Summarizing literature we can say that, though EDM is essentially a material
removal process, efforts have been made to use it as a surface treatment method
and/or an additive process. During the last decade, an increasing interest in the novel
applications of electrical discharge machining (EDM) process, with particular
emphasis on the potential of this process for surface modification has been
Author(Year) Machining Composite Electrode Output parameter
Ramulu and Taya.
(1989)
EDM Al-15%, 25 % SiCp Copper and
Brass
MRR, TWR, SR, sur.
Integrity
Muller and
Monaghan. (2001)
EDM
Laser
Cutting
Al-20%, 35 % SiCp Copper MRR, Surface
integrity
Zhang et al. (2002) WEDM Al2O3 + TiC + WC Brass wire VMRR, Surface
integrity, Flexural
strength
Singh et al. (2004) EDM Al'10%SiCP Brass MRR, TWR, Taper,
ROC and SR,
feasibility
Patel et al. (2009) EDM Al2O3/SiCw/TiC Copper Surface integrity
Chapter-2 Literature Review
Page | 22
accelerated. There are various aspects needs to be considered to understand the
surface quality and integrity obtained by processing the composite material through
EDM. Among this, (1) major process parameters like current, pulse duration, On-Off
time, Electrode rotation, gap voltage, flushing, polarity affect considerably to surface
quality, (2) using hydrocarbon dielectric having higher carbon content than the base
material to obtain increased abrasion and corrosion resistance, (3) Better surface
properties have been obtained by machining with powder metallurgy electrodes which
are, under certain processing and operating conditions, could cause net material
addition instead of material removal. (4) Fine powder mixed-dielectric fluids also
offer desirable surface modifications.
2.6 Literature Summary:
' Metal matrix composites (MMCs) are newly advanced materials having the
properties of light weight, high specific strength, good wear resistance and a
low thermal expansion coefficient. These materials are extensively used in
industry. Greater hardness and reinforcement makes it difficult to machine
using traditional techniques, which has impeded the development of MMCs.
The use of traditional machinery to machine hard composite materials causes
serious tool wear due to the abrasive nature of reinforcement. These materials
can be machined by many non-traditional methods like water jet and laser
cutting but these processes are limited to linear cutting only. Electrical
discharge machining (EDM) shows higher capability for cutting complex
shapes with high precision for these materials.
' There are many processes using which composite material can be fabricated or
manufactured. Among these methods Squeeze casting, Liquid metallurgy
route, Stir casting, Direct Extrusion, Pressure-less infiltration, Powder
metallurgy, Hot Pressing, and Centrifugal casting are widely used by
investigators.
' Composite materials are reinforced by Dispersion particles, Platelets,
Particulates, Dispersoids, Whiskers, non-continuous (short) fibres and
continuous (long) fibres as well as composite materials with hybrid
reinforcement composed of particles and fibres are produced.
Chapter-2 Literature Review
Page | 23
' The major properties tested of fabricated composite material from application
point of view are Fracture toughness, Fatigue stress, Fatigue high cycle stress
Life and Fatigue crack growth mechanism, Wear, Workability, Hardness, etc.
' Modeling of EDM process for different composite materials can be work out
through many methods like Artificial neural network (ANN), Response
surface method (RSM), Nonlinear regression analysis method, Taguchi
techniques; Genetic algorithm (GA). Much modeling work has been carried
out for material removal rate and surface roughness.
' Most of investigators have tried experimental optimization of different EDM
process parameters in various ways. They are mainly discharge voltage, peak
current, pulse duration (pulse on) and pulse interval (pulse off), electrode gap,
polarity, and pulse wave form.
' The major output performance considered are material removal rate, surface
roughness and tool wear rate.
Chapter-3 Experiment Set 'Up
Page | 24
Chapter
3
EXPERIMENT SET-UP
3.1 Fabrication of Composite Material.
Based on literature review, prepared Al based MMCs material with 99.0 %
purity. The chemical compositions of Al is (Si-.18 %, Fe-.60%, Cu-.075%, Mn-
.030%, Zn-.060%,Ti-.010%, Cr-.005%) with two SiC (68 ??m size ) & Graphite (73
??m size) reinforcements.
3.2 Composite Material Fabrication Process.
An experimental set up; five different Al based MMCs have been made.
Namely are under the table forms. Al contain is mean, the percentages of weight ratio
is define as,
''
'
''
'
'
' '
' 100
( ) ( ) ( )
( %) ( )
Al gms SiC gms Gr gms
Alcontain wt Al gms
(3.1)
Table 3.1 sample weight in gms are taken during composite material fabrication
Sr
no
Metal matrix Weight
(gms)
Reinforcement Weight
(gms)
Total weight of
per sample (gms)
Aluminum SiC Gr
1 400 - - 400
2 380 20 - 400
3 360 20 20 400
4 340 40 20 400
5 320 60 20 400
Table 3.2 weight Percentages % are taken during composite material fabrication
Sr
no
Metal matrix in Reinforcement Total weight in
Aluminum SiC Gr percentages %
1 100 - - 100
2 95 5 - 100
3 90 5 5 100
4 85 10 5 100
5 80 15 5 100
Chapter-3 Experiment Set 'Up
Page | 25

The fabrication of Al 'SiC+Gr MMCs material is made by liquid metal stir
casting manufacturing process. In liquid metal stir casting, first aluminum plate small
pieces are incorporated in crucible and melt up to its melting temperature around 620
o C to 650 o C. during the process aluminum pieces melting, simultaneously SiC and
Gr particle are preheated around 500 o C to 550 o C for removing moisture. Once
aluminum pieces are completely melt then, the incorporation of preheated SiC & Gr
particle into melt rotated those constituent by stirrer mechanism up 10 to 15 minutes
at 750 to 800 RPM and then, pouring of composite melt into the mould are carried out
into fully liquid state.
During fabrication some photograph.
Figure-3.1 Weighting Al, SiC + Gr Figure -3.2 Putting Al pieces in
powder crucible
Figure-3.3 & 3.4 melting.
Chapter-3 Experiment Set 'Up
Page | 26
Figure -3.5 Temp measurements by Figure-3.6 Al melt
infrared temp instrument
Figure-3.7 SiC & Gr incorporated Figure-3.8 mould & patterns & stir
cast by motor.
Figure-3.9 and 3.10 (Cut Section view ) 3D modeling of foundry & crucible by
CAD Software
Chapter-3 Experiment Set 'Up
Page | 27
3.3 Testing & Results of Composite Material.
The following test has been carried out on composite material after fabrication
material.
Figure-3.11 All five composite material
3.4 Chemical Composition Test of Aluminium.
The following under the table shows chemical composition of pure aluminum
and others elements. Y
Table 3.3 chemical composition of aluminum
Sr no Constituent Composition in %
1 Aluminum (Al) 99.0
2 Silicon (Si) 0.18
3 Iron (Fe) 0.60
4 Copper (Al) 0.075
5 Manganese (Mn) 0.030
6 Zinc (Zn) 0.060
7 Titanium(Ti) 0.010
8 Chromium (Cr) 0.005
9 Nickel (Ni) 0.004
10 Lead (Pb) 0.025
11 Tin (Tn) 0.002
Chapter-3 Experiment Set 'Up
Page | 28
3.5 Mechanical Properties Testing of Composite Material.
3.5.1 Hardness
Experiments have been conducted by varying weight fraction of Sic (5%,
10%, 15%) and graphite with 5 % of each, Hardness test has been conducted on each
specimen using a load of 250 Kgf and a steel ball of diameter 5 mm as indenter.
Diameter of impression made by indenter has been predicted by Brunnel microscope.
The Brinell hardness test was done on Brinell hardness tester as shown in
Figure-3.12 Serial no 120785, year-2012, EiE makeS Pvt. Ltd,india. Five samples of
Al based hybrid -MMC's for different sizes and weight fraction of SiC and Graphite
particles were prepared.
The formula of BHN is under as,
''
'
''
'
''
'
''
' ' '
'
2 2
2
D D D d
BHN F
'
(3.2)
Figure-3.12 Brinel Hardness Tester
Where D is Ball Diameter in mm, F is applied load on material 250 Kgf, D is
impression diameter in mm, ?? is 3.14 1414 constant.
3.5.2 Density & Tensile strength
The density and tensile strength for different aluminum matrix composite
with SiC and Gr as reinforcement particles can be estimated through equations (3.3)
to (3.4) [29,B1]
Chapter-3 Experiment Set 'Up
Page | 29
' c ' 'mvm ' ' pv p (3.3)
Where, c ' is the composite's density, m ' is the matrix's density, 'v is the
reinforcement particle's density, m v is the volume fraction of the matrix and p v is the
volume fraction of the particle.
c mvm ' pvp ' '' '
(3.4)
Where c ' is the composite's tensile strength, m ' is the matrix's tensile strength, and
p ' is the reinforcement particle's tensile strength.
The results as indicated in Table-3.4 the increasing the hardness of composite
with increase in weight percentage of Sic up to 15% weight fraction. Also it shows
that the different mechanical properties of aluminum reinforced with silicon carbide
and graphite. The table depicts that the ultimate tensile strength & Density increases
with addition of graphite and silicon carbide particulates. [31]
Table 3.4 Mechanical Properties
Sr no Metal matrix Reinforcement BHN Density
(g/cm3)
Tensile
strength
(N/mm2)
Aluminum % SiCp
% Gr
1 100 - - 35 2.7 131.0
2 95 5 - 40 2.72 153.85
3 90 5 5 39 2.698 148.9
4 85 10 5 42 2.718 171.75
5 80 15 5 44 2.738 194.6
Graph-3.1 weight % of MMCs vs BHN Graph-3.2 weight % of MMCs vs
Tensile Strength
Chapter-3 Experiment Set 'Up
Page | 30
Graph-3.3 weight % of MMCs vs. Density
From graph-3.1,3.2 & 3.3 shows that, incresed weight % contribution of
reinforcement in MMCs, mechanical & physical properties is incresed i.e
BHN,Tensile strenth & Density.
3.6 Scanning Electron Microscope Images of Composite Material.
Scanning Electron Microscope (SEM) (Model=JEOL JS-5810LV) test on
composite material, shows the micrographs structure of each composite material.
Microstructure examinations are carried out to investigate of distribution of the silicon
carbide & Graphite particles in the composite. Samples having pure aluminum 5,10
&15 weight percentage of silicon carbide are examined with graphite 5 weight
percentage is taken constant for each composite materials. The samples are polished
using emery paper (1000 and 1500 grit size) and finally etched using 10 % HF.
Figure-3.13 SEM (Model=JEOL JSM-5810LV) Figure-3.14 Pure aluminium
Chapter-3 Experiment Set 'Up
Page | 31
Figure -3.15 (Al-95 % & SIC 5%) Figure 3.16 (Al-90 % ,SiC-5 %,Gr-5%)
Both Micrographs showing the distribution of the reinforcement in the composite
Figure 3.17 (Al-85 %,SiC-10%,Gr-5%) Figure 3.18 (Al-80 % ,SiC-15%,Gr-5%)
Both Micrographs showing the distribution of the reinforcement in the composite
From Figure 3.13 to 3.19 shows that, Increased content of SiC a hard ceramic
will result in enhancement in the hardness of the composites. Increased hardness leads
to lowering of wear loss and seizing. Further, the presence of graphite in hybrid
composites has further influenced the wear behavior of it. Graphite, a solid lubricant
will tend to get smeared out between the rubbing surfaces thereby minimizing the
chances of three body abrasive wear that is normally encountered in conventional
hard ceramic reinforced metal matrix composites. This phenomenon will drastically
reduce the wear loss of the hybrid composites [30].
SiC
SiC
Fe3C Gr
Fe3C
SiC
SiC
SiC
Gr
Gr
Fe3C
Chapter-3 Experiment Set 'Up
Page | 32
3.7 Micro Structure of Composite Material.
Figure 3.19 Al & SiC Figure -3.20 Al+SiC+Gr sample
Microstructure of aluminium matrix
From Figure 3.20 to 3.21 shows that, Microstructure shows Si particle in
light gray, polyhedral form in aluminium matrix.
3.8 Electric Discharge Machining Experiment Details.
Experiments were conducted on a Z 50 JM-322 die-sinking EDM machine
(refer figure 3.21) manufactured by JOEMARS. The existing dielectric circulation
system of Z 50 JM-322 EDM is DEF-92 fluid. The dielectric fluid was circulated by
pumping system. To hold the workpiece, work piece fixture assembly is placed in
tank. The machine tank is field up with dielectric fluid i.e. DEF-92. The five Al
based MMCs material is selected as a work piece material. Copper electrode with
diameter 15 mm have been selected as a tool. The machining is performed in
commercially available DEF-92. Total 31 experiments have been conducted on
composite material through EDM machine.
Figure -3.21 Electric Discharge Machine
Si
Al
Chapter-3 Experiment Set 'Up
Page | 33
The main parts of the machine are:
1. Electric power supply
2. Dielectric system
3. Work piece
4. Electrode (Tool)
5. Servo control
Table 3.5.Specification of EDM machine
1.
Sr
No
SPECIFICATION OF ELECRTIC DISCHAGE MACHINE
1 Made by JOEMARS
2 Model: Z 50 JM-322
3 Table size: 600 ?? 300 mm
4 X, Y, Z Travel: 300/200/200 mm
5 Max. Electrode weight: 60 Kg
6 Tank size: 830 ?? 500 ?? 300 mm
7 Max. Work piece
weight:
550 Kg
8 Weight of machine: 1050 Kg
9 Servo controlled
voltage stabilizer:
Maker:
Made:
Servomax
PS 5 K 3P
10 Technical
specification
Capacity:
Type of cooling:
Input voltage range:
Output voltage:
5 KVA / 3 Phase
Natural air cooled
380 ' 480 volt AC
415 voltages AC +/' 5%
Chapter-3 Experiment Set 'Up
Page | 34
3.9 Mechanism of MRR
The mechanism of material removal of EDM process is most widely
established principle is the conversion of electrical energy it into thermal energy.
During the process of machining the sparks are produced between work piece and
tool .Thus each spark produces a tiny crater, and crater formation shown in this Fig
3.2 in the material along the cutting path by melting and vaporization, thus eroding
the work piece to the shape of the tool.
Figure 3.22 Crater formation in EDM process
It is well-known and elucidated by many EDM researchers by Roethel
that Material Removal Mechanism (MRM) is the process of transformation of
material elements between the work-piece and electrode. The transformation are
transported in solid, liquid or gaseous state, and then alloyed with the ing
surface by undergoing a solid, liquid or gaseous phase reaction.
The material MRR is expressed as the ratio of the difference of weight of
the work piece before and after machining to the machining time and density of the
material.
t
MRR Wtb Wta
'
'
'
'
(3.5)
Where,
Wtb=weight before machining in gm.
Wta=weight after machining in gm.
D=density of work piece material in gm/mm3.
t=time consumed for machining in minute.= 10 sec
Chapter-3 Experiment Set 'Up
Page | 35
The weight of the work piece and tool is measured on precise weighing
machine having least count of 0.0001 gm.
3.10 Mechanism of TWR.
During the EDM process considerable amount of the material from the tool is
removed. However the amount of the material removed from the tool is less than that
of the work piece. TWR is expressed as the volumetric loss of tool per unit time,
expressed as
t
TWR Wtb Wta
'
'
'
'
(3.6)
Where,
Wtb=weight before machining in gm.
Wta=weight after machining in gm.
D=density of work piece material in gm/mm3.
t=time consumed for machining in minute. = 10 sec
3.11 Surface Roughness
Surface topography or surface roughness, also known as surface texture are
terms used to express the general quality of a machined surface, which is concerned
with the geometric irregularities and the quality of a surface. Surface Roughness
measure as the arithmetic average, Ra (??m). The Ra value, also known as centre line
average (CLA) and arithmetic average (AA) is obtained by averaging the height of the
surface above and below the centre line. The Ra will be measured using a surface
roughness tester from Mitutoyo, Model:SJ 201P.
Figure 3.23 Set Up for Surface Roughness Measurement
Chapter-3 Experiment Set 'Up
Page | 36
Some photograph during Experimental work on EDM.
Figure -3.24 Material place on EDM Figure -3.25 parameter set
Figure -3.26 machining on composite Figure -3.27 weighting of material
material
Figure -3.28 weighting of tool Figure -3.29 facing tool every experiments
Chapter-3 Experiment Set 'Up
Page | 37
Figure -3.30 Machining on all five composite materials
Chapter-4 Response Surface Methodology
Page | 38
Chapter
4
RESPONSE SURFACE METHODOLOGY

4.1 Theory of the Experimental Design
The main objective of experimental design is studying the relations between
the response as a dependent variable and the various parameter levels. It provides an
opportunity to study not only the individual effects of each factor but also their
interactions. Design of experiments is a method used for minimizing the number of
experiments to achieve the optimum condition.
The design of experiments for exploring the influence of various predominant
EDM process parameters (e.g. pulse on time, peak current, gap voltage and pulse off
time) on the machining characteristics (e.g. the material removal rate, tool wear ratio,
and the surface finish), were modeled. In the present work experiments were designed
on the basis of experimental design technique using response surface design method.
In order to determine the equation of the response surface, experimental
design has been developed with the attempt to approximate this equation using the
smallest number of experiments possible. In this investigation, experimental design
was established on the basis of 2k factorial, where k is the number of variables, with
central composite-second-order rotatable design to improve the reliability of results
and to reduce the size of experimentation without loss of accuracy. Thus, the
minimum possible number of experiments (N) can be determined from the following
equations:
N n n n....... c a ' ' ' (4.1)
k
nc ' 2
n k a ' 2 '
Chapter-4 Response Surface Methodology
Page | 39
Where nc parameter defines the number of factorial points or corner points.
One central composite design consists of cube points at the corners of a unit cube that
is the product of the intervals [-1, 1]. The na parameter defines the number of axial
points or star points along the axes or outside the cube at a distance ?? = k1/2 from the
centre point of the design to a star point and no parameter means the number of centre
points at the origin and can be get from tables according to the number of independent
variables.
Table 4.1 Components of Central Composite Second Order Rotatable Design
(Cochran and Cox, 1962)
Variables
(k)
Factorial
Points (2k)
Star Points
(2xk)
Center
Points (n)
Total (N) Value of ??
3 8 6 6 20 1.682
4** 16 8 7 31 2.000
5 16* 10 6 32 2.000
6 32* 12 9 53 2.378
* Half replication , **This row is used in the present work
Figure 4.1: Central Composite Rotatable Design in 3X-Variables (Cochran and
Cox, 1962)
Chapter-4 Response Surface Methodology
Page | 40
In this case k = 4 and thus nc = 2k = 16 corner points at ??1 level, na = 2 X k = 8 axial
points at ?? = ??2, and a centre point at zero level repeated 7 times (no). This involves a
total of 31 experimental observations.
Table 4.2 Central Composite Second Order Rotatable Design Matrix for 4
Variables
Sr
No
.
Linear terms Square terms Interaction terms
X1 X X X X1 X2 X3 X4 X1 X1 X1 X2 X2 X3
1 -1 -1 -1 -1 1 1 1 1 1 1 1 1 1 1
2 1 -1 -1 -1 1 1 1 1 -1 -1 -1 1 1 1
3 -1 1 -1 -1 1 1 1 1 -1 1 1 -1 -1 1
4 1 1 -1 -1 1 1 1 1 1 -1 -1 -1 -1 1
5 -1 -1 1 -1 1 1 1 1 1 -1 1 -1 -1 1
6 1 -1 1 -1 1 1 1 1 -1 1 -1 -1 1 -1
7 -1 1 1 -1 1 1 1 1 -1 -1 1 1 -1 -1
8 1 1 1 -1 1 1 1 1 1 1 -1 1 -1 -1
9 -1 -1 -1 1 1 1 1 1 1 1 -1 1 -1 -1
10 1 -1 -1 1 1 1 1 1 -1 -1 1 1 -1 -1
11 -1 1 -1 1 1 1 1 1 -1 1 -1 -1 1 -1
12 1 1 -1 1 1 1 1 1 1 -1 1 -1 1 -1
13 -1 -1 1 1 1 1 1 1 1 -1 -1 -1 -1 1
14 1 -1 1 1 1 1 1 1 -1 1 1 -1 -1 1
15 -1 1 1 1 1 1 1 1 -1 -1 -1 1 1 1
16 1 1 1 1 1 1 1 1 1 1 1 1 1 1
17 -2 0 0 0 4 0 0 0 0 0 0 0 0 0
18 2 0 0 0 4 0 0 0 0 0 0 0 0 0
19 0 -2 0 0 0 4 0 0 0 0 0 0 0 0
20 0 2 0 0 0 4 0 0 0 0 0 0 0 0
21 0 0 -2 0 0 0 4 0 0 0 0 0 0 0
22 0 0 2 0 0 0 4 0 0 0 0 0 0 0
23 0 0 0 -2 0 0 0 4 0 0 0 0 0 0
24 0 0 0 2 0 0 0 4 0 0 0 0 0 0
25 0 0 0 0 0 0 0 0 0 0 0 0 0 0
26 0 0 0 0 0 0 0 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0 0 0 0 0 0 0 0
29 0 0 0 0 0 0 0 0 0 0 0 0 0 0
30 0 0 0 0 0 0 0 0 0 0 0 0 0 0
31 0 0 0 0 0 0 0 0 0 0 0 0 0 0
X11=X1*X1, X22= X2*X2, X33= X3*X3, X44=X4*X4, X12=X1*X2,
X13=X1*X3, X14=X1*X4,X23=X2*X3, X24=X2*X4, X34=X3*X4
Chapter-4 Response Surface Methodology
Page | 41
Table 4.3 Process Parameters and their Levels.
Level Ip (amp) Ton (??s) Toff (??s) % contribution
+2 36 75 50 20
+1 28 60 40 15
0 21 45 30 10
-1 17 30 20 5
-2 9 15 10 0
4.2 Estimation of the Coefficients
As stated earlier the regression equation representing second order response
surface has been assumed as:
' ' '
' ' ' '
' ' ' ' '
k
i
k
i
r
k
i j
i i ii i ij i j Y b b x b x b x x
1 1 2
2
0 '
(4.2)
Where, Y is the estimated response, b's are the coefficients and xi's are the
independent variables.
The method of least squares may be used to estimate the regression
coefficients [B2]. Let xqi denote the qth observation of the variable xi and N the total
number of observations. Then the data for N observations in terms of various
variables will appear as shown below:
In terms of the qth observation the Equation 4.2 can be written as
Chapter-4 Response Surface Methodology
Page | 42
(4.3)
OR
(4.4)
Where,
q = 1, 2' N
The least square function is,
(4.5)
Hence from the Equation 3.5
(4.6)
This function L is to be minimized with respect to b0, b1' This least square
estimate of b0, bi, bii and bij must satisfy the following set of equations:
(4.7)
(4.8)
(4.9)
(4.10)
Chapter-4 Response Surface Methodology
Page | 43
There are P = k+1 normal equations, one for each unknown regression
equation coefficient. Hence, by solving the above equations the coefficients of the
regression equation can be obtained.
It is simpler to solve the normal equations if they are expressed in matrix
form. The second order response surface in matrix form may be written as:
Y ' X' '' (4.11)
Where,
N = Total number of experiments
P = Total number of coefficients
Y is an (N ?? 1) vector of the observations, X is an (N ?? P) matrix of the levels of the
independent variables, ?? is a (P ?? 1) vector of the regression coefficients and ?? is a (N
?? 1) vector of random errors.
The least square estimator is
(4.12)
This may be expressed as
(4.13)
Since is a '??X'Y' (1 ?? 1) matrix and its transpose will also be a (1?? 1) matrix. Then
(4.14)
Chapter-4 Response Surface Methodology
Page | 44
Hence the Equation 3.15 has been written as:
(4.15)
The least square estimates must satisfy
(4.16)
This on simplification yields the values of different coefficients of regression equation
as

(4.17)
4.3 Analysis of Variance
For the analysis of variance, the total sum of squares may be divided into four parts:
' The contribution due to the first order terms
' The contribution due to the second order terms
' A 'Lack of fit' component which measures the deviations of the
response from the fitted surface
' Experimental error which is obtained from the centre points
The general formulae for the sum of squares are given in Table 3.7,
where, N is the total number of experimental points, n0, Ys, 0 Y
represent total number of observations, sth response value and mean
value of response respectively at the centre points of the experimental
region. The design matrix for five independent variables is shown in
Table 4.3
Chapter-4 Response Surface Methodology
Page | 45
Table: 4.4 Analysis of Variance for Central Composite Second Order Rotatable
Design
Sr.
No. Source Sum of Squares Degree of
freedom
1 First order
terms
K
2 Second order
terms
3 Lack of fit Found by subtraction
4 Experimental
error
5 Total
N-1
The F ratio is given by:
(4.18)
Where,
bi'= Regression coefficients
cii = Element of the error matrix (X'X)-1
Chapter-4 Response Surface Methodology
Page | 46
Se = Standard deviations of experimental error calculated from replicating
observations at zero level as:
(4.19)
Where,
Ys = sth response value at the centre
Chapter-5 Result and Discussion
Page | 47
Chapter
5
RESULT AND DISCUSSION
5.1 Introduction.
The present chapter gives the application of the response surface
methodology. The scheme of carrying out experiments was selected and the
experiments were conducted to investigate the effect of process parameters on the
output parameters e.g. MRR, TWR & SR. The experimental results are discussed
subsequently in the following sections. The selected process variables were varied up
to five levels and central composite rotatable design was adopted to design the
experiments. Response Surface Methodology was used to develop second order
regression equation relating response characteristics and process variables. The
process variables and their ranges are given in Table 5.1
Table 5.1 Process Parameters and their Levels.
Coded
Factors
Real
Factors
Parameters Levels
-2 -1 0 +1 +2
X1 Ip (Amp) Peak current 9 17 21 28 36
X2 Ton (??s) Pulse on time 15 30 45 60 75
X3 Toff (??s) Pulse off time 10 20 30 40 50
X4 % contribution % contribution 0 5 10 15 20
5.2 Experimental Results.
The EDM experiments were conducted, with the process parameter levels set
as given in Table 5.1, to study the effect of process parameters over the output
parameters. Experiments were conducted according to the test conditions specified by
the second order central composite design. Experimental results are given in Table 5.2
for Tool wear ratio, Material Removal Rate and surface Roughness. Altogether 31
experiments were conducted using response surface methodology.
Chapter-5 Result and Discussion
Page | 48
Table 5.2 Observed Values for Performance Characteristics (MRR, TWR & SR)
Sr. No. Ip
(amp)
Ton
(??s)
Toff
(??s)
% Contribution MRR
(mm3/min)
TWR
(mm3/min)
SR
(??m)
1 28 60 40 15 3.36296 0.89551 11.423
2 17 60 20 15 6.82963 0.62697 9.542333
3 17 60 40 5 4.17037 0.39213 10.606
4 17 30 40 5 2.6037 0.09775 4.756667
5 17 30 20 15 8.57037 0.78315 6.802333
6 21 45 10 10 3.6963 0.98315 7.013667
7 9 45 30 10 1.95926 0.57303 4.185333
8 28 30 20 15 11.7185 1.05281 8.635333
9 21 45 30 10 6.1143 1.22022 8.136667
10 21 45 30 10 6.07778 0.72697 6.585
11 21 45 30 10 6.58148 0.7236 6.663667
12 17 60 20 5 1.78519 0.52022 10.26167
13 17 30 20 5 11.8481 0.72247 5.622667
14 17 30 40 15 5.48519 0.28539 4.676667
15 28 60 40 5 12.0519 1.79438 11.37733
16 28 30 20 5 17.5593 1.46854 6.822333
Chapter-5 Result and Discussion
Page | 49
Sr. No. Ip
(amp)
Ton
(??s)
Toff
(??s)
% Contribution MRR
(mm3/min)
TWR
(mm3/min)
SR
(??m)
17 21 45 30 10 6.04074 0.78427 7.265
18 21 45 30 10 6.1740 0.72022 7.079667
19 28 60 20 15 14.6222 2.13708 10.65567
20 21 75 30 10 3.98889 1.61124 15.977
21 21 45 30 10 6.2074 0.63146 7.626667
22 28 30 40 5 9.6 0.47416 7.709333
23 21 45 30 10 6.0940 0.84494 6.618667
24 17 60 40 15 0.75926 1.06067 9.312333
25 21 45 50 10 4.57037 0.64382 6.703333
26 28 60 20 5 12.6444 2.04494 10.70667
27 21 15 30 10 1.84444 0.32921 4.513
28 36 45 30 10 23.6111 2.03146 7.662667
29 21 45 30 20 2.31481 0.61461 7.026333
30 21 45 30 0 8.17407 1.21461 6.942333
31 28 30 40 15 12.5519 0.48989 7.188
Chapter-5 Result and Discussion
Page | 50
5.3 Analysis and Discussion of Results.
The experiments were designed and conducted by employing response surface
methodology (RSM). The selection of appropriate model and the development of
response surface models have been carried out by using statistical software, 'Minitab
16'. The regression equations for the selected model were obtained for the response
characteristics, viz., MRR, TWR and SR. These regression equations were developed
using the experimental data (Table 5.2) and were plotted to investigate the effect of
process variables on various response characteristics. The analysis of variance
(ANOVA) was performed to statistically analyze the results.
5.3.1 Effect of Process Variables on MRR.
The unknown coefficients are determined from the experimental data as
presented in Table- 5.3. The standard errors on estimation of the coefficients are
tabulated in the column 'SE coef'. The F ratios are calculated for 95% level of
confidence. For improving the value of R2 the unusual observation with large
standardized residual (i.e. 13) were eliminated. The regression model is reevaluated
by determining the unknown coefficients, which are tabulated in Table- 5.2. The final
response equation for MRR is given in below equation.
Table 5.3 Estimated Regression Coefficients for MRR after elimination of
unusual observation.
Term Coef SE Coef T P
Constant -42.8490 23.9696 -1.788 0.094
Ip (amp) 0.6050 0.8747 0.692 0.500
Ton (??s) 0.5809 0.3600 1.614 0.127
Toff (??s) 0.8986 0.5399 1.664 0.117
% Contribution 2.1190 1.0403 2.037 0.060
Ip*Ip 0.0326 0.0119 2.750 0.015
Ton*Ton -0.0028 0.0023 -1.236 0.235
Toff*Toff -0.0033 0.0051 -0.641 0.531
% Contribution*
% Contribution -0.0020 0.0205 -0.098 0.923
Ip*Ton -0.0058 0.0085 -0.681 0.506
Ip*Toff -0.0195 0.0128 -1.528 0.147
Ip*% Contribution -0.0456 0.0255 -1.789 0.094
Ton*Toff -0.0035 0.0048 -0.722 0.481
Chapter-5 Result and Discussion
Page | 51
Ton*%
Contribution -0.0117 0.0096 -1.219 0.242
Toff*%
Contribution -0.0205 0.0144 -1.426 0.174
R-Sq = 85.97% R-Sq(adj) = 72.87%
The ANOVA table for the curtailed quadratic model (Table-5.3) depicts the
value of Coefficient of determination R2 as 83.81 %, which signifies that how much
variation in the response is explained by the model. The higher of R2, indicates the
better fitting of the model with the data.
It is important to check the adequacy of the fitted model, because an incorrect
or under-specified model can lead to misleading conclusions. By checking the fit of
the model one can check whether the model is under specified. The model adequacy
checking includes the test for significance of the regression model, model
coefficients, and lack of fit, which is carried out subsequently using ANOVA on the
curtailed model (Table-5.4).
Table 5.4 Analysis of variance for MRR after elimination of unusual observation
Source DF Seq SS Adj SS Adj MS F P Significant
Regression 14 669.071 669.071 47.7908 6.56 0.000
Linear 4 533.639 44.923 11.2307 1.54 0.241 NS*
Square 4 84.288 85.021 21.2554 2.92 0.050 S*
Interaction 6 51.144 51.144 8.5240 1.17 0.372 NS*
Residual
Error

15 109.231 109.231 7.2820
Lack-of-Fit 9 109.027 109.027 12.1141 357.34 0.000
Pure Error 6 0.203 0.203 0.0339
Total 29 778.302
S* = Significant, NS* Non Significant
The residual analysis as a primary diagnostic tool is also done. Normal probability
plot of residuals has been drawn (Figure 5.1). All the data points are following the
I cont T T T cont T Cont
T T Cont I T I T
MRR I T T Cont I
p on off on off
on off p on p off
p on off p
0.0456 % 0.0035 0.0117 % 0.0205 %
0.0028 0.0033 0.0020 % 0.0058 0.0195
-42.8490 0.6050 0.5809 0.8986 2.1190 % 0.0326
2 2 2
2
' ' ' ' ' ' ' ' ' ' ' '
' ' ' ' ' ' ' ' ' ' ' '
' ' ' ' ' ' ' ' ' ' '
Chapter-5 Result and Discussion
Page | 52
straight line. Thus the data is normally distributed. It is clear from the P value of
Analysis of variance for MRR after elimination of unusual observation and Residual
plots that the predicted model is accurate and prediction of MRR fitted value from
mathematical model is shown in table 5.5
Table 5.5 Prediction of MRR Fitted value from the Mathematical model.
Obs StdOrder MRR Fit SE Fit Residual St
Resid
1 1 3.363 4.591 2.326 -1.228 -0.90
2 2 6.830 4.608 1.998 2.221 1.22
3 3 4.170 3.892 1.998 0.278 0.15
4 4 2.604 2.910 2.008 -0.306 -0.17
5 5 8.570 5.054 2.008 3.516 1.95
6 6 3.696 6.323 2.150 -2.627 -1.61
7 7 1.959 2.891 2.293 -0.932 -0.65
8 8 11.719 14.124 2.131 -2.406 -1.45
9 9 6.114 6.274 1.016 -0.159 -0.06
10 10 6.078 6.274 1.016 -0.196 -0.08
11 11 6.581 6.274 1.016 0.308 0.12
12 12 1.785 2.685 2.008 -0.899 -0.50
13 14 5.485 4.240 1.998 1.246 0.69
14 15 12.052 11.785 2.115 0.267 0.16
15 16 17.559 13.717 2.176 3.843 2.41 R
16 17 6.041 6.274 1.016 -0.233 -0.09
17 18 6.174 6.274 1.016 -0.100 -0.04
18 19 14.622 11.768 2.115 2.854 1.70
19 20 3.989 3.318 2.072 0.671 0.39
20 21 6.207 6.274 1.016 -0.066 -0.03
21 22 9.600 12.713 2.131 -3.113 -1.88
22 23 6.094 6.274 1.016 -0.180 -0.07
23 24 0.759 1.718 1.971 -0.959 -0.52
24 25 4.570 3.599 2.072 0.972 0.56
25 26 12.644 14.864 2.131 -2.220 -1.34
26 27 1.844 4.171 2.150 -2.326 -1.43
27 28 23.611 23.709 2.198 -0.098 -0.06
28 29 2.315 5.874 2.072 -3.559 -2.06 R
29 30 8.174 6.271 2.150 1.903 1.17
30 31 12.552 9.023 2.115 3.529 2.11 R
Chapter-5 Result and Discussion
Page | 53
-5.0 -2.5 0.0 2.5 5.0
99
90
50
10
1
Residual
Percent
0 5 10 15 20
4
2
0
-2
-4
Fitted Value
Residual
-3 -2 -1 0 1 2 3 4
6.0
4.5
3.0
1.5
0.0
Residual
Frequency
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
4
2
0
-2
-4
Observation Order
Residual
Normal Probability Plot Versus Fit s
Histogram Versus Order
Residual Plots for MRR
Figure 5.1: Residual Plots for MRR
The residual plot in graph and its interpretation as described below.
' Normal probability plot indicate outlines don't much exist in the data, because
standardized residues are within small range and nature is follow straight line.
' Histogram shows the data are not skewed at one side. MRR measurement
accuracy relies on average of reading, hence the pattern can be considered for
the analysis.
' Residual versus order of the data indicates that symmetry effects in the data
due to time of data collection order.
The below response surface is plotted to study the effect of process variables on the
MRR and is shown in Figures 5.1a-5.1d.
Ip
Ton
10 15 20 25 30 35
70
60
50
40
30
20
Toff 30
% Contribution 10
Hold Values
>
'
'
'
'
< 0
0 5
5 10
10 15
15 20
20
MRR
Contour Plot of MRR vs Ton, Ip
Fig. 5.1a Combined Effect of Ton and Ip on MRR
Chapter-5 Result and Discussion
Page | 54
Fig. 5.1 a shows the estimated response surface for MRR in relation to the
process parameters of Ip and Ton while Toff and % Contribution remain constant at
their middle value. It can be seen from the figure, the MRR tends to increase
significantly with the increase in Ip for any value of Ton. However, for any peak
current value, MRR tends to increase first with increase Ton up to 40 ??s and then
decreased with increased value of Ton . Hence, maximum MRR is obtained at high
peak current. This is due to their dominant control over the input energy, i.e. with the
increase in Ip generates strong spark.
Ip
Toff
10 15 20 25 30 35
50
40
30
20
10
Ton 45
% Contribution 10
Hold Values
>
'
'
'
'
'
< 0
0 5
5 10
10 15
15 20
20 25
25
MRR
Contour Plot of MRR vs Toff, Ip
Fig. 5.1b Combined Effect of Toff and Ip on MRR
Fig. 5.1 b shows that the Ip and Toff are significant factors varying linearly with
the response. MRR increases as Ip increases for any value of Toff. The maximum value
of MRR is achieved at 35 amp and low Toff.
Ip
% Contribution
10 15 20 25 30 35
20
15
10
5
0
Ton 45
Toff 30
Hold Values
>
'
'
'
'
'
< 0
0 5
5 10
10 15
15 20
20 25
25
MRR
Contour Plot of MRR vs % Contribution, Ip
Fig. 5.1c Combined Effect of % Contribution and Ip on MRR
Chapter-5 Result and Discussion
Page | 55
Fig. 5.1 c shows the estimated response surface for MRR in relation to the
process parameters of Ip and % Contribution while Toff and Ton Contribution remain
constant at their middle value. It can be seen from the figure, the MRR tends to
increase significantly with increasing Ip. The MRR increases with % Contribution up
to 25 Amp. From 25 amp to 35 amp MRR going to decrease with increase in %
Contribution.
Ton
% Contribution
20 30 40 50 60 70
20
15
10
5
0
Ip 22.5
Toff 30
Hold Values
>
'
'
'
< 0.0
0.0 2.5
2.5 5.0
5.0 7.5
7.5
MRR
Contour Plot of MRR vs % Contribution, Ton
Fig. 5.1d Combined Effect of % Contribution and Ton on MRR
Fig. 5.1d shows the estimated response surface for MRR in relation to the
process parameters of Ton and % Contribution while Toff and Ip remain constant at
their middle value. It can be seen from the figure, the MRR tends to increase with %
Contribution increases. But the combination of Ton and % Contribution is very
important. In certain zone the MRR value is high (e.g. 30 to 70 Ton) for different
condition of % Contribution.
5.3.2 Effect of Process Variables on TWR.
The unknown coefficients are determined from the experimental data as
presented in Table- 5.5. The standard errors on estimation of the coefficients are
tabulated in the column 'SE coef'. The F ratios are calculated for 95% level of
confidence. For improving the value of R2 the unusual observation with large
standardized residual (i.e. 1) were eliminated. The regression model is reevaluated by
determining the unknown coefficients, which are tabulated in Table- 5.2. The final
response equation for TWR is given in below equation.
Chapter-5 Result and Discussion
Page | 56
Table 5.6 Estimated Regression Coefficients for TWR after elimination of
unusual observation
Term Coef SE Coef T P
Constant 0.557429 1.83528 0.304 0.765
Ip (amp) 0.009892 0.07889 0.125 0.902
Ton (??s) -0.052730 0.03171 -1.663 0.116
Toff (??s) 0.020617 0.04757 0.433 0.671
% Contribution 0.070067 0.08881 0.789 0.442
Ip*Ip 0.002032 0.00118 1.718 0.105
Ton*Ton 0.000106 0.00023 0.471 0.644
Toff*Toff -0.000152 0.00051 -0.299 0.769
% Contribution*
% Contribution 0.000402 0.00204 0.197 0.846
Ip*Ton 0.001908 0.00081 2.365 0.031
Ip*Toff -0.002729 0.00121 -2.254 0.039
Ip*% Contribution -0.004499 0.00242 -1.858 0.082
Ton*Toff 0.000622 0.00045 1.385 0.185
Ton*%
Contribution 0.000100 0.00090 0.111 0.913
Toff*%
Contribution 0.000162 0.00135 0.120 0.906
R-Sq = 86.56% R-Sq(adj) =74.79%
I cont T T T cont T Cont
T T Cont I T I T
TWR I T T Cont I
p on off on off
on off p on p off
p on off p
0.00449 % 0.000622 0.000100 % 0.000162 %
0.000106 0.000152 0.000402 % 0.001908 0.00272
0.557429 0.00989 0.05273 0.020617 0.07006 % 0.002032
2 2 2
2
' ' ' ' ' ' ' ' ' ' ' '
' ' ' ' ' ' ' ' ' ' ' '
' ' ' ' ' ' ' ' ' ' '

The ANOVA table for the curtailed quadratic model (Table-5.5) depicts the
value of Coefficient of determination R2 as 86.56 %, which signifies that how much
variation in the response is explained by the model. The higher of R2, indicates the
better fitting of the model with the data.
It is important to check the adequacy of the fitted model, because an incorrect
or under-specified model can lead to misleading conclusions. By checking the fit of
the model one can check whether the model is under specified. The model adequacy
checking includes the test for significance of the regression model, model
coefficients, and lack of fit, which is carried out subsequently using ANOVA on the
curtailed model (Table-5.6).
Chapter-5 Result and Discussion
Page | 57
Table 5.7 Analysis of variance for TWR after elimination of unusual observation
Source D F Seq SS Adj SS Adj MS F P Significant
Regression 14 7.48804 7.48804 0.534860 7.36 0.000
Linear 4 6.07444 0.33044 0.082611 1.14 0.375 NS*
Square 4 0.24551 0.24551 0.061378 0.84 0.517 NS*
Interaction 6 1.16808 1.16808 0.194681 2.68 0.054 S*
Residual
Error
16 1.16294 1.16294 0.072684
Lack-of-Fit 10 0.93853 0.93853 0.093853 2.51 0.136
Pure Error 6 0.22442 0.22442 0.037403
Total 30 8.65098
S* = Significant, NS* Non Significant
The residual analysis as a primary diagnostic tool is also done. Normal probability
plot of residuals has been drawn (Figure 5.2). All the data points are following the
straight line. Thus the data is normally distributed. It is clear from the P value of
Analysis of variance for TWR after elimination of unusual observation and Residual
plots that the predicted model is accurate and prediction of MRR fitted value from
mathematical model is shown in table 5.8
Table 5.8 Prediction of MRR Fitted value from the Mathematical model:
Obs StdOrder TWR Fit SE Fit Residual St Resid
1 1 0.896 1.310 0.211 -0.414 -2.47 R
2 2 0.627 0.782 0.197 -0.155 -0.84
3 3 0.392 0.738 0.197 -0.346 -1.88
4 4 0.098 0.297 0.197 -0.200 -1.08
5 5 0.783 0.685 0.197 0.099 0.53
6 6 0.983 1.063 0.206 -0.079 -0.46
7 7 0.573 0.441 0.225 0.132 0.89
8 8 1.053 1.086 0.211 -0.033 -0.20
9 9 1.220 0.799 0.102 0.421 1.69
10 10 0.727 0.799 0.102 -0.072 -0.29
11 11 0.724 0.799 0.102 -0.076 -0.30
12 12 0.520 0.673 0.197 -0.153 -0.83
13 13 0.722 0.606 0.197 0.116 0.63
14 14 0.285 0.408 0.197 -0.123 -0.67
15 15 1.794 1.664 0.211 0.130 0.78
16 16 1.469 1.503 0.211 -0.034 -0.20
17 17 0.784 0.799 0.102 -0.015 -0.06
18 18 0.720 0.799 0.102 -0.079 -0.32
19 19 2.137 1.813 0.211 0.324 1.93
20 20 1.611 1.393 0.206 0.218 1.25
21 21 0.631 0.799 0.102 -0.168 -0.67
Chapter-5 Result and Discussion
Page | 58
22 22 0.474 0.594 0.211 -0.119 -0.71
23 23 0.845 0.799 0.102 0.046 0.18
24 24 1.061 0.879 0.197 0.182 0.99
25 25 0.644 0.414 0.206 0.230 1.32
26 26 2.045 2.200 0.211 -0.155 -0.92
27 27 0.329 0.397 0.206 -0.068 -0.39
28 28 2.031 2.070 0.219 -0.039 -0.25
29 29 0.615 0.769 0.206 -0.155 -0.89
30 30 1.215 0.910 0.206 0.305 1.75
31 31 0.490 0.210 0.211 0.280 1.67
-0.50 -0.25 0.00 0.25 0.50
99
90
50
10
1
Residual
Percent
0.0 0.5 1.0 1.5 2.0
0.50
0.25
0.00
-0.25
-0.50
Fitted Value
Residual
-0.4 -0.2 0.0 0.2 0.4
8
6
4
2
0
Residual
Frequency
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
0.50
0.25
0.00
-0.25
-0.50
Observation Order
Residual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for TWR
Figure 5.2: Residual Plots for TWR
The residual plot in graph and its interpretation as described below.
' Normal probability plot indicate outlines don't much exist in the data, because
standardized residues are within small range and nature is follow straight line.
' Histogram shows the data are not skewed at one side. TWR measurement
accuracy relies on average of reading, hence the pattern can be considered for
the analysis.
' Residual versus order of the data indicates that symmetry effects in the data
due to time of data collection order.
The below response surface is plotted to study the effect of process variables on the
TWR and is shown in Figures 5.2a-5.2d.
Chapter-5 Result and Discussion
Page | 59
Ip
Ton
10 15 20 25 30 35
70
60
50
40
30
20
Toff 30
% Contribution 10
Hold Values
>
'
'
'
'
'
'
< 0.5
0.5 1.0
1.0 1.5
1.5 2.0
2.0 2.5
2.5 3.0
3.0 3.5
3.5
TWR
Contour Plot of TWR vs Ton, Ip
Fig. 5.2a Combined Effect of Ton and Ip on TWR
Fig. 5.2a shows the estimated response surface for TWR in relation to the
process parameters of Ip and Ton while Toff and % Contribution remain constant at
their middle value. It can be seen from the figure, the TWR increases significantly
with the increase in Ip. However, the TWR tends to increase with increase in Ton.
Hence, maximum TWR is obtained at high peak current & Ton. This is due to their
dominant control over the input energy, i.e. with the increase in Ip generates strong
spark. Also below the 40 pulse on time & 15 Amp current ranges, TWR is slide
increased significantly with decreased Ton & Ip.
Ip
Toff
10 15 20 25 30 35
50
40
30
20
10
Ton 45
% Contribution 10
Hold Values
>
'
'
'
'
'
< 0.5
0.5 1.0
1.0 1.5
1.5 2.0
2.0 2.5
2.5 3.0
3.0
TWR
Contour Plot of TWR vs Toff, Ip
Fig. 5.2b Combined Effect of Toff and Ip on TWR
Fig. 5.2 b shows that the Ip and Toff are significant factors varying linearly with
the response. TWR increases as Ip increases for any value of Toff. The maximum value
of TWR is achieved at 35 amp and low Toff.
Chapter-5 Result and Discussion
Page | 60
Ip
% Contribution
10 15 20 25 30 35
20
15
10
5
0
Ton 45
Toff 30
Hold Values
>
'
'
'
'
< 0.5
0.5 1.0
1.0 1.5
1.5 2.0
2.0 2.5
2.5
TWR
Contour Plot of TWR vs % Contribution, Ip
Fig. 5.2c Combined Effect of % Contribution and Ip on MRR
Fig. 5.2 c shows the estimated response surface for TWR in relation to the
process parameters of Ip and % Contribution while Toff and Ton Contribution remain
constant at their middle value. It can be seen from the figure, the TWR tends to
increase significantly with increasing Ip. The TWR increases with % Contribution up
to 25 amp. From 25 amp to 35 amp MRR going to increased with decreased in %
Contribution.
Ton
% Contribution
20 30 40 50 60 70
20
15
10
5
0
Ip 22.5
Toff 30
Hold Values
>
'
'
'
'
< 0.50
0.50 0.75
0.75 1.00
1.00 1.25
1.25 1.50
1.50
TWR
Contour Plot of TWR vs % Contribution, Ton
Fig. 5.2d Combined Effect of % Contribution and Ton on TWR
Fig. 5.2d shows the estimated response surface for TWR relation to the
process parameters of Ton and % Contribution while Toff and Ip remain constant at
their middle value. It can be seen from the figure, the TWR tends to decreases with %
Contribution increases. But increased with increased with peak current value .
Chapter-5 Result and Discussion
Page | 61

5.3.3 Effect of Process Variables on SR.
The unknown coefficients are determined from the experimental data as
presented in Table- 5.7. The standard errors on estimation of the coefficients are
tabulated in the column 'SE coef'. The F ratios are calculated for 95% level of
confidence. For improving the value of R2 the unusual observation with large
standardized residual (i.e. 13) were eliminated. The regression model is reevaluated
by determining the unknown coefficients, which are tabulated in Table- 5.2. The final
response equation for SR is given in below equation.
Table 5.9 Estimated Regression Coefficients for Surface Roughness after
elimination of unusual observation
Term Coef SE Coef T P
Constant 2.63169 5.30593 0.496 0.627
Ip (amp) 0.44336 0.22806 1.944 0.070
Ton (??s) -0.14531 0.09168 -1.585 0.133
Toff (??s) -0.17297 0.13753 -1.258 0.227
% Contribution 0.15348 0.25675 0.598 0.558
Ip*Ip -0.00648 0.00342 -1.896 0.076
Ton*Ton 0.00397 0.00065 6.062 0.000
Toff*Toff 0.00046 0.00147 0.309 0.761
% Contribution*
% Contribution
0.00308
0.00589
0.523
0.608
Ip*Ton -0.00395 0.00233 -1.694 0.110
Ip*Toff 0.00409 0.00350 1.168 0.260
Ip*% Contribution 0.00489 0.00700 0.698 0.495
Ton*Toff 0.00213 0.00130 1.637 0.121
Ton*%
Contribution
-0.00367
0.00260
-1.414
0.176
Toff*%
Contribution
-0.00509
0.00390
-1.306
0.210
R-Sq = 94.80 % R-Sq(adj) = 90.25 %
The ANOVA table for the curtailed quadratic model (Table-5.7) depicts the
value of Coefficient of determination R2 as 94.80 %, which signifies that how much
I cont T T T cont T Cont
T T Cont I T I T
SR I T T Cont I
p on off on off
on off p on p off
p on off p
0.00489 % 0.00213 0.00367 % 0.00509 %
0.00397 0.00046 0.00308 % 0.00395 0.00409
2.63169 0.44336 0.14531 0.17297 0.15348 % 0.00648
2 2 2
2
' ' ' ' ' ' ' ' ' ' ' '
' ' ' ' ' ' ' ' ' ' ' '
' ' ' ' ' ' ' ' ' ' '
Chapter-5 Result and Discussion
Page | 62
variation in the response is explained by the model. The higher of R2, indicates the
better fitting of the model with the data.
It is important to check the adequacy of the fitted model, because an incorrect
or under-specified model can lead to misleading conclusions. By checking the fit of
the model one can check whether the model is under specified. The model adequacy
checking includes the test for significance of the regression model, model
coefficients, and lack of fit, which is carried out subsequently using ANOVA on the
curtailed model (Table-5.8).
Table 5.10 Analysis of variance for SR after elimination of unusual observation
Source D F Seq SS Adj SS Adj MS F P Significant
Regression 14 177.276 177.276 12.6626 20.84 0.000
Linear 4 142.761 7.674 1.9185 3.16 0.043 S*
Square 4 27.768 27.768 6.9420 11.43 0.000 S*
Interaction 6 6.747 6.747 1.1246 1.85 0.152 NS*
Residual 16 9.720 9.720 0.6075
Lack-of-Fit 10 7.664 7.664 0.7664 2.24 0.168
Pure Error 6 2.056 2.056 0.3427
Total 30 186.996
S* = Significant, NS* Non Significant
The residual analysis as a primary diagnostic tool is also done. Normal probability
plot of residuals has been drawn (Figure 5.3). All the data points are following the
straight line. Thus the data is normally distributed. It is clear from the P value of
Analysis of variance for SR after elimination of unusual observation and Residual
plots that the predicted model is accurate and prediction of SR fitted value from
mathematical model is shown in table 5.11
Table 5.11 Prediction of SR Fitted value from the Mathematical model
Obs StdOrder SR Fit SE Fit Residual St Resid
1 1 11.423 11.060 0.609 0.363 0.75
2 2 9.542 9.892 0.569 -0.350 -0.66
3 3 10.606 10.654 0.569 -0.048 -0.09
4 4 4.757 4.322 0.569 0.435 0.82
5 5 6.802 5.939 0.569 0.864 1.62
6 6 7.014 7.678 0.595 -0.664 -1.32
7 7 4.185 4.284 0.650 -0.099 -0.23
8 8 8.635 8.009 0.609 0.626 1.29
9 9 8.137 7.195 0.294 0.941 1.30
Chapter-5 Result and Discussion
Page | 63
10 10 6.585 7.195 0.294 -0.610 -0.85
11 11 6.664 7.195 0.294 -0.532 -0.74
12 12 10.262 10.134 0.569 0.128 0.24
13 13 5.623 5.078 0.569 0.545 1.02
14 14 4.677 4.165 0.569 0.512 0.96
15 15 11.377 11.782 0.609 -0.404 -0.83
16 16 6.822 6.611 0.609 0.212 0.44
17 17 7.265 7.195 0.294 0.070 0.10
18 18 7.080 7.195 0.294 -0.116 -0.16
19 19 10.656 10.658 0.609 -0.003 -0.01
20 20 15.977 15.433 0.595 0.544 1.08
21 21 7.627 7.195 0.294 0.431 0.60
22 22 7.709 6.754 0.609 0.956 1.97
23 23 6.619 7.195 0.294 -0.577 -0.80
24 24 9.312 9.394 0.569 -0.082 -0.15
25 25 6.703 7.077 0.595 -0.374 -0.74
26 26 10.707 10.363 0.609 0.344 0.71
27 27 4.513 6.095 0.595 -1.582 -3.15 R
28 28 7.663 8.210 0.633 -0.547 -1.20
29 29 7.026 7.500 0.595 -0.473 -0.94
30 30 6.942 7.507 0.595 -0.565 -1.12
31 31 7.188 7.134 0.609 0.054 0.11
-2 -1 0 1
99
90
50
10
1
Residual
Percent
5.0 7.5 10.0 12.5 15.0
1
0
-1
Fitted Value
Residual
-1.5 -1.0 -0.5 0.0 0.5 1.0
8
6
4
2
0
Residual
Frequency
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
1
0
-1
Observation Order
Residual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for Ra
Figure 5.3: Residual Plots for SR
The residual plot in graph and its interpretation as described below.
' Normal probability plot indicate outlines don't much exist in the data, because
standardized residues are within small range and nature is follow straight line.
Chapter-5 Result and Discussion
Page | 64
' Histogram shows the data are not skewed at one side. SR measurement
accuracy relies on average of reading, hence the pattern can be considered for
the analysis.
' Residual versus order of the data indicates that symmetry effects in the data
due to time of data collection order.
The below response surface is plotted to study the effect of process variables on the
TWR and is shown in Figures 5.3a-5.3d.
IP
Ton
10 15 20 25 30 35
70
60
50
40
30
20
Toff 30
%Contribution 10
Hold Values
>
'
'
'
'
'
'
< 2
2 4
4 6
6 8
8 10
10 12
12 14
14
Ra
Contour Plot of Ra vs Ton, IP
Fig. 5.3a Combined Effect of Ton and Ip on SR
Fig. 5.3a shows the estimated response surface for SR in relation to the
process parameters of Ip and Ton while Toff and % Contribution remain constant at
their middle value. It can be seen from the figure, the SR increases significantly with
the increase in Ip. However, the SR tends to increase with increase in Ton.
IP
Toff
10 15 20 25 30 35
50
40
30
20
10
Ton 45
%Contribution 10
Ho ld Values
>
'
'
'
'
'
< 4
4 5
5 6
6 7
7 8
8 9
9
Ra
Contour Plot of Ra vs Toff, IP
Fig. 5.3b Combined Effect of Toff and Ip on SR
Chapter-5 Result and Discussion
Page | 65
Fig. 5.3b shows that SR increases as Ip increases. The maximum value of SR
is achieved at peak value of Toff & Ip. However, with the increase in Toff, SR
decreases. It is because it takes time before next spark and reduces the crater effect
due to higher temperature.

IP
%Contribution
10 15 20 25 30 35
20
15
10
5
0
Ton 45
Toff 30
Hold Values
>
'
'
'
< 4.5
4.5 6.0
6.0 7.5
7.5 9.0
9.0
Ra
Contour Plot of Ra vs %Contribution, IP
Fig. 5.3c Combined Effect of % Contribution and Ip on SR
Fig. 5.3c shows the estimated response surface for SR in relation to the
process parameters of Ip and % Contribution while Toff and Ton Contribution
remain constant at their middle value. It can be seen from the figure, the SR tends to
increase significantly with increasing Ip. The SR decreased with % Contribution
increased. It is because, if % Contribution increased, the hardness properties of
material as become increased.
Ton
%Contribution
20 30 40 50 60 70
20
15
10
5
0
IP 22.5
Toff 30
Hold Values
>
'
'
'
'
'
< 6
6 8
8 10
10 12
12 14
14 16
16
Ra
Contour Plot of Ra vs %Contribution, Ton
Fig. 5.3 d Combined Effect of % Contribution and Ton on SR
Chapter-5 Result and Discussion
Page | 66
Fig. 5.3d shows the estimated response surface for SR relation to the process
parameters of Ton and % Contribution while Toff and Ip remain constant at their
middle value. It can be seen from the figure, the SR tends to increased with Ton
increases of any value of % contribution.
5.4 Confirmation Test
Table 5.12 Plan of confirmation experiments and results.
Sr
No
Ip
(A)
Ton
(?? m)
Toff
(?? m)
%
contribution
MRR
(mm3/min)
TWR
(mm3/min)
Ra
(?? m)
Predicted Exp. Predicted Exp. Predicted Exp.
1 36 45 30 0 30.46 37.42 9.598 8.93 7.703 7.524
2 17 40 10 5 12.583 14.292 1.338 1.457 6.237 6.048
3 9 60 50 10 3.8996 3.407 4.102 4.078 7.752 7.748
4 28 40 20 15 13.854 14.848 2.232 3.237 8.025 8.805
5 21 30 40 20 4.577 4.829 0.823 0.641 5.631 6.371
The mathematical models developed for MRR, TWR and Ra, have already
been validated statistically through lack-of-fit test as described above. The
coefficient of variation (R2) for these models was appropriate, which indicates the
suitability of these models for making future predictions. In addition to statistical
validation, the developed models have also been validated by conducting
confirmation experiments selected randomly within range rather than 31
experiments conducted for analysis. Experiments have been performed to verify
the adequacy of the developed mathematical models. The details of the confirmation
experiments are given in Table 5.12
Chapter-6 Conclusion And Future Work Scope
Page | 67
Chapter
6
CONCLUSION AND FUTURE WORK SCOPE
6.1 Conclusion.
In present study production of aluminum based hybrid MMCs using stir
casting methodology and parametric analysis has been carried out for three
responses, Material Removal Rate, Tool wear rate and Surface Roughness. The
experiments were conducted under Central composite Rotatable Design. The
process parameters with significant influence on various responses were determined
using RSM. This study highlights the development of a comprehensive mathematical
model for correlating the interactive and higher order influences of various electrical
discharge machining parameters through response surface methodology (RSM),
utilizing relevant experimental data as obtained through experimentation. The
research findings of the present study based on RSM models can be used effectively
in machining of aluminum based hybrid MMCs in order to obtain best possible
EDM efficiency. Minitab 16 software was used for analyze the experimental data.
Following conclusions drawn after analysis.
1. 15wt%SiC + 5wt%Gr hybrid composites material exhibit relatively higher
hardness compared to others Al based hybrid composite materials i.e.5wt%SiC,
5wt%SiC + 5wt%Gr, 10wt%SiC + 5wt%Gr.
2. The maximum tensile strength &density is found for 15wt%SiC + 5wt%Gr
hybrid composite.
3. The porosities of the obtained cast composite have found to be increased with the
increase of SiC particulates. This is due to the vortex found because of the stirring
action, which enhances the dissolution of gases and causes more bubbles to be
formed inside the melt which decreases the hardness of Al-Gr Composites.
Chapter-6 Conclusion And Future Work Scope
Page | 68
4. The addition of 5wt% of graphite as a secondary reinforcement reduces the
tensile strength slightly. This may due to the HCP structure of graphite which
allows slipping of different layers at relatively low forces.
5. The MRR tends to increase significantly with the increase in Ip for any value of
Ton. However, for any peak current value, MRR tends to increase first with
increase Ton up to 40 ??s and then decreased with increased value of Ton. Hence,
maximum MRR is obtained at high peak current. This is due to their dominant
control over the input energy, i.e. with the increase in Ip generates strong spark.
6. It can be seen from the figure, the MRR tends to increase significantly with
increasing Ip. The MRR increases with % Contribution up to 25 Amp. From 25
amp to 35 amp MRR going to decrease with increase in % Contribution.
7. The TWR increases significantly with the increase in Ip. However, the TWR
tends to increase with increase in Ton. Hence, maximum TWR is obtained at
high peak current & Ton. Also below the 40 pulse on time & 15 Amp current
ranges, TWR is slide increased significantly with decreased Ton & Ip.
8. The TWR increases with % Contribution up to 25 amp. From 25 amp to 35 amp
MRR going to increased with decreased in % Contribution.
9. The SR increases significantly with the increase in Ip. However, the SR tends to
increase with increase in Ton.
10. The SR tends to increase significantly with increasing Ip. The SR decreased with
% Contribution increased. It is because, if % Contribution increased, the
hardness properties of material as become increased.
6.2 Scope for Future Work.
' The effect of machining parameters on recast layer thickness and
Cutting rate should be investigated.
' The effect of process parameters such as flushing pressure,
conductivity of dielectric, polarity etc. may also be investigated.
' Higher order mathematical model can also generate for material rather
than used for this investigation.
' Also developed artificial neural network and compare with RSM model
References
Page | 69
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Website
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www.springerlink.com

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