R. KRISHNAN et al.: OPTIMIZATION OF THE MACHINING PARAMETERS IN THE ELECTROCHEMICAL ...
253–258
OPTIMIZATION OF THE MACHINING PARAMETERS IN THE
ELECTROCHEMICAL MICRO-MACHINING OF NICKEL
OPTIMIZACIJA PARAMETROV ELEKTROKEMIJSKE
MIKROMEHANSKE OBDELAVE NIKLJA
Rajendiran Krishnan
1
, Saravanan Duraisamy
2
, Parthiban Palanisamy
3
,
Anandakrishnan Veeramani
3
1
Jayaram College of Engineering and Technology, Department of Mechanical Engineering, Thuraiyur, Tiruchirappalli – 621014, India
2
Nelliandavar Institute of Technology, Department of Mechanical Engineering, Nerunjikori Village, Ariyalur District – 621704, India
3
National Institute of Technology, Tiruchirappalli- 620015, Tamil Nadu, India
rajrajendiran72@gmail.com
Prejem rokopisa – received: 2017-04-26; sprejem za objavo – accepted for publication: 2017-11-03
doi:10.17222/mit.2017.045
Micro-machining is one of the basic technologies for the production of miniature parts and miso components. Electrochemical
micro-machining (ECMM) is an emerging non-conventional technology for making micro-scale components. The main
objective of this paper is to maximize the metal removal rate (MRR).The process parameters were considered such as electrolyte
concentration, machining voltage, machining current, duty cycle and frequency. In this research, specimens for experimentation
were 0.15-mm-thick nickel sheet, which is mainly in welded battery and gas turbine components. Taguchi’s L18 orthogonal array
was used for the experimentation. Analysis of variance (ANOVA) was used to estimate the contribution by each process para-
meters on MRR. Genetic algorithms (GAs) was used to optimize the process parameters. Based on the experimentations, it was
observed that the machining current and frequency have contributed to a high MRR compared to other parameters. The MRR
obtained using optimized process parameters was very closer to the value obtained from the validation experiment (2.9%). The
optimum process parameters were obtained using the Taguchi method and a genetic algorithm to get the maximum MRR.
Keywords: electrochemical micro-machining, genetic algorithms, metal removal rate, Taguchi orthogonal array
Pri~akuje se, da bo mikromehanska obdelava ena od osnovnih tehnologij izdelave miniaturnih izdelkov in mezokomponent.
Elektrokemijska mikromehanska obdelava (ECMM, angl.: Electro Chemical Micro Machining) je prihajajo~a nekonvencionalna
tehnologija za izdelavo komponent mikronske velikosti. Osnovni cilj avtorjev tega ~lanka je prikazati, kako dose~i maksimalno
hitrost odvzema kovine (MRR; angl.: Metal Removal Rate). V raziskavi so analizirali vpliv procesnih parametrov, kot so kon-
centracija elektrolita, napetost in tok mehanske obdelave, storilnost in frekvenca. V raziskavi so za testne vzorce uporabili
nikljevo plo~evino debeline 0,15 mm, ki se v glavnem uporablja v varjenih baterijah in komponentah plinskih turbin. Kot
osnovo so za eksperimentiranje uporabili Taguchijevo L18 ortogonalno matriko. Za oceno prispevka vsakega od analiziranih
procesnih parametrov na MRR so uporabili analizo variance (ANOVA) in za optimizacijo procesnih parametrov so uporabili
genetske algoritme (GAs). Na osnovi preizkusov so ugotovili, da sta v primerjavi z drugimi parametri, tok in frekvenca ECMM
najve~ prispevala k visoki MRR. Dose`ena MRR z uporabo optimiziranih procesnih parametrov je bila zelo blizu tisti, ki so jo
dosegli z ocenitvenim prakti~nim preizkusom (2,9 %). Optimalne procesne parametre za doseganje maksimalne MRR so torej
dobili s pomo~jo Taguchijeve metode in genetskega algoritma.
Klju~ne besede: elektrokemijska mikromehanska obdelava, genetski algoritmi, hitrost odvzema kovine, Taguchijeva ortogo-
nalna matrika
1 INTRODUCTION
Micro-machining is one of the important manufactur-
ing processes for producing a large number of micro/miso
components. Micro-machining refers to a small amount
of material removal that ranges from 1 μm to 999 μm,
B. Bhattacharyya et al.
1
Fabrication of parts made of
hard and difficult-to-cut materials, finds applications in
aerospace, biomedical engineering, electronics, etc. In
ECMM process, the work piece is connected to an anode
and the micro tool is connected to a cathode and they are
placed inside the electrolyte with a small gap between
them. On the application of adequate electrical energy,
positive metal ions leave from the work piece and ma-
chining takes place. Electrolyte circulation removes the
machined particles from the electrode gap. To continue
the machining process, the electrode gap has to be main-
tained by moving the tool at the required rate. The
ECMM process is capable of machining electrically
conductive, tough and hard materials without inducing
any residual stress, producing no tool wear, B. Bhatta-
charyya et al.
2
The process does not induce any
deformation in the work piece because no force is acting
on the work piece and there is no heat generation in-
volved while machining, K. P. Rajurkar et al.
3
Monitor-
ing method using radio frequency emission is given,
which together with the current and voltage signals,
identifies more clearly the events occurring within the
gap, A. K. M. De Silva.
4
The ECMM system setup was
developed for achieving satisfactory control of ECMM
process parameters, B. Bhattacharyya et al.
5
The
influence of key factors on the quality of hole produced
by ECMM process, M. Sen et al.
6
The effect of machin-
ing voltage, pulse duration and pulse frequency on
machining performance, S. H. Ahna et al.
7
The influence
Materiali in tehnologije / Materials and technology 52 (2018) 3, 253–258 253
UDK 620.1:669.243.88:621.9.048 ISSN 1580-2949
Original scientific article/Izvirni znanstveni ~lanek MTAEC9, 52(3)253(2018)

of the ECMM parameters machining voltage, electrolyte
concentration, pulse period and frequency on MRR,
accuracy and surface finish J. Munda et al.
8
Investigated
the effective range of the process parameters for
moderate MRR with lesser overcut, which is difference
between the hole and tool sizes, J. Munda et al.
9
Deve-
loped a ECMM experimental setup with constant
electrode gap control system and studied the influence of
tool tip shape and machining gap on MRR, R. Thani-
gaivelan et al.
10
Electrochemical machining has no tool
wear on machining hard materials and it does not leave a
defective layer on the machined surface, R. J. Leese et
al.
11
In ECMM the main disadvantage is poor accuracy,
due to the dissolution that occur in the gap, generated by
gas bubbles that increase the current density at the side
gap. To prevent this drawback, it has been demonstrated
that using ultra-short current pulses (100 ns and less) at
high frequency (around the MHz), to improve the
machining accuracy, N. Giandomenico et al.
12
The
Taguchi method is used to find the optimal cutting
parameters for surface roughness. L
9
orthogonal array,
signal-to-noise (S/N) ratio and analysis of variance
(ANOVA) is applied to synthesize the effect of Input
parameters on the nickel-based alloy Inconel-718, O. G.
Sonar et al.
13
The process parameters are optimized
through the Non Dominated Sorting Genetic Algo-
rithm-II (NSGA-II) approach to maximize the metal
removal rate and minimize surface roughness, Chinna-
muthu Senthilkumar et al.
14
From the literature study, it
is observed that only a very few authors have investiga-
ted the performance of ECMM process. Further investi-
gation is required for machining performance improve-
ment for many newly developed difficult-to-cut
materials. The objective of the present paper is to maxi-
mize the MRR by using ECMM process on selecting
process parameters: electrolyte concentration, machining
voltage, machining current, duty cycle and frequency.
The Taguchi L
18
orthogonal array is used for experimen-
tation and to identify the most significant process
parameter. Then analysis of variance (ANOVA) is used
to verify statistical significance of these parameters. The
process parameters were optimized by using genetic
algorithm (GA) to maximize MRR.
2 EXPERIMENTAL PART
The ECMM set-up shown in Figure 1 consists of
various sub components: work holding platform, tool
feeding device, control system, electrolyte flow system
and power supply system.
The work-holding unit has two rectangular platforms
to hold the work piece. The platforms were made up of
acrylic because of its non-corrosive property. The work
piece was placed between the two detachable plates
which were fastened together by means of screws.
The machining chamber was filled with electrolyte
and was clamped to the base. The platforms were
immersed inside the electrolyte tank during machining.
A tool feeding device was used to feed the tool towards
the work piece at the required rate during the machining.
The tool feeding device was actuated by a stepper motor,
which moves the tool up or down based on the signals
received from the control unit. The control system
maintains the electrode gap at a desirable value either to
avoid short circuiting or to avoid reduction in MRR.An
ammeter was used to verify the electrode gap set
between the tool and the work piece before machining.
The tool can be moved according to the amount of inter-
electrode gap required between the tool and the work
piece. The rate of pulses given to the control unit main-
tains the tool feed.
15
For every pulse supplied to the
stepper motor, the tool is moved for four microns.
A pumping system directs the electrolyte to the elec-
trode gap with a medium velocity and drives out the
material removed from the work piece. The electrolyte
passes through two nozzles with the desired pressure into
the machining chamber. The material removed from the
work piece was dissolved in the electrolyte. The electro-
lyte was filtered before re-circulated into the machining
chamber using a filter. During the machining operation
hydrogen gas is evolved at the tool end. The gas bubbles
formed act as a short circuiting medium creating micro
sparks, which can erode the tool material. Hence, to
avoid the micro spark generation, the electrolyte is
pumped out at a moderate pressure, which removes the
hydrogen gas generated. HCl or mixture of brine and
H
2
SO
4
is used as an electrolyte for machining of nickel.
Non continuous pulsed DC supply was used in ECMM.
The pulse current used in ECMM process improve
electrolyte conductivity by flushing electrolyte in pulse
off-time.
15
In ECMM the application of a voltage pulse at
high current density in the anodic dissolution process
improves the machining accuracy and surface finish.
16
It
is observed that the maximum improvement in MRR with
voltage and concentration were 100 % and 70 % res-
pectively.
17
In this study, Taguchi technique was used for the de-
sign and analysis of the experiments. The process para-
R. KRISHNAN et al.: OPTIMIZATION OF THE MACHINING PARAMETERS IN THE ELECTROCHEMICAL ...
254 Materiali in tehnologije / Materials and technology 52 (2018) 3, 253–258
Figure 1: ECMM machining set-up

meters such as electrolyte concentration, voltage,
current, duty cycle, and frequency were selected as fac-
tors. The levels of the parameters were determined based
on tool material and work material used, and size of the
hole to be machined. Based on the number of levels
used, the total degrees of freedom were calculated and a
suitable orthogonal array L
18
was chosen. The parameters
and their levels were given in Table 1. The experiments
were carried out as per the orthogonal array. The
signal-to-noise ratio (S/N ratio) was to be computed to
get the optimized parameter levels. The S/N ratio for
larger the better was used in this study to increase the
MRR. ANOVA is conducted to identify the contribution
of the individual parameter on MRR. Then a confirma-
tion experiment was to be conducted using the optimal
parameter levels combination.
Table 1: Factors and levels
Factor (A) (B) (C) (D) (E)
Level 1 0.1 3.5 0.1 33.33 30
Level 2 0.2 5 0.3 50 40
Level 3 0.3 6.5 0.5 66.66 50
A. Electrolyte concentration (mol/lit.); B. Machining
voltage (volts); C. Machining current, (amp); D. Duty
cycle (%); E. Frequency (Hz).
2.1 Optimization using genetic algorithm
Genetic algorithms (GAs) were a nontraditional opti-
mization algorithm based on the principles of natural
genetics. The Genetic Algorithm was used to maximize
the metal removal rate. The GA has been used for mini-
mization problem by appropriately modifying the objec-
tive function.
18
GAs evaluates the objective function using the basic
elements of GAs consist of a chromosome and fitness
value. The performance of GAs is mainly influenced by
these three operator such as reproduction, crossover and
mutation. The new population also goes through the
same set of GAs operations: fitness evaluation, selection,
crossover and mutation and to continue to develop best
solutions which may maximize the objective function.
This evolution continues until the convergence has
criteria has reached.
21
In this study, the objective is to maximize the MRR
by optimally selecting the process parameters. In ECMM
process, the MRR is controlled by the value of electrolyte
concentration, current, voltage, duty cycle and frequency.
The objective function, MRR is given as,
MRR=f(EC, C, V, DC, F) (1)
The useful ranges of the ECMM process parameters
are considered as the constraints for the above optimi-
zation problem. The constraints are stated as follows:
Bounds on electrolyte concentration (EC)
EC
L
 EC  EC
H
(2)
where, EC
L
and EC
H
and are the least and highest
values of electrolyte concentrations used, respectively,
in the experiments.
Bounds on the machining current (C)
C
L
 C  C
H
(3)
where, C
L
and C
H
are the least and highest values of ma-
chining current used respectively in the experiments.
Bounds on the machining voltage (V):
V
L
 V  V
H
(4)
where, V
L
and V
H
are the smallest and highest values of
machining voltage used, respectively, in the experi-
ments.
Bounds on duty cycle (DC)
DC
L
 DC DC
H
(5)
Where, DC
L
and DC
H
are the smallest and highest
values of duty cycle used, respectively, in the experi-
ments
Bounds on frequency (F)
F
L
 F  F
H
(6)
where, F
L
and F
H
are the smallest and highest values of
frequency used, respectively, in the experiments.
R. KRISHNAN et al.: OPTIMIZATION OF THE MACHINING PARAMETERS IN THE ELECTROCHEMICAL ...
Materiali in tehnologije / Materials and technology 52 (2018) 3, 253–258 255
Figure 2: Modified genetic algorithm

3 RESULTS AND DISCUSSION
3.1 Experimentation
In this study, the specimen was made of 50 mm ×
25 mm × 0.15 mm Ni. The tool electrode was made of
brass with a diameter of 250 μm. The sidewalls of the
tool electrode were coated with bonding liquid. The
electrolyte used for the experiments was fresh HCL
solution. Variable rectangular DC pulsed supply has been
used for the experimentations. The experimental combi-
nations of machining parameters were given in Table 2.
During the experiments, pulse rectifier was switched on
and the desired values of voltage, current, duty cycle and
frequency are set before machining. The parameter levels
set as per Taguchi L
18
orthogonal array.
19
The interac-
tions between the machining parameters were neglected
in this study. The experiment with each combination of
parameter level was repeated for three times to eliminate
the random variations in the experimental results.
Table 2: Input parameters of orthogonal array and MRR
Exp. No
Inputs Output
(A) (B) (C) (D) (E) (F)
1 0.1 3.5 0.1 33.33 30 0.001632
2 0.1 5 0.3 50 40 0.002123
3 0.1 6.5 0.5 66.66 50 0.006307
4 0.2 3.5 0.1 50 40 0.001839
5 0.2 5 0.3 66.66 50 0.004033
6 0.2 6.5 0.5 33.33 30 0.006703
7 0.3 3.5 0.3 33.33 50 0.002874
8 0.3 5 0.5 50 30 0.009577
9 0.3 6.5 0.1 66.66 40 0.003226
10 0.1 3.5 0.5 66.66 40 0.003724
11 0.1 5 0.1 33.33 50 0.001369
12 0.1 6.5 0.3 50 30 0.004675
13 0.2 3.5 0.3 66.66 30 0.004807
14 0.2 5 0.5 33.33 40 0.004402
15 0.2 6.5 0.1 50 50 0.00188
16 0.3 3.5 0.5 50 50 0.003931
17 0.3 5 0.1 66.66 30 0.003393
18 0.3 6.5 0.3 33.33 40 0.006546
A. Electrolyte concentration (mol/lit.); B. Machining
voltage (volts); C. Machining current, (amp); D. Duty
cycle (%); E. Frequency (Hz); F. MRR (mm
3
/min).
Table 3: Ranking of Parameters, S/N Ratio (Delta)
Level A B C D E
1 0.0033 0.0031 0.0022 0.0039 0.0051
2 0.0039 0.0041 0.0041 0.0040 0.0036
3 0.0049 0.0048 0.0057 0.0042 0.0033
Delta 0.0016 0.0017 0.0035 0.0003 0.0017
Rank 42153
The MRR was determined by calculating the volume
of material removed per unit time (mm
3
/min). The aver-
age MRR calculated from each combination of parameter
levels were tabulated in Table 2. Using Minitab
software
22
S/N response table was created for larger was
the better. The average value of S/N ratio (delta) for each
combination parameter level used in experiments as
given in Table 3. As inferred from Table 3 the current is
ranked as the most influencing process parameter, which
was followed by voltage, frequency, electrolyte concen-
tration and duty cycle.
3.2 Analysis of variance (ANOVA)
The ANOVA was performed to predict the statistical
significance of the process parameters using Minitab
software. It helps to determine the contribution of the
individual input parameter on MRR. The result of
ANOVA is presented in Table 4.
Table 4: ANOVA for MRR, using adjusted SS for tests
Source
D
O
F
Seq SS Adj SS Adj MS F P #%
A 2 0.000008 0.000008 0.000004 2.18 0.18 10.12
B 2 9.3E-06 9.30E-06 4.70E-06 2.55 0.14 11.67
C 2 0.000042 0.000038 0.000019 10.37 0.00 53.14
D 2 0.000003 3.00E-07 2.00E-07 0.09 0.91 3.69
E 2 1.05E-05 1.05E-05 5.30E-06 2.88 0.12 13.28
Error 7 6.4E-06 1.28E-05 1.80E-06 8.10
Total 17 0.000079 100
Based on the results presented in Table 4, current is
found to be the most influencing parameter, with a 53.14 %
contribution to MRR, followed by frequency (13.28 %)
and voltage (11.67 %). The main effects plot generated
by Minitab software is shown in Figure 3. It can be
inferred that a higher MRR could be obtained when the
current and voltage are at higher levels.
3.3 Implementation of genetic algorithm
The MRR was maximized by optimizing the process
parameters using Gas.
20, 21
The Minitab software was
used to model the relationship between the input para-
meters and MRR using experimental results as input.
22
The objective function obtained from the software is,
R. KRISHNAN et al.: OPTIMIZATION OF THE MACHINING PARAMETERS IN THE ELECTROCHEMICAL ...
256 Materiali in tehnologije / Materials and technology 52 (2018) 3, 253–258
Figure 3: Main effects plot generated by Minitab software

MRR = – 0.00018 + 0.00810EC + 0.000585C +
0.00888V + 0.000010DC – 0.000087F (7)
Subject to the constraints,
Electrolyte concentration (EC): 0.1 EC  0.3
Machining current (C): 0.1  C  0.3
Machining voltage (V): 3.5  V  6.5
Duty cycle (DC): 33.33  DC 66.66
Frequency (F): 30  F  50
The constrained optimization problem was solved
using GAs module available Minitab software.
22
The
GAs parameters used in this optimization problem are:
Population size = 100
Length of chromosome = 20
Selection operator = stochastic uniform
Crossover probability = 0.8
Mutation probability = 0.2
Fitness parameter = MRR
The optimized process parameters obtained for the
maximum MRR is given in Table 5. Figure 4 shows the
reduction of fitness value as the number of generation
increases. It shows that as the generation progresses the
solutions are approaching optimum. A validation experi-
ment was conducted using optimum process parameters.
The MRR obtained from both the optimization process
and validation experiments were given in Table 5.Itwas
observed that the MRR obtained from validation experi-
ment was closer to the optimized MRR obtained using
GAs (2.9 %). This study demonstrates the practical
applicability of the combined use of the Taguchi metho-
dology and GAs for optimizing the ECMM process para-
meters to obtain the maximum MRR. Figure 5 shows the
optical microscope image of the machined micro hole.
Table 5: Optimized results
(A) (B) (C) (D) (E) (F)
GA 0.25 6.49 0.29 63.34 30.00 0.05
EV 0.25 6.50 0.30 66.66 30.00 0.05
GA-genetic algorithm, EV-experimental values
4 CONCLUSIONS
In this paper the Taguchi methodology and GAs were
used together to optimize the ECMM process parameters
for Ni machining. The Taguchi L
18
orthogonal array was
used and the statistical significance of the machining
process parameters has been determined by ANOVA.
The percentage contributions of the individual parame-
ters on MRR have been found. The machining current
makes the highest contribution (53.6 %) to MRR,
followed by frequency (13.29 %) and voltage (11.72 %).
The optimization of the process parameters has been
carried out using GAs. The optimized value of electro-
lyte concentration, voltage, current, duty cycle and
frequency on MRR are: 0.25 (mol/L), 6.49 (volts), 0.29
(amp), 63.34 (%), 30.00 (Hz). The maximum MRR
obtained using the optimized process parameters is
0.05mm
3
/min. The coherence between theoretically
optimized values and the experimentally obtained values
ensures the scope for using GAs to optimize the ECMM
process parameters.
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