Investigation on process response and parameters in wire electrical discharge machining

INTERNATIONALMechanical Volume 4, Issue 1, January - February (2013) © IAEME–
International Journal of JOURNAL OF MECHANICAL ENGINEERING
6340(Print), ISSN 0976 – 6359(Online)
Engineering and Technology (IJMET), ISSN 0976

AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 4 Issue 1 January- February (2013), pp. 54-65 IJMET
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INVESTIGATION ON PROCESS RESPONSE AND PARAMETERS IN
WIRE ELECTRICAL DISCHARGE MACHINING OF INCONEL 625

Rodge M. K1, Sarpate S. S2, Sharma S. B3
1&2
Research scholars, 3Professor
Production Engineering Dept., SGGSIE&T, Nanded, India.
(mkrodge64@rediffmail.com)

ABSTRACT

Continuous research in the field of material science leads to production of very
hard, tough, high temperature and corrosion resistant materials which are difficult-to
machine with conventional methods. Advanced manufacturing processes play an
important role in production of complicated profiles on such difficult-to-machine
components. Inconel 625 is one of the recent materials developed to have high
strength, toughness and corrosion resistant. The high degree of accuracy, fine surface
quality and good productivity made wire electrical discharge machining (WEDM) a
valuable tool in today’s manufacturing scenario. The right selection of the machining
conditions is the most important aspect to take into consideration in the processes
related to WEDM. As electrode wire is not reused, its wear is generally ignored.
However, it is interesting to study wire wear as it may have an effect on kerf width
and surface quality of the product. The present study is focused on investigation of the
effect of process parameters on multiple performance measures such as cutting width,
electrode wear and hardness during WEDM of Inconel 625. The control factors
considered are: pulse-on time, pulse-off time, upper flush, lower flush, wire feed and
wire tension. The relationships between control factors and responses are established
by means of regression analysis. The study demonstrates that, there is a good
agreement between experimental and predicted (theoretical) values of performance
measures.

Keywords: Orthogonal array (OR), Signal-to-noise (S/N) ratio, Taguchi’s design of
experiment (DOE), Wire Electrical Discharge Machining (WEDM)

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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 1, January - February (2013) © IAEME

1. INTRODUCTION

As newer and more exotic materials with requirement of complex shapes are
developed, conventional machining operations will continue to reach their limitations. Wire
electrical discharge machining (WEDM) is an extremely potential (thermoelectric) process
having capacity to machine parts made up of conductive materials regardless of their
hardness, toughness and geometry. In WEDM, a series of discrete electrical sparks between
the work and tool electrodes immersed in a liquid dielectric medium melt and vaporize
minute amounts of the work material which is then ejected and flushed away by the dielectric
fluid. Latest WEDMs are assisted by CNC table to produce any complex two and three
dimensional profiles on work. Due to high process capability, this method is widely used in
manufacturing of car wheels, special gears, various press tools, dies and similar complex and
intricate shapes. Hence, the increased use of the WEDM in manufacturing will continue to
grow at an accelerated rate.
Wire electrical discharge machining manufacturers and users emphasize on achievement of
better stability, higher machining productivity along with desired accuracy and surface
quality. However, due to involvement of large number of variables, even a highly skilled
WEDM operator is rarely able to achieve the optimal performance. Proper selection of
process parameters for best process performance is a challenging job. An effective way to
attempt this problem is to establish the relationship between performance measures of the
process and its controllable input parameters. Optimization of process parameters can play a
important role in this regard. In WEDM, the commonly affecting process parameters are
ignition pulse current, time between two pulses, pulse duration, servo voltage, wire speed,
wire tension and dielectric fluid injection pressure. Any slight variation in one of the
parameters can affect the production quality and economics of the process. The parameter
settings given by manufacturers are only applicable for commonly used steel grades and
alloys.
The important performance measures in WEDM are metal removal rate (MRR), cutting width
(kerf) and surface quality. In WEDM operations, MRR determines the economics of
machining and rate of production where as kerf denotes degree of precision and dimensional
accuracy. The internal corner radius to be produced is limited by the kerf. The gap between
the electrode wire and work usually ranges from 0.025 to 0.075 mm and it is constantly
maintained by a computer controlled positioning system. In setting the machining parameters,
particularly in rough cutting operation, the goal is twofold: the maximization of MRR and
minimization of kerf.
Konda R. et al. [1999] classified the various potential factors affecting the WEDM
performance measures into five major categories: the different properties of work material,
dielectric fluid, machine characteristics, adjustable machining parameters and component
geometry. They have applied the design of experiments (DOE) technique to study and
optimize the possible effects of variables and validated the experimental results using noise-
to-signal (S/N) ratio analysis. Different areas of WEDM research identified by Ho K. H. et al.
[2004] are: such as process optimization, process monitoring and control. The settings for the
various process parameters play a crucial role in producing an optimal machining
performance. The application of adaptive control systems to the WEDM is vital for the
monitoring and control of the process. The authors have investigated the advanced
monitoring and control systems including the fuzzy, the wire breakage and the self-tuning
adaptive control systems used in WEDM process. Cabanes I. et al. [2008] analyzed new

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symptoms that allow us to predict wire breakage. Symptoms may be increase in discharge
energy, peak current, increase/decrease in ignition delay time. They have proposed a novel
wire breakage monitoring and diagnostic system with virtual instrumentation system (VIS)
that measures relevant magnitudes and diagnostic system (DS) that detect new quality cutting
regimes and predicts wire breakage. Almost in 80% of total wire breakage cases, the
anticipation time longer than 50 ms has been detected. Efficiency of supervision system has
been quantified to 82%.
Parashar Vishal et al. [2010] analyzed kerf width of wire cut electro discharge machining of
SS304L steel using DOE technique. They have used statistical methods and regression
analysis for finding kerf width. Mixed OR of L32 is used for experimentation. ANOVA is
used to find out the variables affecting kerf width more significantly. Results show that pulse-
on time and dielectric flushing pressure are the most significant factor to the kerf width.
Theoretical and experimental results appeared to be in good agreement. Mohammadreza
Shabgard et al. [2011] used 3D finite element for prediction of the white layer thickness, heat
affected zone (HAZ) and surface roughness (SR) of electro discharge machined AISI H13
tool steel. They carried out experimental investigations to validate the numerical results. Both
numerical and experimental results show that increasing the pulse-on time leads to a higher
white layer thickness, depth of HAZ and surface roughness and increase in the pulse current
slightly decrease the white layer thickness and depth of HAZ with increase in SR.
Experimental and numerical results are closer to each other. Manoj Malik et al. [2012] have
carried out optimization of process parameters of WEDM using Zinc-coated brass wire for
MRR, electrode wear rate (EWR) and SR. They observed that, for minimum EWR, pulse-on
time and pulse peak current should be high. For EWR, pulse peak current is the most critical
factor and duty cycle time is the least significant parameter.
From the literature it is found that, most of the authors have studied the effect process
parameters on different response variables while WEDM of commonly used materials like,
die steel, EN31, AISI H13 steel, etc. Studies on WEDM of Inconel 625 are scantly available.
Inconel 625 is recently coming up as one of best candidate materials which is extensively
used in various applications including: marine, aerospace, chemical processing, nuclear
reactors and pollution control equipments. It is used in any environment that requires
resistance to heat and corrosion retaining mechanical properties. It is an alloy having
excellent corrosion resistance in a wide range of corrosive media being especially resistant to
pitting and crevice. It is a favorable choice for sea water applications. Therefore, it is
interesting to study the effect of control parameters on work as well as electrode materials.
The studies about effect of control factors on hardness of work material is important as it is
one of the most critical parameter in relation to wear and tear of components made out of
WEDM.

2. METHODOLOGY

2.1 Taguchi Method
Generally, the machine tool builder provides information in the form of tables to be
used for setting machining parameters. The selection of process parameter relies heavily on
the experience of the operators. With a view to alleviate this difficulty, a simple but reliable
method based on statistically designed experiments is suggested for investigating the effects
of various process parameters and to get optimal process settings. This new experimental
strategy proposed by Genichi Taguchi is called Taguchi method. It is a powerful

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experimental design tool which uses simple, effective and systematic approach for deriving
the optimal levels of machining parameters. This approach efficiently reduces the effect of
the sources of variation. It requires minimum experimental cost due to reduction in number of
experiments required to meet the specific requirements in terms of quality and reliability. It
uses specially constructed tables known as orthogonal arrays (OA). Each row represents a set
of parameters for a particular experiment. This is a best way to study the effect of large
number of variables on desired quality characteristics with small number of experiments.

2.2 Design of Experiment
To evaluate the effects of machining parameters on performance characteristics (kerf,
wire wear and hardness of machined surface) a specially designed experimental procedure is
required. In this study, the Taguchi method, a powerful tool for parameter design of the
performance characteristics is used to determine optimal machining parameters for minimum
of kerf, minimum wire wear and higher hardness of the machined surface. Six control factors
with five levels each and three response variables are used to get qualitative results. The
control factors represent stability in design of manufacturing process whereas the noise
factors denote all factors that cause variation. Table 1 shows six parameters with five levels
each. This may increase number of experiments to be carried out. However, it may help in
getting a good relationship between input and output parameters. Based on Taguchi method
we use L25 orthogonal array (obtained using Minitab 16 software) as shown in Table 2. The
experiments are performed as per orthogonal array which has 25 rows indicating 25 of
experiments. The results are shown in Table 2. The kerf is measured with the help of a
microscope. The loss in weight of electrode wire per meter length is determined by
subtracting final weight from initial weight of the wire. The hardness of machined surface is
measured with the help of hardness tester.

Table 1: Parameters and their levels
Factors level 1 level 2 level 3 level 4 level 5 Unit
Pulse-on time (Ton) 3 4 5 6 7 µs
Pulse-off time (Toff) 3 4 5 6 7 µs
Wire Feed (WF) 6 7 8 9 10 mm/s
Upper Flush (UF) 6 7 8 9 10 kg/cm2
Lower Flush (LF) 6 7 8 9 10 kg/cm2
Wire Tension (WT) 60 70 800 900 1000 kg

2.3 Experimental set-up
The inputs used in the present study are chosen through review of literature,
experience and some preliminary investigations. Each time an experiment was performed, a
particular set of input parameters was chosen. The work piece is a block of Inconel 625 with
length 100 mm × width 30 mm × thickness 10 mm. The workpiece material composition is
tested at ICS, Pune. Its percentage composition is: C - 0.035, Mn - 0.130, Si - 0.247, S -
0.010, P - 0.0064, Cr - 22.86, Ni – 58.1, Mo - 8.55, W - <0.0050, V - <0.0005, Al - 0.144,
Co - 0.072, Cu - 0.022, Nb - 3.24, Ti - 2.98, Fe- 3.60. A brass wire of 0.25 mm diameter is
used as an electrode for experimentation and it is discarded once used. The cuts of 10 mm

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depth along the length of the work are taken. The experiments are performed on Maxicut–e
WEDM (Figure 1). The machine allows operator to choose input parameters according to
geometry and material of electrodes from a manual provided by the WEDM manufacturer.

Figure 1: Maxicut-e WEDM

2.4 Signal-To-Noise Ratio
In signal-to-noise (S/N) ratio signal represents the desirable value (mean for output
parameters) and noise represents undesirable value (the square deviation of output
parameters). Thus, it is the ratio of mean to square deviation. It is designated by symbol ‘ƞ’
with unit of dB. The characteristic for which the lower value represents better performance,
the S/N ratio should be smaller the better (SB) and the characteristic for which the large value
represents better performance, the S/N ratio should be larger the better (LB). In this study the
parameters kerf and wire wear should have lower values and hardness of the machined
surface should have larger values.
The loss function (L) for kerf width, wire wear and hardness is defined as:
ଵ
LSB = ௡ ∑௡ ܻ ଶ kerf
௜ୀଵ
ଵ
LSB = ௡ ∑௡ ܻ ଶ ww
௜ୀଵ
ଵ
LLB = ∑௡ 1/ܻ ଶhv
௡ ௜ୀଵ
where Ykerf, Yww and Yhv are the responses for kerf width, wire wear and hardness
respectively and n denotes the number of experiments. The S/N ratios can be calculated as a
logarithmic transformation of the loss function as shown below.
S/N ratio for kerf width = -10 log10 (LSB) i)
S/N ratio for wire wear = -10 log10 (LSB) ii)
S/N ratio for hardness = -10 log10 (LLB) iii)
The analysis is done using the popular software specifically used for design of experiment
applications known as MINITAB 16. The S/N ratio for kerf width, wire wear and hardness is
computed using Eqs. i), ii) and iii) respectively for each treatment as shown in Table 2. Then,
overall mean for S/N ratios of kerf width, wire wear and hardness are calculated as average of
all treatment responses for each level (Table 3, 4 and 5). The graphical representation of the
effect of the six control factors on kerf width, wire wear and hardness is shown in Figure 2, 3
and 4 respectively.

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Table 2: Orthogonal array, experimental results for kerf width (KW), wire wear (WW) and
hardness (HV) of WEDMed surface along with S/N ratios

S.N Ton Tof U L W WT KW S/N WW S/N HV S/N
1 3 3 6 6 6 600 0.33 9.6297 0.01 40.000 310 49.827
2 3 4 7 7 7 700 0.32 9.8970 0.01 40.000 312 49.883
3 3 5 8 8 8 800 0.33 9.6297 0.03 30.457 286 49.127
4 3 6 9 9 9 900 0.34 9.3704 0.01 40.000 301 49.571
5 3 7 10 10 10 100 0.35 9.1186 0.03 30.457 331 50.396
6 4 3 7 8 9 100 0 31 10.172 0.01 40.000 325 50.237
7 4 4 8 9 10 600 0.30 10.457 0.01 40.000 325 50.237
8 4 5 9 10 6 700 0.35 9.1186 0.03 30.457 295 49.396
9 4 6 10 6 7 800 0.30 10.457 0.02 33.979 276 48.818
10 4 7 6 7 8 900 0.31 10.172 0.02 33.979 311 49.855
11 5 3 8 10 7 900 0.30 10.457 0.03 30.457 325 50.237
12 5 4 9 6 8 100 0.29 10.752 0.02 33.979 341 50.655
13 5 5 10 7 9 600 0.28 11.056 0.03 30.457 296 49.425
14 5 6 6 8 10 700 0.34 9.3704 0.04 27.958 290 49.248
15 5 7 7 9 6 800 0.30 10.457 0.02 33.979 325 50.237
16 6 3 9 7 10 800 0.29 10.752 0.03 30.457 301 49.571
17 6 4 10 8 6 900 0.28 11.056 0.01 40.000 309 49.799
18 6 5 6 9 7 100 0.28 11.056 0.02 33.979 299 49.513
19 6 6 7 10 8 600 0.33 9.6297 0.03 30.457 311 49.855
20 6 7 8 6 9 700 0.33 9.6297 0.02 33.979 309 49.799
21 7 3 10 9 8 700 0.30 10.457 0.05 26.020 297 49.455
22 7 4 6 10 9 800 0.29 10.752 0.01 40.000 310 49.827
23 7 5 7 6 10 900 0.29 10.752 0.01 40.000 298 49.484
24 7 6 8 7 6 100 0.32 9.8970 0.01 40.000 295 49.396
25 7 7 9 8 7 600 0.31 10.172 0.02 26.020 290 49.248

Table 3: Response Table for S/N ratios (smaller the better) for kerf width

Level Ton Toff UF LF WF WT
1 9.529 10.294 10.196 10.244 10.032 10.189
2 10.076 10.583 10.182 10.355 10.408 9.6950
3 10.419 10.323 10.014 10.081 10.128 10.410
4 10.425 9.7450 10.033 10.360 10.196 10.362
5 10.406 9.9100 10.429 9.8150 10.090 10.199
Delta 0.8960 0.8380 0.4150 0.5450 0.3760 0.7150
Rank 1 2 5 4 6 3

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Table 4: Response Table for S/N ratios (smaller the better) for wire wear
1 36.18 33.39 35.18 36.39 36.89 34.98
2 35.68 38.80 36.89 34.98 34.48 31.68
3 31.37 33.07 34.98 34.48 30.98 33.77
4 33.77 34.48 33.77 34.80 36.89 36.89
5 36.00 33.28 32.18 32.37 33.77 35.68
Delta 4.82 5.73 4.70 4.02 5.91 5.20
Rank 4 2 5 6 1 3

Table 5: Response Table for S/N ratios (larger the better) for hardness
1 49.76 49.67 49.65 49.72 49.73 49.72
2 49.71 50.08 49.94 49.63 49.54 49.56
3 49.96 49.39 49.76 49.53 49.78 49.52
4 49.71 49.38 49.69 49.80 49.77 49.79
5 49.48 49.91 49.58 49.94 49.79 50.04
Delta 0.48 0.70 0.36 0.41 0.25 0.52
Rank 3 1 5 4 6 2

Figure 2: Graphs for kerf width

Figure 3: Graphs for wire wear

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Figure 4: Graphs for hardness of WEDMed surface

Table 6: Experimental and Predicted values of kerf width (KW), wire wear (WW) and
hardness (HV)

S. N. EKW PKW EW PW EH PHV
1 0.33 0.3138 0.01 0.016 310 304.00
2 0.32 0.3166 0.01 0
0.020 312 308.00
3 0.33 0.3258 0.03 0
0.024 286 311.10
4 0.34 0.3300 0.01 0
0.028 301 314.20
5 0.35 0.3378 0.03 0
0.032 331 317.30
6 0.31 0.3000 0.01 0
0.017 325 322.24
7 0.30 0.3200 0.01 8
0.036 325 309.44
8 0.35 0.3200 0.03 8
0.026 295 306.44
9 0.30 0.3100 0.02 8
0.027 276 298.74
10 0.31 0.3200 0.02 8
0.020 311 308.00
11 0.30 0.2800 0.03 8
0.020 325 317.00
12 0.29 0.2900 0.02 6
0.021 341 309.88
13 0.28 0.3100 0.03 6
0.040 296 297.00
14 0.34 0.3200 0.04 6
0.033 290 306.68
15 0.30 0.3200 0.02 6
0.023 325 303.00
16 0.29 0.2880 0.03 6
0.034 301 308.00
17 0.28 0.2900 0.01 4
0.024 309 305.00
18 0.28 0.3000 0.02 4
0.017 299 314.00
19 0.33 0.3210 0.03 4
0.036 311 302.00
20 0.33 0.3100 0.02 4
0.037 309 294.00
21 0.30 0.2800 0.05 4
0.037 297 303.00
22 0.29 0.2900 0.01 2
0.030 310 313.16
23 0.29 0.2800 0.01 2
0.031 298 305.00
24 0.32 0.2900 0.01 2
0.021 295 302.00
25 0.31 0.3100 0.02 2
0.040 290 289.66
0
where E = Experimental, P = Predicted values from regression analysis

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The regression analysis is used for modeling the responses in terms of process variables. The
regression equations are obtained by using Minitab software.
Regression equation for kerf width
KW = 0.318 - 0.00760Ton + 0.00580Toff - 0.00100UF +0 .00320LF + 0.00040WF
-0.000024WT
Regression equation for wire wear
WW = - 0.0098 + 0.00200Ton + 0.00140Toff + 0.00220UF + 0.00060LF + 0.00280WF
-0.000030 WT
Regression equation for hardness of machined surface
HV = 286 -2.06Ton - 2.16Toff - 1.30UF + 2.16LF + 1.22WF + 0.0318WT
From these equations, the predicted (theoretical) values of kerf width, wire wear and
machined surface hardness are determined manually for all set of parameters. Table 6 shows
experimental and predicted values for above said process responses.

3. RESULTS AND DISCUSSION

The purpose of the experimentation is to identify the factors which have strong effects
on the machining performance. From mean of S/N ratios (Table 3) for kerf width, it is found
that pulse-on time has highest rank ‘1’. Therefore, it has most significant effect on kerf width.
As pulse-on time increases the kerf width increases significantly. The wire feed has least
effect on kerf width. The order of other influencing parameters for kerf width is: pulse-off
time, wire tension, lower flush and upper flush. Also, from mean of S/N ratios (Table 4) for
wire wear, it is observed that, the wire feed has highest rank ‘1’ and therefore, it affects wire
wear significantly. The wire wear initially decreases significantly with increase in wire feed;
however it increases with further rise in wire feed. This may be due the combined effect of
other factors. The lower flush has least effect on wire wear. The order of other influencing
parameters for wire wear is: pulse-off time, wire tension, pulse-on time and upper flush.
Table 5 shows that, for hardness of the machined surface, the pulse-off time has highest rank
‘1’ and hence, it affects hardness of the machined surface most significantly. However, the
effect of increase in pulse-off time on hardness of the machined surface has no fixed nature.
The hardness first increases and then decreases significantly. Again it increases. The wire
feed has least effect on hardness. The order of other influencing parameters of hardness is:
wire tension, pulse-on time, lower flush, upper flush and upper flush.
From Table 3, the optimal combination of process parameters for minimum kerf width is
found to be: A1B4C3D5E1F2. The symbols A, B, C, D, E and F represents process
parameters: Ton, Toff, UF, LF, WF and WT respectively and numbers represents the levels.
This means, to have minimum kerf width, Ton should be set on level 1, Toff on 4, UF on 3,
LF on 5, WF on 1 and WT on 2. Similarly from Table 4, it is observed that, the optimal
combination of process parameters for minimum wire wear is: A3B3C5D5E3F2. This means,
to have minimum wire wear, Ton should be set on level 3, Toff on 3, UF on 5, LF on 5, WF
on 3 and WT on 2. It is to be noted that the optimal levels of factors differ widely for both the
objectives (for minimum kerf width and minimum wire wear). From Table 5, the optimal
combination of process parameters for hardness of wire electrical discharge machined surface
is: A3B2C2D5E5F5. The hardness of the machined surface should be more for better working
performance. This means to have high hardness Ton should be set on level 3, Toff on 2, UF
on 2, LF on 5, WF on 5 and WT on 5.
From Figure 5, it is observed that, the predicted values for kerf width determined from regression
analysis are in agreement to experimental values. However, wire wear during the WEDM operations

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is sensitive to many operational parameters other than current characteristics of the system such as
wire tension, wire feed, flushing conditions which must have attributed toward the non uniformity in
experimental wire wear reading with respect to predicted one (Figure 6). Lower wire wear generally
results in higher kerf width and the present experimental study also depicts the similar results. Figure
7, shows good agreement between experimental and predicted hardness values reasonably. The
hardness of the machined surface is first decreased and then improved when lower flush, wire feed
and wire tension is increased. Whereas it first increases and then decreases when pulse-on, pulse-off
and upper flush is increased. Thus, the recommended values are of the combined effect of the process
parameters.

Figure 5: Comparison of experimental and predicted values of kerf width

EKW
0.37 PKW
0.35
0.33
Kerf width

0.31
0.29
0.27
0.25
1 3 5 7 9 11 13 15 17 19 21 23 25

Figure 6: Comparison of Experimental and predicted values of wire wear

EWW
0.06 PWW
0.05
Wire wear

0.04
0.03
0.02
0.01
0
1 3 5 7 9 11 13 15 17 19 21 23 25

Figure 7: Comparison of Experimental and predicted values of hardness
360
EHV
340
PHV
HARDNESS HV

320
300
280
260
240
1 3 5 7 9 11 13 15 17 19 21 23 25

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4. CONCLUSIONS

WEDM process parameter’s optimization is responsive not only to the process
variables but also the work materials. Therefore, for quality machining performance of a
material, parameter optimization is essential to result cost effective usages of the material for
the given application. The present investigation revealed that pulse-on ranks high in terms of
machining performance of Inconel 625 and it has a predominant effect on kerf width. During
machining of Inconel, as pulse-on increases, the kerf width increases which resulted
relatively lower wire wear. The wire wear initially decreases significantly with increase in
wire feed; however it increases with further rise in wire feed. This may be due the combined
effect of other factors. With regard to hardness of machined components, pulse-off causes
significant variation. Optimized process parameters could be used as guideline for WEDM of
Inconel 625.

5. REFERENCES

1. Atul Kumar and D. K. Singh, ‘Strategic optimization and investigation effect of process
parameters on performance of Wire Electrical Discharge Machine (WEDM)’, Int. J. of
Engineering Science and Technology, Vol. 4, No. 6, 2012, 2766-2772..
2. Atul Kumar and D. K. Singh, ‘Performance analysis of Wire Electrical Discharge
Machining (WEDM)’, Int. J. of Engineering Science and Technology, Vol. 1, No. 4, 2012,
1-9.
3. Cabanes I., Portillo E, Marcos M. and Sanchez J A, ‘On-line prevention of wire breakage
in wire electro-discharge machining’, Robotics and Computer-Integrated Manufacturing,
Volume 24, Issue 2, April 2008, Pages 287-298
4. Choudhary Rajesh, H. Kumar and R. K. Garg, ‘Analysis and evaluation of heat affected
zone in electrical discharge machining of EN-31 die steel’, Indian Journal of Engineering
and Material Science, Vol. 17, 2010, 91-98.
5. Debabrata Mandal, Surjya K. Pal and Partha Saha, ‘Modeling of electrical discharge
machining process using back propagation neural network and multi-objective
optimization using non-dominating sorting genetic algorithm-II’, Journal of Materials
processing Technology 186 (2007) 154-162.
6. Hsien-Ching Chen, Jen-Chang Lin, Yung-Kuang Yang, and Chih-Hung Tsai,
‘Optimization of wire electrical discharge machining for pure tungsten using a neural
network integrated simulated annealing approach’, Expert Systems with Applications 37
(2010) 7147-7153.
7. Konda R., Rajurkar K. P., Bishu R. R. , Guha A. and Parson M., ‘Design of experiments
to study and optimize process performance’, Int. J. Quality, Reliability and Management,
16 (1) (1999) 56–71.
8. Mahapatra S. S. and Amar Patnaik, ‘Parametric optimization of WEDM using Taguchi
technique’, Journal of Braz. Soc. of Mech. Sci. and Engg. (2006).
9. Manoj Malik, Rakesh Kumar Yadav, Nitesh Kumar, Deepak Sharma and Manoj,
‘Optimization of process parameters of wire EDM using Zinc-coated brass wire’,
International Journal of Advanced Technology & Engineering Research (IJATER), Vol. 2,
No. 4, 2012, 127-130.

64


10.Manna A and Bhattacharyya, ‘Study for optimization of CNC-Wire cut EDM parameters
during of Al/Sic-MMC using design of experiment’, 20th AIMTDR, BIT, Ranchi 13-14
December (2002) 294-299.
11.Mohammadreza Shabgard, Samad Nadimi Bavil Oliaei, Mirsadegh Seyedzavvar and
Ahmad Najadebrahimi, ‘Experimental investigation and 3D finite element prediction of
the white layer thickness, heat affected zone, and surface roughness in EDM process’,
Journal of Mechanical Science and Technology 25 (12) (2011) 3173-3183.
12.Parashar Vishal, A. Rehaman, J. L. Bhagoria, Y.M. Puri, ‘Kerf width analysis for wire cut
electro discharge machining of SS304L using design of experiments’, Indian Journal of
Science and Technolgy, vol. 3 No. 4, 2010, 369-373.
13.Pujari Srinivasa Rao, Koona Ramji and Beela Satyanarayana, ‘Prediction of material
removal rate for Aluminum BIS-24345 alloy in Wire-Cut EDM’, Int. J. of Engineering
Science and Technology, Vol. 2, No. 12, 2010, 7729-7739.
14.Sarkar S., Mitra S. and Bhattacharyya B., ‘Parametric analysis and optimization of wire
electrical discharge machining of γ-titanium aluminide alloy’, Journal of Materials
Processing Technology 159 (2005) 286-294.
15.Scott D., Boyina S. and Rajurkar K. P., ‘Analysis and optimization of parameter
combination in wire electrical discharge machining’, Int. J. Prod. Res. 29 (11) (1991)
2189–2207.
16.Shajan Kuriakose and M. S. Shunmugam, ‘Characteristics of wire-electro discharge
machined Ti6A14V surface’, Materials Letters, 58 (2004) 2231-2237.
17.Sivakiran S., Bhaskar Reddy and Eswara Reddy, ‘Effect of process parameters on MRR in
wire electrical discharge machining of En31 steel’, Int. J. of Engineering Research and
applications (IJERA), Vol. 2, No. 6, 2012, 1221-1226.
18.Tarng Y. S., Ma S. C. and Chung L. K., ‘Determination of optimal cutting parameters in
wire electrical discharge machining’, Int. J. Mach. Tools & Manuf. 35 (12) (1995) 1693–
1701.
19. Satyanarayana.B, Ranga Janardhana.G, Kalyan.R.R and Hanumantha Rao.D, “Prediction
of Optimal Cutting Parameters For High Speed Dry Turning of Inconel 718 Using
Gonns”, International Journal of Mechanical Engineering & Technology (IJMET),
Volume 3, Issue 3, 2012, pp. 294 - 305, Published by IAEME.
20 U. D. Gulhane, A. B. Dixit, P. V. Bane and G. S. Salvi, “Optimization Of Process
Parameters For 316L Stainless Steel Using Taguchi Method And Anova”, International
Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp.
67 - 72, Published by IAEME.
21. U. D. Gulhane, S. B. Mishra and P. K. Mishra, “Enhancement of Surface Roughness of
316L Stainless Steel and Ti-6al-4v Using Low Plasticity Burnishing: Doe Approach”,
International Journal of Mechanical Engineering & Technology (IJMET), Volume 3,
Issue 1, 2012, pp. 150 - 160, Published by IAEME.

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