M. KOLLI, K. ADEPU: OPTIMIZATION OF THE PARAMETERS FOR THE SURFACTANT-ADDED EDM ...
229–238
OPTIMIZATION OF THE PARAMETERS FOR THE
SURFACTANT-ADDED EDM OF A Ti–6Al–4V ALLOY USING THE
GRA-TAGUCHI METHOD
OPTIMIZACIJA POVR[INSKO AKTIVNIH ME[ANIH EDM
PARAMETROV NA Ti-6Al-4V ZLITINI Z UPORABO
GRA-TAGUCHI METODE
Murahari Kolli, Kumar Adepu
Department of Mechanical Engineering, National Institute of Technology, Warangal, Telangana, India
kmhari.nitw@gmail.com
Prejem rokopisa – received: 2014-09-30; sprejem za objavo – accepted for publication: 2015-03-23
doi:10.17222/mit.2014.249
In this research, the Taguchi technique was applied to determine the optimum process parameters for the electrical discharge
machining (EDM) of a titanium alloy with an added surfactant. The surfactant added to the dielectric fluid played an important
role in the discharge gap, increasing the conductivity and suspending debris particles in the dielectric fluid, reducing abnormal
discharge conditions of the machine and improving the overall machining efficiency. The performance characteristics were the
material-removal rate (MRR) and the surface roughness (SR), which were experimentally explored for various input parameters
such as the discharge current, the pulse-on time, the pulse-off time and the surfactant concentration in the dielectric fluid. The
optimum setting of the parameters was verified through planned and conducted experiments and analysed using the Taguchi
technique. Further, a multi-response optimisation was carried out, maximising the MRR and minimising the SR, using the Grey
relational analysis (GRA). It was observed that the pulse-on time, the discharge current, the pulse-off time and the surfactant
concentration contribute significantly to the multi-response optimisation. Conformation test results showed an improvement in
the MRR by 20.69 % and in the SR by 11.09 %. A scanning-electron-microscope (SEM) analysis was conducted to study the
recast layer that evolved during the electrical discharge machining process and the topography of surfaces was also observed.
Keywords: surfactant, Ti-6Al-4V alloy, Grey-Taguchi method, material-removal rate, surface roughness, recast-layer thickness
V ~lanku je uporabljena Taguchi metoda za dolo~anje optimalnih parametrov procesa povr{insko aktivne elektroerozije (EDM)
na titanovi zlitini. Povr{insko aktivne snovi, dodane dielektri~ni teko~ini, igrajo pomembno vlogo pri razelektritveni re`i, kar
pove~a prevodnost in ustavi delce v dielektri~ni teko~ini, zmanj{a neobi~ajne razelektritve pri obdelavi in na splo{no pove~a
u~inkovitost obdelave. Zna~ilnosti zmogljivosti sta hitrost odstranjevanja materiala (MRR) in hrapavost povr{ine (SR), ki sta bili
eksperimentalno uporabljeni za razli~ne vhodne parametre, kot je: tok razelektritve, trajanje impulza, trajanje prekinitve in kon-
centracija povr{insko aktivne snovi v dielektri~ni teko~ini. Optimalna postavitev parametrov je bila preizku{ena z na~rtovanimi
preizkusi in analizirana s Taguchi metodo. Izvr{ena je bila optimizacija z ve~ odgovori, to je maksimiranje MRR in mini-
miziranje SR s pomo~jo uporabe Grey relacijske analize (GRA). Ugotovljeno je, da tok razelektritve, trajanje pulza, ~as brez
pulza in koncentracija povr{insko aktivne snovi, mo~no prispevajo k optimizaciji. Rezultati potrditvenih preizkusov so pokazali
izbolj{anje MRR za 20,69 % in SR za 11,09 %. Z vrsti~nim elektronskim mikroskopom (SEM) je bila izvr{ena analiza (SEM)
pretaljenega sloja, ki se je razvil med postopkom elektroerozije, opazovana pa je bila tudi topografija povr{ine.
Klju~ne besede: povr{insko aktivne snovi, Ti-6Al-4V zlitina, Grey–Taguchi metoda, hitrost odstranjevanja materiala, hrapavost,
debelina nataljene plasti
1 INTRODUCTION
The usage of and demand for the Ti-6Al-4V alloy is
on an increase day by day because of its high strength,
low weight ratio, high corrosion resistance, resistance to
high temperature and high toughness. This makes it the
appropriate alloy for surgery, medicine, aerospace, the
automotive industry, chemical plants, pressure vessels
and power generation. It is used to manufacture propeller
shafts, riggings and other parts of boats. It is also used to
create artificial hips, pins for setting the bones and other
biological implants due to its excellent biocompatibility.
Its application also includes aircraft-turbine components,
aircraft structural components, aerospace fasteners and
high-performance automotive parts.1,2
Ti-6Al-4V is one of the materials difficult to machine
due to its properties. It chemically reacts with almost all
cutting-tool materials. Its low thermal conductivity and
low modulus of elasticity reduce the machinability.3 Low
cutting speeds, high feed rates, a huge quantity of cutting
fluids, sharp tools and a rigid set-up are essential for the
conventional machining of Ti-6Al-4V. This makes its
conventional machining uneconomical. EDM is one of
the advanced manufacturing processes, with which a
Ti-6Al-4V alloy can be machined economically and
efficiently.4
In modern manufacturing industries, EDM is one of
the most popular non-conventional machining processes,
used to produce dies, moulds or process ceramics, etc.
Tough and hard metals that are generally used in the
industries, such as aerospace, automotive and surgical
applications, can be machined easily using EDM, taking
into account various material properties, such as the elec-
Materiali in tehnologije / Materials and technology 50 (2016) 2, 229–238 229
UDK 669.295:621.9.048:621.7.015:621.7.01 ISSN 1580-2949
Original scientific article/Izvirni znanstveni ~lanek MTAEC9, 50(2)229(2016)
trical and thermal conductivity. EDM is a thermoelectric
machining process, in which the electrode and the work-
piece do not come into direct contact, which eliminates
the mechanical residual stresses and chatter or vibration
problems during the machining.5 In an EDM process, the
dielectric fluid plays a vital role, affecting the material-
removal rate and the surface integrity of the machined
surface. The vital tasks of the working fluid are trans-
porting sediment particles, enhancing the discharge-ener-
gy density of the plasma zone and cooling the electro-
des.6 It has been noticed from a wide literature survey
that most of the researchers focused on reducing the sur-
face roughness and increasing the material-removal rate
using kerosene, water and water-oil emulsions of diffe-
rent materials. Very few researchers studied the effect of
a surfactant added to EDM. A scarce amount of research
work was done on the surfactant added to the EDM oil in
the conventional EDM of the titanium alloy.
A surfactant is added into the dielectric fluid for a
better circulation in the discharge gap and to avoid parti-
cle agglomeration (debris and tar). During the machining
process, surfactant molecules enter the discharge gap,
thereby breaking down the voltage easily, reducing the
insulating strength by increasing the discharge gap and
passage and ensuring an even discharge distribution
during the machining process. As a result, the process
becomes more stable, thereby improving the machining
efficiency, reducing the surface roughness and increasing
the material-removal rate.7–9
A considerable amount of work on EDM of titanium
alloy Ti-6Al-4V was reported by the researchers. Chen et
al.10 compared the influences of kerosene and distilled
water. It was noticed that the material-removal rate was
increased and the relative electrode-wear rate was
decreased when distilled water was used as the dielectric,
when compared to kerosene. Ho et al.11 investigated the
use of a powder-metallurgy (PM) compacted electrode. It
was observed that the PM electrode was more alloyed
than the solid electrode. The thickness of the recast layer
was increased when the PM electrode and positive
polarity were used.
Hascalik and Caydas12 analysed the effects of copper,
graphite and aluminum electrodes. The material-removal
rate was increased in the case of the graphite electrode
and the surface roughness was reduced with the alumi-
num electrode, compared to other electrodes. Caydas and
Hascalik.13 developed a model of the electrode wear and
recast-layer thickness using the response surface metho-
dology. It was observed that the values predicted by the
model reasonably matched the experimental values.
Fonda et al.14 studied the effects of the properties of
Ti-6Al-4V on EDM. It was observed that the optimum
duty factor was 7 % as far as the productivity and quality
of an EDMed surface were concerned.
Abdulkareem et al.15 analysed the effect of cryogenic
cooling on the copper-electrode wear and the surface
roughness of Ti-6Al-4V. It was observed that the
electrode wear reduced remarkably and the surface finish
improved due to cryogenic cooling. Bozdana et al.16
compared the performance of brass-copper tubular
electrodes with copper electrodes. It was observed that
the material-removal rate increased with decreased elec-
trode wear when brass electrode was used, as compared
to the copper electrode. Khan et al.17 developed a single-
order mathematical model for correlating the parameters
and performance characteristics during EDM. It was
observed that as the peak current increased, the surface
roughness increased. The combination of a high-peak
current and long pulse-on time deteriorated the surface
roughness. Rahman18 developed an artificial-intelligence
model to predict the optimum parameters. It was found
that the developed model was within the limits of
tolerable errors with respect to experimental results.
Kolli and Kumar19 investigated the effects of the additi-
ves added to the dielectric fluid on the EDM of a tita-
nium alloy. Their experiments were conducted by
varying the concentration of the additives and the
discharge current to measure their effects on the MRR,
SR and TWR. It was observed that the MRR increased
with an increase in the discharge current and the
surfactant concentration.
Multi-response optimisation has become an increas-
ingly important area in the modern manufacturing
industry to satisfy the customer’s strict requirement for
the overall quality improvement in the process and ma-
chined components, Jeyapaul et al.20 and Kao et al.21
considered the Taguchi method and the grey relational
analysis to optimize the multiple-performance characte-
ristics of the EDM of the Ti–6Al–4V alloy. Kerosene
was taken as the dielectric fluid and electrolytic copper
as the electrode. It was observed that the optimized
parameters increased the MRR by 12 %, EWR by 15 %
and SR by 19 %, when applying the GRA technique.
Tang and Du22 employed the multi-objective optimiza-
tion technique for the grey EDM of the Ti-6Al-4V alloy.
In this study, tap water was used as the dielectric fluid
and tungsten copper as the electrode of a 10 mm dia-
meter. The input process parameters included the dis-
charge current, the open-circuit voltage, the lifting time,
the pulse-on time and the pulse-off time. The machining
performance parameters like the EWR, the MRR and the
SR were estimated. The authors concluded that when the
multi-optimization technique was included in the
Taguchi method it increased the machining performance
parameters, i.e., the MRR by 2 %, the EWR by 59 % and
the SR by 4 %.
Lin et al.23 applied the Grey-Taguchi technique to
optimise the EDM parameters for a titanium alloy with
kerosene as the dielectric fluid. The effects of the pro-
cess parameters such as the peak current, the pulse-on
time, the pulse-off time and the gap voltage on their
performance parameters like the electrode depletion
(ED), the MRR and the overcut were studied. It was
observed from the final results that the discharge current
M. KOLLI, K. ADEPU: OPTIMIZATION OF THE PARAMETERS FOR THE SURFACTANT-ADDED EDM ...
230 Materiali in tehnologije / Materials and technology 50 (2016) 2, 229–238
and the pulse-on time were the most significant para-
meters of the EDM using the Grey-Taguchi method. In
addition, it was mentioned that the Grey-Taguchi method
is highly suitable for the optimization of the ED, the
MRR and the overcut.
In this experimental study, the dielectric fluid with an
added surfactant used for the electrical discharge
machining of a titanium alloy was investigated and the
characteristics were optimized with the Grey-Taguchi
technique. The process parameters such as the discharge
current, the pulse-on time, the pulse-off time and the
surfactant concentration were varied. The optimum
setting of the parameters was verified through the
planned experiments, conducted and analysed with the
Taguchi technique. The material-removal rate (MRR) and
the surface roughness (SR) were considered as the
performance characteristics. Further study was carried
out to observe the influence of the process parameters on
the presence of the recast layer on the machined surfaces
using SEM and also to observe the topography of
surfaces.
2 EXPERIMENTAL SET-UP
The experiments were performed on a Formatics
EDM-50 die-sinking machine mounted on a custom-built
dielectric-cycling system. The electrode was fed down-
wards, under the DC servo control, into the workpiece.
Surfactant-added (Span 20) spark-erosion 450 EDM oil
was used as the dielectric fluid for the machining, used
in die-sinking machines for a high machining speed and
a good surface finish. The surfactant was added in a
certain amount into the dielectric fluid and continuously
stirred in order to maintain a uniform distribution. The
homogenously mixed dielectric fluid was pumped into
the machining region using side flushing. Surfactant che-
mical properties are presented in Table 1. The experi-
ments were conducted with a reverse-polarity electrode.
A electrolytic copper with a diameter of 14 mm and a
length of 70 mm was selected as the electrode. A
workpiece with a length of 100 mm, a width of 50 mm
and a thickness of 5 mm was employed. Each experiment
was conducted for 30 min.
Prior to the machining, the workpiece and the
electrode were cleaned and polished. The workpiece was
firmly clamped in the vice and immersed in the dielectric
fluid. The die-sinking EDM-machine experimental
set-up is shown in Figure 1 and the titanium-alloy che-
mical composition and properties are shown in Tables 2
and 3. The weight of the workpiece and the electrode
tool was measured using a digital weighing balance
M. KOLLI, K. ADEPU: OPTIMIZATION OF THE PARAMETERS FOR THE SURFACTANT-ADDED EDM ...
Materiali in tehnologije / Materials and technology 50 (2016) 2, 229–238 231
Figure 1: Modified experimental set-up
Slika 1: Spremenjen eksperimentalni sestav
Table 2: Chemical composition of Ti-6Al-4V alloy
Tabela 2: Kemijska sestava Ti-6Al-4V zlitine
Ele-
ment C Al V N O Fe H Ti
% Max.0.014 6.07 4.02 0.00360.1497 0.03 0.0115
Bal-
ance
Table 3: Properties of Ti-6Al-4V alloy
Tabela 3: Lastnosti zlitine Ti-6Al-4V
Property Values
Hardness (HRC) 32–34
Melting point (°C) 1649–1660
Density (g/cm3) 4.43
Ultimate tensile strength (MPa) 897–950
Thermal conductivity (W/m K) 6.7–6.9
Specific heat (J/kg K) 560
Mean coefficient of thermal expansion
(W/kg K) 8.6 × 10
–6
Volume electrical resistivity ( cm) 170
Elastic modulus (GPa) 113–114
Table 4: Experimental settings
Tabela 4: Eksperimentalne nastavitve
Working parameters Description
Workpiece material Ti-6Al-4V
Size of work piece 100 × 50 × 5 mm
Electrode material Electrolytic copper
Size of electrode  14 × 70 mm
Electrode polarity + ve (reverse polarity)
Dielectric fluid Spark erosion 450 EDM oil
+ surfactant
Discharge open voltage 110 V
Discharge gap voltage 65 V
Flushing pressure 0.75 MPa
Machining time 30 min
Table 1: Surfactant properties
Tabela 1: Lastnosti povr{insko aktivnih snovi
Property Quantity
Chemical formula C18H34O6
Molecular weight 346.47 (g/mol)
Density 1.032 g/mL at 25 °C (L)
Flash point > 230 °F (110 °C )
Relative index n 20/D1.4740 (L)
HLB value
Water content
8.6
< 1.5 (%)
Acid value 4–8
Heavy metal 9.5–10.0 (L/%)
Saponification value 158–170
(manufactured by CITIZEN) before and after the ma-
chining to calculate the MRR. The surface roughness of
the machined workpieces was measured using Handysurf
equipment. The range of each factor was taken based on
the capability of the machine and the data from the
literature, and preliminary experiments were conducted
to that effect.19,24,25 The process parameters and experi-
mental conditions considered are listed in Tables 4 and
5, respectively.
Table 5: Process parameters
Tabela 5: Parametri procesa
Symbols Control factors Level 1 Level 2 Level 3 Units
A Dischargecurrent (Ip)
10 15 20 A
B Pulse-on time(Ton)
25 45 65 μs
C Pulse-off time(Toff)
24 36 48 μs
D
Surfactant
concentration
(SC)
0.25 0.50 0.75 g/L
2.1 Taguchi method
The Taguchi method is very effective when dealing
with responses influenced by many parameters. It is a
simple, efficient and systematic approach for deter-
mining optimum process parameters. It is a powerful tool
for designing experiments, drastically reducing the num-
ber of experiments required for modelling and opti-
mising the responses. Also, it saves lot of time and
reduces the experimental cost. The Taguchi method is
devised for process optimization and identification of the
optimum levels of the process parameters for given res-
ponses.26–28 In Taguchi method, the experimental values
of various responses are further transformed to the
signal-to-noise (S/N) ratio. The response that is to be
maximized is called the šhigher the better’ response and
the one that is to be minimized is called the šlower the
better’ response. The Taguchi method uses the S/N ratio
to measure the deviation of a response from the mean
value. S/N ratios for šhigher the better’ and šlower the
better’ characteristics are calculated using Equations (1)
and (2), respectively:
 = −
⎡
⎣⎢
⎤
⎦⎥=
∑10 1 110 2
1
lg
n y ii
n
(1)
 = −
⎡
⎣⎢
⎤
⎦⎥=
∑10 110 2
1
lg
n
y i
i
n
(2)
where  denotes the S/N ratio of experimental values, yi
represents the experimental value of the ith experiment
and n is the total number of experiments. In the present
study, the Taguchi method was applied to experimental
data using the MINITAB 16 software. The number of
process parameters considered under this study is four
and the level of each factor is three. The degree of free-
dom of all four factors is eight. Hence, the L9 (34) ortho-
gonal array is selected. Each condition of the expe-
riment was repeated three times in order to reduce the
noise/error effects. The quality characteristics of the
MRR and the SR of the titanium alloy, evaluated for all
the experimental results are listed in Table 6. The
optimum element combinations were verified using the
statistical analysis of variance (ANOVA).
2.2 Grey relational analysis (GRA)
The Grey relational analysis (GRA) was initially
introduced by Dr. Deng in 1982. GRA has uncertain
relations between one main factor and multi-input factors
in a given system. It is an impact-measurement method
of the grey-system theory.29 The Taguchi method, along
with GRA, is applied to optimize the EDM process
parameters with multi-performance characteristics,
which include the following steps:
1. Identifying the performance characteristics and EDM
process parameters to be evaluated.
2. Finding out the number of levels for further EDM
control parameters.
3. Selecting the suitable OA layout (orthogonal array).
4. Conducting OA-layout experiments.
5. Normalizing the experimental results, i.e., the
measured features of the performance characteristics
ranging from 0 to 1, usually known as the Grey
relational generation.
6. Calculating the Grey relational co-efficient () based
on the experimental results.
7. Obtaining the grey relational grade for selected per-
formance parameters by averaging the corresponding
grey relational coefficients.
8. Analysing the experimental results by using the Grey
relational grade and statistical ANOVA.
9. Selecting the optimum level of EDM process para-
meters for maximizing the overall Grey relational
grade.
10. Verifying the optimum EDM process parameters
through the conformation test.
During the Grey relational generation, the mate-
rial-removal rate (MRR) corresponding to the higher the
better (HB) criterion can be expressed as:
x k
y k y k
y k y ki
i i
i i
( )
( ) ( )
( ) ( )
=
−
−
min
max min
(3)
The surface roughness (SR) should follow the smaller
the better (LB) criterion, which can be expressed as:
x k
y k y k
y k y ki
i i
i i
( )
( ) ( )
( ) ( )
=
−
−
max
max min
(4)
However, if a definite target value is to be achieved,
the original sequence is normalised in the following
form:
{ }x k
y k OB
y k OB OB y k
i
i
i i
( )
( )
max ( ) , ( )
= −
−
− −
1
max min
(5)
M. KOLLI, K. ADEPU: OPTIMIZATION OF THE PARAMETERS FOR THE SURFACTANT-ADDED EDM ...
232 Materiali in tehnologije / Materials and technology 50 (2016) 2, 229–238
where OB is the target value.
Alternatively, the original sequence can be simply
normalised using the most basic methodology, i.e., the
values of the original sequence are divided by the first
value of the sequence:
x k
y k
yi
i
i
( )
( )
( )
=
1
(6)
where xi(k) is the value after the grey relational gene-
ration (data processing), yi(k) is the original sequence,
max yi(k) is the largest value of yi(k) for the kth response,
and min yi(k) is the smallest value of yi(k), being the
desired value.29,30 The ideal sequence of responses is
yi(k) (k = 1, 2, 3…., m).
Further, during the data pre-processing, the Grey
relational coefficient is calculated to express the relation-
ship between the ideal and actual normalised experi-
mental results. The Grey relational coefficient can be
expressed as follows:


i i
k
k
( )
( )
min max
max
=
+
+
Δ Δ
Δ Δ0
(7)
where 0i(k) is the deviation sequence of the reference
sequence xi(k) and the comparability sequence yi(k).
0i = x k x ki0 ( ) ( )− = difference of the absolute value
between x0(k)and xi(k);
 = distinguishing coefficient (0 ~ 1)
Δ min
min min ( ) ( )= ∀ ∈ ∀ −j i k x k x ki0
Δ max
max max ( ) ( )= ∀ ∈ ∀ −j i k x k x ki0
After obtaining the Grey relational coefficient, its
average is calculated to obtain the grey relational grade.
The Grey relational grade () is defined as follows:31
 i i
k
n
n
k=
=
∑1
1
( ) (8)
The GRA also indicates the degree of influence that
the comparability sequence can exert over the reference
sequence. Therefore, if a particular comparability
sequence is more important than the other comparability
sequence for the reference sequence, then the Grey rela-
tional grade for that comparability sequence and the refe-
rence sequence is higher than the other Grey relational
grade.31,32 With this technique, both the comparability
sequence and the reference sequence are of equal
preference.
3 RESULTS AND DISCUSSION
3.1 Effects of the parameters on the MRR
Table 6 shows the values of MRRs and their S/N
ratios. Table 7 has the corresponding ANOVA results
where the contributions of individual parameters are
calculated. It can be observed that the discharge current
with a contribution of 90.51 %, the pulse-on time with a
contribution of 7.95 % and the surfactant with a con-
tribution of 1.01 % significantly affect the MRR at the 95
% confidence interval, but the pulse-off time makes an
insignificant contribution to the MRR.
Table 6: Experimental layout L9 (34) OA and results for S/N ratios for
MRRs and SRs
Tabela 6: Eksperimentalna postavitev L9 (34) OA in rezultati razmerja
S/N, MRR in SR
Ex. no (A) (B) (C) (D) MRR S/Nratio SR S/N ratio
1 10 25 24 0.25 1.108 0.893 2.51 –7.958
2 10 45 36 0.50 1.260 2.010 2.75 –8.786
3 10 65 48 0.75 1.461 3.292 3.19 –10.076
4 15 25 36 0.75 1.755 4.887 2.65 –8.464
5 15 45 48 0.25 1.917 5.652 3.57 –11.053
6 15 65 24 0.50 2.216 6.911 3.66 –13.271
7 20 25 48 0.50 2.321 7.315 3.23 –10.184
8 20 45 24 0.75 2.514 8.008 4.22 –12.465
9 20 65 36 0.25 2.530 8.062 4.48 –13.029
Table 7: ANOVA of means for MRRs
Tabela 7: ANOVA sredstva za MRR
Parameters
Degree of
freedom
(DF)
Sum of
squares (SS)
Mean sum
of squares
(MSS)
% contri-
bution
A 2 50.835 25.417 90.51
B 2 4.466 2.233 7.95
C 2 0.292 0.146 0.53
D 2 0.571 0.285 1.01
Total 8 56.164 100.00
The main response plot of the S/N ratio in Figure 2
shows that the MRR increases with an increase in the
discharge current. It indicates that the discharge current
is the leading impact factor for the MRR. The discharge
current mainly influences the discharge density available
in the discharge gap. To improve the discharge current,
spark energy is increased, which results in a higher
discharge density. This eventually heats the workpiece,
thus increasing the MRR at increased discharge-current
conditions.21 The pulse-on time controls the pulse
duration of the time, for which the current is supplied to
the flow per cycle. The pulse-on time increases with an
increase in the MRR. Due to higher discharge energy on
the workpiece, more material is melted and evaporated in
the machined zone. During the pulse-off time, no
material is removed from the workpiece as there is no
current supply to the workpiece and the existing material
on the machined surface of the workpiece is removed.33
Another observation from the present experiment is
that an increase in the surfactant concentration increases
the MRR. The conductivity of the dielectric fluid is
increased by adding non-polar surfactant, which results
in a shorter bridging time and a higher electrical-dis-
charge efficiency, which finally results in an increase in
the MRR. An addition of the surfactant to the EDM oil
retards the agglomeration of debris, carbon dregs due to
electrostatic forces or Van der Waals forces during ma-
M. KOLLI, K. ADEPU: OPTIMIZATION OF THE PARAMETERS FOR THE SURFACTANT-ADDED EDM ...
Materiali in tehnologije / Materials and technology 50 (2016) 2, 229–238 233
chining. After that the dielectric-fluid behaviour in the
inter-electrode gap is changed, minimizing the bridge
effect that leads to a better distribution of discharge
energy, resulting in an overall increase in the MRR.34,35
3.2 Effects of the parameters on the SR
The average values of the SR for each experiment and
their respective S/N ratio values are presented in Table 6.
Figure 3 shows SR response curves, representing
individual effects of discharge current, pulse-on time,
pulse-off time and surfactant concentration. The ANOVA
results tabulated in Table 8 show the contributions of the
discharge current (52.89 %), the pulse-on time (39.61 %)
and the concentration of surfactant (6.07 %), reducing
the SR considerably. It is found that the discharge current
has a leading impact on the SR. It is observed from
Figure 3 that an increase in the SR takes place with an
increase in the discharge current. The explanation for
this can be that an increase in the discharge current
causes a corresponding increase in the spark energy,
which results in the formation of deeper and larger
craters and which, in turn, results in an increased surface
roughness. The SR increases with the increasing pulse-on
time due to the fact that at a constant supply of current,
the increase in the pulse-on time is proportional to the
increase in the spark energy; subsequently, the melting
boundary becomes wider and deeper and hence there is
an increase in the SR value.36
Table 8: ANOVA of means for SRs
Tabela 8: ANOVA sredstva za SR
Parameters
Degree of
freedom
(DF)
Sum of
squares (SS)
Mean sum
of squares
(MSS)
%
contribution
A 2 13.138 6.569 52.89
B 2 10.707 5.353 39.61
C 2 0.389 0.194 1.53
D 2 1.500 0.750 6.07
Total 8 23.878 100.00
The pulse-off time is the least important factor and
shows the smallest contribution as far as the SR is con-
cerned. This is due to the fact that no material is removed
from the workpiece as there is no supply of the discharge
current. It is observed that the pulse-off time increases
with an increase in the SR. An increase in the surfactant
concentration increases the surface roughness. This may
be due to the fact that an increase in the surfactant con-
centration increases the dielectric conductivity, causing
the spark energy to be more concentrated, which results
in increased surface roughness. On the other hand, an
increase in the surfactant concentration causes an
increase in the dielectric conductivity and, at the same
time, the dielectric fluid is mixed with the debris and tar
particles accumulated in the machining gap.35,37,38
3.3 Multi-response optimisation of the parameter com-
bination for the MRR and the SR based on the
GRA
Normalisation of the experimental data using Equa-
tions (3) and (4) is performed and considered in the
range between 0–1. The normalised data and the devi-
ation sequence for each of the responses are listed in
Table 9. A higher value of the response indicates a better
performance and the higher normalised values that are
equal to 1 depict the best performance. Hence, all the
experimental values presented in Table 6 are substituted
in Equations (3) and (4) to get the normalised values
shown in Table 9. Typically, larger values of the MRR
and smaller values of the SR are desirable for any
machining operation. Thus, in the present work, the
selected criterion for the MRR is the larger the better and
for the SR it is the smaller the better.
These normalised-sequence values (data-processing
values) are then substituted in Equation (5) and the Grey
M. KOLLI, K. ADEPU: OPTIMIZATION OF THE PARAMETERS FOR THE SURFACTANT-ADDED EDM ...
234 Materiali in tehnologije / Materials and technology 50 (2016) 2, 229–238
Figure 2: Main-effect plot for MRR
Slika 2: Diagram glavnega u~inka MRR
Figure 3: Main-effect plot for SR
Slika 3: Diagram glavnega u~inka SR
relational coefficient with the weights of  MRR = 0.5
and  SR = 0.5 is calculation using Equation (6). The
grey relational grade is calculated using Equation (7) and
the responses are presented in Table 10. Thus, the
multi-criteria optimisation problem was transformed into
a single equivalent objective function using the Grey-
Taguchi relational analysis. The higher the value of the
Grey relational grade, the closer to the optimum value is
the corresponding factor in the combination. Table 11
suggests that the highest value of the Grey relational
grade was achieved for experiment no. 4. This result
indicates that the best combination of the parameters for
multiple responses among the nine experiments is
A2B1C2D2.
Table 10: Deviation sequences (Grey relational generating)
Tabela 10: Odkloni sekvenc (Grey relacijsko generiranje)
Number
Deviation sequences
MRR (i ) 1 SR (i ) 2
Ideal sequence 1 1
1 1 0.0000
2 0.8931 0.1218
3 0.7518 0.3452
4 0.5450 0.0711
5 0.4311 0.5381
6 0.2208 0.5838
7 0.1470 0.3655
8 0.0113 0.8680
9 0.0000 1.0000
A Grey-relational-response graph is shown in Figure
4 and its ANOVA analysis is shown in Table 12. The
most significant factors contributing to the multiple res-
ponses are the discharge current (33.79 %), the pulse-on
time (34.59 %), the pulse-off time (22.28 %) and the
surfactant concentration (9.34 %). The results of Figure
4 indicate that the optimum parameter combination for
these multiple responses is obtained for A3B1C2D1.
3.4 Confirmation test
Conformation experiments were conducted for the
optimum parameter combinations to verify the improve-
ment in the responses. The results of the conformation
experiments are compared with the outcome of the initial
data and the predicated parameter combination based on
the GRA (M. Kolli, A. Kumar19). Table 13 shows the
results of the conformation experiments using the opti-
mum surfactant-added EDM-process parameters ob-
M. KOLLI, K. ADEPU: OPTIMIZATION OF THE PARAMETERS FOR THE SURFACTANT-ADDED EDM ...
Materiali in tehnologije / Materials and technology 50 (2016) 2, 229–238 235
Table 11: Grey relational coefficient and grade for each performance
Tabela 11: Grey relacijski koeficient in dose`ena stopnja za vsako
izvajanje
Number
Grey relational
coefficient Grey relational grade
MRR SR Averagevalue Rank
1 0.3333 1.0000 0.6667 4
2 0.3589 0.8041 0.5815 6
3 0.3994 0.5916 0.4955 9
4 0.4785 0.8756 0.6770 1
5 0.5370 0.4817 0.5093 8
6 0.6937 0.4614 0.5775 7
7 0.7728 0.5777 0.6753 2
8 0.9780 0.3655 0.6717 3
9 1.0000 0.3333 0.6667 5
Table 12: ANOVA of means for Grey relational analysis
Tabela 12: ANOVA sredstva za Grey relacijsko analizo
Parameters
Degree of
freedom
(DF)
Sum of
squares (SS)
Mean sum
of squares
(MSS)
% con-
tribution
A 2 3.267 1.633 33.79
B 2 3.343 1.671 34.59
C 2 2.154 1.077 22.28
D 2 0.903 0.451 9.34
Total 8 9.667 100.00
Figure 4: Process-parameter effect on Grey relational grade
Slika 4: U~inek parametrov procesa na Grey relacijski razred
Table 9: Sequence of each performance characteristic after data
pre-processing
Tabela 9: Zaporedje vsake lastnosti po predobdelavi podatkov
Number
Deviation sequences
MRR (larger the
better)
SR (smaller the
better)
Ideal sequence 1 1
1 0 1.0000
2 0.1069 0.8782
3 0.2482 0.6548
4 0.4550 0.9289
5 0.5689 0.4619
6 0.7792 0.4162
7 0.8530 0.6345
8 0.9887 0.1320
9 1.0000 0.0000
tained using the GRA-Taguchi method, i.e., the initial
data with combination A2B1C2D3 and the optimum com-
bination A3B1C2D1. It is observed that the MRR increased
from 1.755 to 2.213 mm3/min. However, the SR was
obtained in a considerable range. The corresponding
percentage improvements in the MRR and the SR are
20.69 % and 11.07 %, respectively.
Table 13: Results of the conformation experiment
Tabela 13: Rezultati preizkusa skladnosti
Observed values Orthogonalarray
Optimum combination
level of machining
parameters
Prediction Experiment
Levels A2B1C2D3 A3B1C2D1 A3B1C2D1
MRR 1.755 – 2.213
SR 2.65 – 2.98
Grey relational grade 67.70 69.18 70.45
3.5 Recast-layer thickness
The EDM process is very complex due to rapid local
heating causing an increase in the local temperatures,
exceeding the melting point of the material, where
melting/vaporization is followed by rapid cooling and
also by random attacks of the sparks. This results in
surface damage in the form of cracks and generation of
high thermal stresses exceeding the fracture strength of
the material. It is also observed from Figure 5 that the
RLT decreases significantly with the surfactant added to
the dielectric fluid. The addition of the surfactant to the
dielectric improves its conductivity and lowers its
viscosity, resulting in a smoother flow of the dielectric in
the inter-electrode gap. As a result, the increase in the
dreg-removal rate leads to improved flushing conditions.
At the same time, an increase in the surfactant concen-
tration in the dielectric results in a uniform distribution
of discharge energy, which lowers the amount of the heat
energy penetrating into the work surface and reducing
the thickness of the recast layer.
3.6 Surface micrographs
Figure 6 shows SEM micrographs of the machined
surfaces with and without the surfactant in the dielectric
fluid. It is observed that the EDM process produces
complex surfaces covered with globules of debris, large
and small melted drops, pocket marks and cracks of
various sizes. During the EDM process, some particles
were eroded and attached to the material surface and the
molten material was expelled randomly from the
machining gap. As it can be seen from the figure, the
surface structure is uneven. The size of the molten drops
depends on the surfactant concentration, which is
associated with a low SR of the machined sample as
shown in Figure 6b.
As discharge current is applied to the machining
zone, equal-intensity discharge energy strikes the metal
surface and a large quantity of the molten and flushed
metal is suspended in the electrical-discharge gap, which
results in a deterioration of the SR. Other reasons for a
better surface finish of the machined surface are lower
and smaller craters, cracks produced during the machin-
ing and generation of fewer micro-cracks because of the
intense impulsive forces and stresses due to the
equal-discharge energy.36
4 CONCLUSION
This paper presents an effective approach for the
optimisation of the surfactant-added EDM of a titanium
alloy. The process parameters were the discharge current,
the pulse-on time, the pulse-off time and the surfactant
concentration; the multi-optimization Grey-Taguchi
approach was used. Based on the experimental results of
the present study, the following conclusions are drawn:
• The MRR at the optimum condition (i.e., A3B3C3D3)
increases with the increase in the discharge current
(20 A), the pulse-on time (65 μs) and the surfactant
concentration (0.75 g/L). As the pulse-off time
increases from 24 to 36 μs, the MRR decreases, while
beyond 36 μs the MRR increases.
M. KOLLI, K. ADEPU: OPTIMIZATION OF THE PARAMETERS FOR THE SURFACTANT-ADDED EDM ...
236 Materiali in tehnologije / Materials and technology 50 (2016) 2, 229–238
Figure 6: SEM micrographs of the machined samples: a) without
surfactant and b) with the optimum GRA surfactant
Slika 6: SEM-posnetka obdelanih vzorcev: a) brez povr{insko aktivne
snovi in b) z optimalno povr{insko aktivno snovjo GRA
Figure 5: SEM micrographs of cross-sectional view of EDMed recast
layer with surfactant and without surfactant: a) without EDM oil, b)
with GRA surfactant at optimum conditions
Slika 5: SEM-posnetki pre~nega prereza z EDM preoblikovano
plastjo s povr{insko aktivno snovjo in brez povr{insko aktivne snovi:
a) brez EDM olja, b) s povr{insko aktivno snovjo GRA pri optimalnih
pogojih
• The optimum condition for the SR was observed at
A1B1C2D1 having lower values of the discharge
current (10 A), the pulse-on time (25 μs) and the
surfactant concentration (0.25 g/L). It was observed
that the SR is directly proportional to the discharge
current, the pulse-on time and the surfactant
concentration.
• The optimum combination of the surfactant-added
EDM parameters and their levels for the multi-perfor-
mance characteristics of the surfactant-added EDM
process are A3B1C2D1, the discharge current of 20 A,
the pulse-on time of 25 μs, the pulse-off time of 36 μs
and the surfactant concentration of 0.25 g/L.
• All the process parameters significantly affect the
multi-performance characteristics of the surfactant-
added EDM process. However, the machining
performance of the MRR significantly increases from
1.755 mm3/min to 2.213 mm3/min. The percentage
contributions of various process parameters like the
discharge current, the pulse-on time, the pulse-off
time and the surfactant concentration are (33.79,
34.58, 22.28 and 9.34) %, respectively.
• Fewer micro-cracks and even craters on the work-
piece surface machined by EDM are formed when a
surfactant is added to the dielectric fluid.
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