Wire EDM

1. Accepted Manuscript
Parametric Optimization of Electric Discharge Coating on
Aluminium-6351 Alloy with Green Compact Silicon Carbide and
Copper Tool: A Taguchi Coupled Utility Concept Approach
Sujoy Chakraborty , Siddhartha Kar , Subrata Kumar Ghosh ,
Vidyut Dey
PII: S2468-0230(17)30011-1
DOI: 10.1016/j.surfin.2017.02.001
Reference: SURFIN 66
To appear in: Surfaces and Interfaces
Received date: 17 October 2016
Revised date: 11 January 2017
Accepted date: 6 February 2017
Please cite this article as: Sujoy Chakraborty , Siddhartha Kar , Subrata Kumar Ghosh , Vidyut Dey ,
Parametric Optimization of Electric Discharge Coating on Aluminium-6351 Alloy with Green Compact
Silicon Carbide and Copper Tool: A Taguchi Coupled Utility Concept Approach, Surfaces and Interfaces
(2017), doi: 10.1016/j.surfin.2017.02.001
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Parametric Optimization of Electric Discharge Coating on Aluminium-6351 Alloy with
Green Compact Silicon Carbide and Copper Tool: A Taguchi Coupled Utility Concept
Approach
Sujoy Chakraborty1
, Siddhartha Kar2
, Subrata Kumar Ghosh3
, Vidyut Dey4
1,2,4
Department of Production Engineering, NIT Agartala, India
3
Department of Mechanical Engineering, NIT Agartala, India
ABSTRACT
In this article, an attempt has been made for optimizing the process parameters of Electro Discharge
Coating (EDC) on Aluminium-6351 alloy using SiC/Cu green compact tool by the application of utility
concept of Taguchi method. A set of best possible combination of process parameters like
compaction load, tool composition, current and pulse on time on two output parameters such as
micro hardness and surface roughness has been developed. The experiments are designed based on
L16 orthogonal array. The analysis of means (ANOM) is used to determine the optimal level of the
process parameters and analysis of variance (ANOVA) is used to identify the relative importance
among the process parameters. The ANOVA results shows that the most important process
parameter is pulse on time followed by current, compaction load and tool composition which affects
the optimization of the output parameters. Based on the optimal levels of process parameters, a
confirmation test has been carried out to explain the effectiveness of the Taguchi optimization
technique. The highest micro hardness of 301 μm and the surface roughness of 2·63 μm are
successfully obtained. Moreover, to confirm the presence of tool materials on the workpiece
surface, XRD, SEM, EDX and also optical microscopic image analysis have been carried out.
KEYWORDS: Electric discharge coating, Powder metallurgical tool, Process parameter, Micro
hardness,
Surface roughness, Utility concept
1. INTRODUCTION
Electro discharge machining (EDM) is a well known nonconventional machining process for
machining extremely hard materials (such as Tungsten, Inconel etc.)1
. The last decade has seen
emergence of Electro discharge machining as a tool for surface modification by premeditated
deposition of tool materials over the substrate surface. Surface modification by EDM is an emerging
technique which is still in developing stage. The development of hard carbide layers and tool wear
are the inherent feature of EDM2-5
. This is due to the reaction of tool materials and disintegration of
hydrocarbon5
from the dielectric6
which led to the invention of EDC. The coating of a hard material
over the top surface of substrate is made more soaring by intentional transfer of tool materials with
the use of powder metallurgy compact, either in semi sintered or green state1
. For fine deposition,
minimum flushing pressure or zero flushing pressure of the dielectric medium is required otherwise
the deposited materials on the workpiece surface will erode due to that flushing pressure1
. There
are several ways to carry out the surface modification by EDM7
such as EDC with the use of semi
sintered or green compact electrode, EDC with solid electrode, EDC with thin wires and EDC with
powder suspended in working oil1
. But surface modification with conventional electrodes has not
met with great success8
. Several composition of electrode like WC/Co, TiC/Cu, TiC, Ti, Cu-W etc.6, 9-14
prepared by powder metallurgy method either in semi sintered or green compact state is used for
changing the workpiece surface characteristics1
. The significant alteration of w/p surface has also
been done by suspending powders like Al2O3, TiC, Ti, WC and TiH2 etc. in dielectric medium7, 15-17
. But,
to maintain the concentration of those powder particles near the machining region is a gruelling
task1
. EDC is mainly carried out in reverse polarity to that of EDM, i.e. work piece is connected to
cathode and tool to the anode. This is done to facilitate the deposition mechanism by eroding more
amounts of tool materials. In EDC, both deposition phenomenon and machining takes place
simultaneously which makes the process complicated. But it can be controlled by selecting
appropriate process parameters. An increase in hardness of the coated layer compare to the base

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metal has been reported by many researchers3,6-7,9-11,15-16,18-20
. The hardness of the modified surface
along with surface finish invariably depends on the input process parameters. The intended
outcomes of the responses are of contradictory nature which makes the process difficult to be
optimized simultaneously. Though, surface modification of Al-6351 alloy with green compact SiC/Cu
tool and also the optimization of those process parameters by OEC method have been discussed1
,
but the utility concept by Taguchi technique is not yet used to identify the influence of EDC process
parameters over the output responses. Moreover SEM and EDX analysis is also not been conducted
before to confirm the presence of the tool material on the workpiece surface.
In this present study, an attempt is made for determining the best combination values of EDC
parameters like tool composition, compaction load, current and pulse on time by Taguchi technique
using utility concept, in order to maximize the micro hardness, as well as to minimize the surface
roughness simultaneously. The electrode has been prepared by green compaction powder
metallurgy method, which consists of silicon carbide and copper of mesh 325 each and Al-6351 alloy
is used as base metal within this range of study.
2. METHODOLOGY
2.1 Taguchi Approach
The conventional design of experiments (DOE) methods involves one variable at a time by keeping
the other parameters at fixed levels21
. Generally this method takes a huge time and also needs a
substantial number of experiments to be done21
. For example, when there are ‘k’ numbers of factors
with ‘l’ levels described for all factors, then ‘lk
’ numbers of experiments are essentially to be done21
.
Taguchi method uses orthogonal array to investigate various design parameters by means of a single
quality characteristic22
. Generally, orthogonal experiments are conducted to establish the relative
importance of each and every parameter in respect of their main effects on the output response23
by
determining the best possible combination of each process parameter level24
. A suitable array has
been selected based on the number of process parameters and their levels. A process parameter
and its setting level in every experiment are represented by each column of the orthogonal array
and similarly an experiment with different levels of the process parameters in that experiment are
represented by each row24
. Based on the quality characteristic such as lower the better, higher the
better and nominal the better25
, the best possible level of each parameter for a particular response
can be determined by using signal to noise (S/N) ratio of Taguchi design5
. The higher levels S/N ratios
of the equivalent factors are considered to be optimum for a particular output response, in spite of
the selected quality characteristics for that particular response5
. Analysis of variance (ANOVA) is a
technique to find statistically significant process parameters26
. Thus, the best possible combination
of controllable process parameters for each output response can be determined by using S/N ratio
and ANOVA1
.
2.2 Taguchi Approach with Utility Concept
Taguchi technique can be used for handling single objective problems but not sufficient enough to
handle multi objective problems27
. These types of problems are solved by giving individual weights
to the responses on priority basis5
. Utility of a product is represented by a composite index which is
the summation of evaluation of multiple characteristics21,28
. The overall utility of a product is the
sum of utilities of each of the quality performance characteristics28,29
. The overall utility function is
given by28
:
∑
(1)
where, X is a measure of effectiveness of an attribute i, n is the number of attributes evaluating the
outcome space and is the utility of the ith
attribute. Based on the requirements, the attributes
may be given priorities and weights. Hence, the weighted form of Equation 1 is30
:

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∑
(2)
where, ∑ (3)
and is the weight assigned to attribute i.
3. EXPERIMENTAL PROCEDURE
3.1 Plan of Experiments
Based on the pilot runs (experiments), the higher level and also the lower levels of factors have been
selected which is given in Table 1. To conduct the experiments, the array size is determined by
counting Degrees of Freedom (DOF) of the factors within this range of study. This type of level of
factors which is unequally distributed can be tailored to fit the mixed levels of factors and its DOF
must be equal or exceed the sum of DOF of all the factors of experiment31
. The DOFs are calculated
as follows:
For 3 four-level factor, DOFs are : 3*(4 – 1) = 9
For 1 two-level factors, DOFs are : 1*(2 – 1) = 1
Total DOFs of experimental factors : 9+1 = 10
The DOFs of an L-16 Taguchi array : 16−1 = 15
Thus, an L-16 Taguchi orthogonal array is selected with mixed level shows in Table 2. By pilot
experiments, it has been seen that reverse polarity was more suitable for coating5
. Duty cycle has
been varied from 0.5 to 0.57 throughout the experimentation. Time of experiment is fixed for 5
minutes. To maintain the gap voltage in the range of 55-60 Volts throughout the experiment, the
gap sensing knob is adjusted1
.
Table 1: Process parameters and their levels5
Parameter Code Unit
Level
1 2 3 4
Compaction load P ton 5 10 15 20
Current Ip ampere 2 4 6 8
Pulse on time Ton µs 11 21 50 100
Tool Composition Ct (SiC:Cu) 30:70 50:50 - -
Table 2: L16 orthogonal array5
Exp.
No.
Input parameters
P (ton) Ip (A) Ton (µs) Ct (SiC:Cu)
1 5 2 11 30:70
2 5 4 21 30:70
3 5 6 50 50:50
4 5 8 100 50:50
5 10 2 21 50:50
6 10 4 11 50:50
7 10 6 100 30:70
8 10 8 50 30:70
9 15 2 50 30:70
10 15 4 100 30:70
11 15 6 11 50:50
12 15 8 21 50:50
13 20 2 100 50:50

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14 20 4 50 50:50
15 20 6 21 30:70
16 20 8 11 30:70
3.2 Materials and Processes
The experiments have been carried out in Die Sinking EDM (Sparkonix F25). Based on the results of
test run (pilot experiments), tool and work piece are connected to anode and cathode respectively
(i.e. reverse polarity) for better deposition5
. Mechanism of EDC is shown in Fig. 1. For making tool
electrode, Silicon carbide (325 mesh size) and Copper powders (325 mesh size) were mixed properly
by using mortar and pestle made of ceramic (Fig. 2) in requisite amount by weighing to fulfil the
required composition. Addition of Cu powder enhances the binding strength, electrical conductivity
and thermal conductivity of the product. So, proper mixing of the powders confirms uniform
distribution of such properties throughout the product. The tool electrodes were prepared by
powder metallurgy process in a pellet press (Fig. 3) in the form of pellets (Fig. 4). The pellet press
consists of a die and punch assembly. The pellets were prepared by varying the compaction load at
different composition (30:70 and 50:50 weights %) of SiC and Cu powders in pellet press machine.
The size of the pellets was 5 mm in height and 13 mm in diameter1
. The pellets were then joined to
an equal diameter of copper rod by an electrically conductive epoxy resin (Fig.5). Aluminium 6351
alloy has been selected as a substrate due to its large application in the field of turbine blades,
automotive industries, piping, aerospace industries, roll texturing etc1
. The dimension of the
workpiece was 25×25×5 mm1
. Total 16 numbers of work-pieces were prepared by using wire-cut
EDM machine. The burrs on the edges were removed by using emery paper (abrasive paper) of
different grades. Kerosene oil has been used in this coating process as dielectric. The photograph of
the specimens after EDC process has been shown in Fig. 6.
Fig.2. Die and punch assembly along with pestle and
mortar
Fig.1. Mechanism of EDC Process1
Fig.3. Pellet press Fig.4. Pellets

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Fig.5. Pellets joined with copper rod (Tool Electrode) Fig. 6. Photograph of 16 nos. of
specimens after
experiment (Coated specimens)
Average surface roughness (Ra) has been taken for this present study due its wider use in the
manufacturing industry. Ra is the arithmetic average value of the departure of profile from the
centerline along sampling length21
which is to be measured by a non-contact profilometer (Taylor
Hobson 3D Profilometer) on the coated samples. Micro Hardness (Hm) value of the coated specimens
has been taken by a Vickers micro-hardness tester (MATSUZAWA MMT-X series) by giving 50 gm of
load and at dwell time of 10 sec1
.
4. RESULTS AND DISCUSSION
In this present study of EDC process parameters optimization, the main objective is to maximize the
micro hardness and minimize the surface roughness. This is because, both the output parameters
are equally important in terms of surface coating.
The equation of Ra (average surface roughness) is given below:
∫
(4)
where, L is the sampling length, Z(x) is the ordinate of the profile curve and x is the profile direction.
The unit of Ra is µm.
The computed values of Ra (Surface roughness) and Hm (Micro hardness) have been summarized in
Table 3. As S/N ratio can reflect both mean and variation of the performance characteristics22-23
, so
Taguchi design of S/N ratio is used for the evaluation characteristic to elucidate the trial results in
the optimum setting analysis21
. In this present study, the utility concept of Taguchi design is
introduced for optimizing the various performance characteristics (Ra and Hm). Here, micro-hardness
(Hm) is to be maximized and surface roughness (Ra) to be minimized. Hence, “larger the better type’’
and ‘‘smaller the better type’’ characteristics for Hm and Ra are selected respectively. The S/N ratios
for Hm and Ra responses are given below22-23
:
⁄
(5)
(6)
Multi response S/N ratio of the utility concept is given below 21,29
:
(7)

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where, and are the S/N ratio weighting factors for the output responses Hm and Ra
respectively. To optimize the various output responses based on their priorities, these weighting
factors are determined21
. In this present study, 0.5 equal weighting factors are considered for both
the output responses Hm and Ra respectively. The calculated values of S/N ratio for each responses
and the multi response S/N ratio for each experiments are enlisted in Table 3.
Table 3: Responses with their S/N ratios and computed multi response S/N ratio (η)
Exp
No.
Response S/N Ratio
Set 1 Set 2 Set 3 Average
Hm
(HV)
Ra
(µm)
Hm
(HV)
Ra
(µm)
Hm
(HV)
Ra
(µm)
Hm
(HV)
Ra
(µm)
η1 for Hm η2 for Ra η
1 147 2.75 148 2.71 152 2.79 149 2.75 43.4637 -8.7867 17.3385
2 278 3.02 303 2.7 307 2.95 296 2.89 49.4258 -9.218 20.1039
3 195 3.41 178 3.66 194 3.34 189 3.47 45.5292 -10.8066 17.3613
4 206 4.55 181 4.78 186 4.53 191 4.62 45.6207 -13.2928 16.1639
5 242 2.84 231 2.67 214 2.59 229 2.70 47.1967 -8.6273 19.2847
6 243 2.93 257 2.83 277 2.55 259 2.77 48.2660 -8.8496 19.7082
7 199 3.69 207 3.49 188 3.65 198 3.61 45.9333 -11.1501 17.3916
8 188 2.98 197 2.86 194 2.77 193 2.87 45.7111 -9.1576 18.2768
9 210 2.68 203 2.66 214 2.88 209 2.74 46.4029 -8.755 18.8240
10 226 3.02 238 2.79 241 2.77 235 2.86 47.4214 -9.1273 19.1470
11 201 2.97 196 3.05 188 2.74 195 2.92 45.8007 -9.3077 18.2465
12 278 3.35 291 3.16 283 3.21 284 3.24 49.0664 -10.2109 19.4277
13 199 2.85 193 2.91 178 2.73 190 2.83 45.5751 -9.0357 18.2697
14 258 3.04 265 2.87 269 2.64 264 2.85 48.4321 -9.0969 19.6676
15 298 2.81 295 2.92 310 2.46 301 2.73 49.5713 -8.7233 20.4240
16 178 2.71 183 2.83 179 2.35 180 2.63 45.1055 -8.3991 18.3532
The effects of the EDC process parameters on the two output responses (Hm and Ra) based on Mean
S/N ratios are explained below:
Fig. 7 Effect of input parameters on Mean S/N Ratio of micro hardness
Effect of input parameters on mean S/N ratio of micro hardness is shown in Fig. 7. Here, ‘larger the
better type’’ characteristic for Hm is selected. From the figure, it is clearly seen that, micro-hardness

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value increases with increase in compaction load as with increase in load the layer developed at
slower rate and the probability of formation of pore is less. In case of current and pulse duration, the
micro hardness value follows a similar trend which increases from 1st
level to 2nd
level and is highest
at 2nd
level due to the adequate amount energy available for sufficient time period which helps in
deposition of tool material and also may be due to the presence of more carbon particles on the
workpiece surface from the dielectric fluid. As the current and pulse duration increases to 3rd
level
and 4th
level, micro hardness decreases gradually may be due to the less number of carbon particles
presence on the workpiece surface because most of the carbon particles flushes away from the work
surface at high current and longer pulse duration32
. Micro hardness values are better with tool
composed of 50:50 than 30:70 by ratio weight of SiC and Cu due to the presence of more amounts of
SiC particles which are harder in nature.
Fig. 8 Effect of input parameters on Mean S/N Ratio of surface roughness
Effect of input parameters on mean S/N ratio of surface roughness is shown in Fig. 8. Here, ‘smaller
the better type’ characteristic for Ra is selected. From the fig., it is clearly seen that, surface
roughness value decreases with increase in compaction load because with increase in load the
density of compacted powder is more thus leading to less wear and hence less deposition. With the
increase of current and pulse duration, the deposition rate is more and formation of crater on the
workpiece surface is also more. Therefore surface roughness is also more. As we know that, the
amount of tool-electrode materials deposited on the substrate is directly proportional to the
amount of applied energy which is controlled by the pulse duration and the peak current33-34
.
Therefore, with longer pulse duration and at high current, more amount of material will be eroded
from the electrode and hence deposited on the top surface of the work-piece. Surface roughness
values are also higher with tool composed of 50:50 than 30:70 by ratio weight of SiC and Cu may due
to the presence of more amounts of SiC particles which leads to more deposition of tool materials
on the workpiece surface as the compactness of the tool materials are less due to the less
percentage of Cu and also leads to more craters formed in workpiece surface.
4.1 Analysis of Means
The analysis of means (ANOM) is the process of estimating the main effects of each process
parameter by determining the best possible parametric combination settings21
. Due to the overall
mean response, the parameter level gets deviated. The η overall mean related to sixteen trials is
calculated as follows21
:
∑
(8)
The effect of a process parameter level i for parameter j is28
:

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∑
(9)
We know that the best possible combination of process parameter level gives the highest
S/N ratio30
. The maximization of multi response S/N ratio for the optimal level related to each
process parameter is30
:
{ }
(10)
The ANOM result for multi-response S/N ratio35
is enlisted in Table 4 and the effect of input
parameters on mean multi-response S/N Ratio is shown in figure 9. Thus, from the table 4 and fig 9,
it is clearly seen that the optimal process parameter setting is P at level 4, Ip at level 2, Ton at level 2
and Ct at level 1, within this range of study. Thus, the best possible combination values of process
parameter for getting maximum micro hardness and minimum surface roughness are 20 ton
compaction load, 4 ampere current, 21 µs pulse on time and SiC30:Cu70 composition.
Table 4: Mean response table for multi response S/N ratio (η)
Process
Parameter
Mean multi response S/N ratio
Max-Min
Level 1 Level 2 Level 3 Level 4
P 24.95 25.41 25.53 25.65 0.69
Ip 25.30 25.87 25.26 25.11 0.76
Ton 25.29 25.94 25.35 24.96 0.97
Ct 25.44 25.33 - - 0.11
Fig. 9 Effect of input parameters on Mean Multi Response S/N Ratio, η
4.2 Analysis of Variance
Analysis of variance (ANOVA) is a technique to find statistically5
important process parameters26
. It is
used to find a relative importance among the each process parameter in respect of percentage
contribution on the overall response22-23,36
. The variance of error for the effects and confidence
interval of the prediction error21,36
is also determined by ANOVA. If the estimates are considerably
different, then F test (statistical tool or F ratio) can be used by using a desirable confidence level22,37
.
The greater F ratio indicates that the variation of the parameter has a major impact on the η. ANOVA
results carried out on various performance characteristics are given in table no. 5. Higher percentage
contribution parameters are ranked higher in respect of importance in the experiment and also have
major effects in controlling the overall output responses21
. From the ANOVA table (Table 5), it is

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clearly noticed that the pulse on time has main contribution (43.91%) in optimizing the various
performance characteristics followed by current (29.45%), compaction load (22.97%) and tool
composition (0.92%). Further, the ANOVA has resulted in 6.10% of error contribution, which shows
that the effects of interaction of the process parameters are very less for simultaneously minimizing
the surface roughness and maximizing the micro hardness30
. The model has significantly high
correlation coefficient (R2
= 97.25 %) and adjusted correlation coefficient (91.75%) which indicates
better fitting and also high importance of the model. Hence, a good correlation between multi-
response parameters and the input variables exists38
.
Table 5: Result of ANOVA for multi response S/N ratio (η)
Source
Degree of
Freedom
Sum of
square
Mean
square
F-
ratio
P
Contribution
(%)
Compaction
load
3 4.6797 1.5599 13.93 0.007 22.97
Current 3 5.9983 1.9994 17.85 0.004 29.45
Pulse on time 3 8.9455 2.9818 26.62 0.002 43.91
Tool
Composition
1 0.1869 0.1869 1.67 0.253 0.92
Error 5 0.5600 0.1120 - - 2.75
Total 15 20.3704 - - - 100
R2
= 97.25%, R2
(adjusted) = 91.75%
4.3 Confirmation Test of Optimal Result
The final step of Taguchi design is to conduct a confirmation test30
. An alternate method has been
adopted to predict the various characteristics as the best possible combination of process
parameters is not included in the main design of experiment. The predicted optimum value of mean
multi response S/N ratio (ηopt) is determined as21,30
:
∑
(11)
where, is the total mean, is the mean of optimum level i of parameter j and is the
number of most important design parameters21
that affect the various performance. The predicted
mean of multi response S/N ratio39
( ) for the optimum parameter levels (4-2-2-1) is 21.5049 dB.
Based on the best possible combination of process parameter levels 4-2-2-1, the validation
experiment has been conducted. The average value of surface roughness is 2.32 µm and the
calculated value of micro hardness for the optimal parameter setting21
is 274 HV. The mean multi
response S/N ratio ( ) for the confirmation test is 20.7226 dB. The confidence interval (CI) value
of at 95% band is determined for the best possible combination of process parameter level, in
order to justify the closeness of obtained S/N ratio value with that predicted value28
. The CI is given
by40-41
:
√
(12)
where, is the degrees of freedom for error28
= 5, is the F value for 95% confidence
interval28
at DOF 1 and = 6.61, is the variance of error30
= 0.1120, ; N = Total trial

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number = 16, v = Degrees of freedom of p process parameters21
= 10, is the validation test trial
number = 3. In this present study, the difference between and is 0.7823 dB (i.e. prediction
error). The value of this error is very much within the CI value of ±0.8693 dB and hence, extenuating
the capability and accuracy of the proposed model.
4.4 Material Deposition Rate (MDR)
The surface properties like hardness and roughness analyzed in this study are highly dependent on
the deposition phenomenon of tool materials on the workpiece surface. Thus it is essential to
analyze the effect of the process parameters on the material deposition rate (MDR) of the EDC
process. A wide range of MDR was obtained which varied from 0.08 to 0.81 mg/min (Table.6). The
effect of input parameters on the MDR is depicted in Fig. 10. From this figure, it is clearly observed
that MDR is decreasing with the increase of compaction load. It may be due to that with the increase
in compaction load, the binding strength of the pellets increases resulting in lowering MDR. It is also
seen that MDR increases with increase in current and pulse duration which may be due to the
increment in energy content and the same energy applied for a longer duration respectively.
Moreover, the inclusion of Cu in the fabrication of the pellets imparts higher binding strength and
thermal conductivity too. So, the increase in the ratio of Cu reduces the deposition rate of tool
material. As a result of which MDR values are higher with tool composed of 50:50 than 30:70 by
ratio weight of SiC and Cu.
Table 6.− Experimental results for MDR1
(in mg/min)
Exp. no. 1 0.27 (mg/min)
Exp. no. 2 0.31 (mg/min)
Exp. no. 3 0.69 (mg/min)
Exp. no. 4 0.81 (mg/min)
Exp. no. 5 0.48 (mg/min)
Exp. no. 6 0.51 (mg/min)
Exp. no. 7 0.37 (mg/min)
Exp. no. 8 0.34 (mg/min)
Exp. no. 9 0.15 (mg/min)
Exp. no. 10 0.18 (mg/min)
Exp. no. 11 0.40 (mg/min)
Exp. no. 12 0.46 (mg/min)
Exp. no. 13 0.24 (mg/min)
Exp. no. 14 0.21 (mg/min)
Exp. no. 15 0.08 (mg/min)
Exp. no. 16 0.10 (mg/min)

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Fig. 10 Effect of input parameters on mean of material deposition rate
5. SURFACE CHARACTERIZATION:
Microscopic image of pellet consisting of Silicon carbide and Copper (50:50) is shown in Fig. 11,
where red colour corresponds to Cu and grey colour corresponds to SiC. From this figure, it is clear
that the tool material comprises of SiC and Cu which is going to be deposited over the workpiece
surface during EDC process. Fig.12 shows the microscopic image of base metal (parent) and
deposited layer. The deposited materials (layer) on the top surface of the substrate are clearly seen
from this figure. To confirm the presence of this deposited materials on the top surface of substrate,
X-Ray Diffraction (XRD) analysis has been carried out42
. The XRD plot of a coated layer using
SiC50:Cu50, 20 ton compact tool is shown in Figure 13. The peaks of Al, Si, SiC and Cu along with the
peaks of inter-metallic phases Al2Cu and Al2CuMg are clearly seen from the figure5
. Therefore, it
confirms the transfer of tool materials on the substrate surface in the elemental state and also in the
form of carbides (SiC) and thus forming a hard layer of composite5,43-44
. Moreover, EDX has also
been carried out on the upper surface of one of the specimens. The EDX plot of a coated layer at
SiC50:Cu50 composition, 10 ton compaction load, 2 amp current and pulse on time of 21 μs is shown
in Figure 14. The peaks of Al, Si, C, and Cu along with the peaks of Mg and Mn are clearly seen from
the figure5
. Therefore, it also helps to confirm the presence of tool materials on the substrate.
Fig.11 Microscopic image of Pellet (SiC50:Cu50) Fig.12 Microscopic image of base (Parent)
metal &
deposited layer

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30 40 50 60 70 80 90
0
10
20
30
40
50
60
70
6 42 6
6
3
1
1
1,4
4 2,3
2,3
3
3
3
5
5 5
Intensity
2 angle
20 Ton SiC50:Cu50
1 - Al
2 - Si
3 - SiC
4 - Cu
5 - Al2Cu
6 - Al2CuMg
Fig.13. XRD plot of a coated layer using SiC50:Cu50, 20 ton compact tool
Fig.14. EDX plot of a coated layer at 10 ton compaction load and SiC50:Cu50 composition
5.1 Scanning Electron Microscopy (SEM) Analysis:
Fig.15 shows the SEM image of the top surface of one of the coated samples20
at 10 ton compaction
load, 2 amp current, 21μs pulse on time and SiC50:Cu50 composition. From this figure, it is clearly
observed that a particle like phase is distributed over the layer45
. The X-ray elemental distribution of
C, Cu and Si is shown in fig. 16 where yellow colour corresponds to C, red colour corresponds to Cu
and violet colour corresponds to Si respectively. Spot EDS spectrum on the particle is also shown in
fig. 17. Hence, the elemental mapping and EDS spectrum suggest that the particle is SiC45
. Therefore,
this can also be the witness (confirmation) of the deposited materials on the top surface of the
substrate.

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Fig.15. SEM micrograph of one of the top surface of a sample
Fig. 16. (a) SEM image of SiC/Cu composite layer and corresponding X-ray elemental map of (b)
Overall, (c) C, (d) Cu and (e) Si.

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Fig. 17 Spot EDS spectrum
5.1.1 Interface Analysis:
Fig. 18 (a to c) shows the interface between the parent material and deposited layer of few
experimental conditions at different magnifications. The workpiece and tool material melts and
solidifies repeatedly by recurring discharges. Due to the repeated melting and solidification, the
eroded elements diffuse each other and forms close bond of the deposited material with the surface
of the work-piece. To a great extent, it is conceded that metallurgical bonding takes place in the
interface1,46
. The cross sectional and top surface view at different magnifications are shown in the
figures 18 (a), 18 (b) & 18 (c). From these figures, the parent material, interface and also the
deposited layer are clearly identified. Moreover, it is also observed that a very thin layer deposition
with less number of craters takes place on the workpiece surface at low current and low pulse
duration (shown in fig. 19.a). And at high current and long pulse duration, a thin layer deposition
with large number of craters takes place on the workpiece surface1
which results in increase in
surface roughness (Ra) (shown in fig. 19.b). This may be due to the fact that increases in current and
pulse duration results in more sparks, more erosion and simultaneously more craters. But, if the
compaction load and also the Cu percentage in the pellets (compact tool) are increased, then the
craters formation on the deposited surface is less. It may be due to the increase in density and also
the bonding strength of the compact tool which results less number of pores and less erosion1
. Thus,
selection of optimum combination of process parameters is required for obtaining uniform
deposition as well as better surface finish.

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Fig. 18 (a) Cross sectional view
Fig. 18 (b) Top surface view with low magnification
Fig. 18 (c) Top surface view with high magnification
Fig. 18 Parent material, interface and deposited layer

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Fig. 19 (a) P: 10, Ip: 2, Ton: 21, Ct: 50:50
Fig.19 (b) P: 5, Ip: 8, Ton: 100, Ct: 50:50
Fig. 19 Craters formation of the top surface of coated samples
5.2 Layer Thickness (LT):
The micro structure of the deposited layer over the workpiece surface is also analysed by the optical
microscopic image at 200X magnification. From the microscopic view, a thin dark black portion
(coated layer) is visible in between the out of focus portion and the work piece surface, starting from
one extreme edge of work piece and propagating to some distance. Here, this dark area represents
SiC & Cu composite layer. Therefore, it is observed from the fig.20 that a uniform thin layer of
silicon carbide is formed on the base metal, which also shows the presence of tool materials on the
substrate1
.

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Fig.20 Optical microscopic image of deposited layer of SiC/Cu over the base metal of a sample
6. CONCLUSIONS
The application of utility concept of Taguchi method is used to find the best possible combination
values of EDC parameters like tool composition, compaction load, current and pulse duration for
maximizing the micro hardness and minimizing the surface roughness during deposition of SiC/Cu
over the surface of Al-6351 alloy. The optimal operational parameter levels of EDC have been
achieved through ANOM and the percent contribution of that operational parameter in optimizing
the various performances was determined by ANOVA. The conclusions are as follows:
i. A wide variety of Ra (2.63-4.62 µm) and Hm (149.02 to 301.05 HV) are obtained in different
parametric combinations of composition, compaction load, current and pulse duration
during EDC process.
ii. A wide range of MDR (0.08-0.81 mg/min) is also obtained in different parametric
combinations of composition, compaction load, current and pulse duration during EDC
process.
iii. ANOM results indicate that a combination of compaction load 20 Ton, current 4 Ampere,
pulse on time 21µs and tool composition of 30:70 by ratio weight of Silicon carbide and
Copper is essential for simultaneously maximizing the micro hardness and minimizing the
surface roughness.
iv. ANOVA result shows that pulse duration emerged as the most important process parameter
influencing the multi-response S/N ratio followed by current, compaction load and tool
composition with their corresponding contribution as 43.91 %, 29.45 %, 22.97 % and 0.92 %
respectively in optimizing the output parameters.
v. Confirmation test is carried out based on the optimal parameters of ANOM and the
difference between the predicted result and experimental test was within the range of
calculated confidence interval of the model.
vi. Optical microscopic images, XRD, SEM and EDX analysis of the coated layer proved the
development of a hard composite layer composed of SiC, Si, C, Cu, Al, Al2Cu, and Al2CuMg on
the substrate.
vii. Micro-Hardness of the coated layer ranged from 149.02 to 301.05 HV whereas the hardness
of the base metal is 95 HV which shows that the hardness value of deposited layer is 1.5 to 3
times more than the parent material.
Therefore, it can be concluded that the utility concept of Taguchi method is an adequate and useful
tool which can be used for instantaneous optimization of contradictory performance characteristics
of EDC process.
ACKNOWLEDGEMENT

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The authors are extremely thankful to the Mechanical Department, NIT Silchar & CRF, NIT Agartala
for providing the lab facilities and especially Dr. Promod Kr. Patowari, Associate Professor,
Mechanical Engg. Deptt., NIT Silchar for his kind support.
NOMENCLATURE
P = Compaction Load; Ton = Pulse on time/Pulse duration; IP = Current; Ct = Tool composition
Hm = Micro-Hardness; Ra = Average Surface Roughness; Ƞ = S/N ratio; LT = Layer thickness
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