30120130405009

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME
71
INFLUENCE RANKING OF PROCESS PARAMETERS IN ELECTRIC
DISCHARGE MACHINING OF TITANIUM GRADE 5 ALLOY USING
BRASS ELECTRODE
Saravanan P Sivam1&2
, Dr. Antony Michael Raj2
& Dr. Satish Kumar S3
1
(Engineering Department / Nizwa College of Technology, Nizwa, Sultanate of Oman)
2
(Mechanical Department / SRM University, Chennai, India)
3
(Production Engineering Department /Velammal Engineering College, Chennai, India)
ABSTRACT
Titanium alloys have received great attention because of its high tensile strength, high
toughness, light weight, extraordinary corrosion resistance, having high hardness at extreme
temperatures and its great weld ability. For these reasons and the electrochemical incompatibility of
aluminium with the composite materials used in the aerospace industry, titanium alloys are
considered to be the replacement for aluminium in space applications. Electric discharge machining
(EDM) process, which is suitable for machining titanium alloy has the electrical parameters with its
tool shape and size affects the machining adversely. This paper explains about an experiment
conducted to investigate the effect of these electrical parameters like discharge current, pulse on time
and pulse off time along with tool shapes such as square, circular and triangle shapes on material
removal rate (MRR), tool wear rate (TWR), surface roughness and angle of deviation between entry
and exit of the holes made in titanium grade five alloy work piece when brass is used as the
electrode. Jet flushing was used in this experiment. At conclusion this paper ranks the influence of
studied parameters over important responses. Taguchi technique was used to design the experiment
with L27 orthogonal array and analysis of means(ANOM) technique was used to rank the influence.
Keywords: Entry Exit Deviation, Influence Ranking, MRR, Surface Roughness, Titanium EDM,
Tool Geometry, TWR.
I. INTRODUCTION
Electrical discharge machining (EDM) is one of the most extensively used non-conventional
machining processes. Its uniqueness to machine electrically conductive parts regardless of hardness
has been its distinctive advantage. In addition, the absence of physical contact between the electrode
and the work piece in EDM eliminating mechanical stresses and other related problems during
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 4, Issue 5, September - October (2013), pp. 71-80
© IAEME: www.iaeme.com/ijmet.asp
Journal Impact Factor (2013): 5.7731 (Calculated by GISI)
www.jifactor.com
IJMET
© I A E M E

72
machining and it is a reproductive shaping process in which the form of the electrode is mirrored in
the work piece[1]. This process is being used to machine very high hardness, conductive materials
such as titanium and its alloys. The high strength, low weight and outstanding corrosion resistance
and weld ability[2] possessed by titanium and its alloys have led to a wide and diversified range of
successful applications in aerospace, automobile, chemical plant, power generation, oil and gas
extraction, surgical instruments and other major industries. However, the susceptibility of titanium
alloys to work hardening during machining impairs their machinability. Thus machining of titanium
alloys has been a topic of interest for industrial production and scientific research worldwide. Again,
the property like strong alloying tendency or chemical reactivity of Ti-6Al-4V with most tool
materials, which causes rapid destruction of the cutting tool with galling, welding and smearing at
the interacting surface, leads to excessive chipping and premature tool failure and poor surface
finish[3]. So EDM like non-conventional machining process gained interest in machining titanium.
The good properties of titanium and the electrochemical incompatibility of aluminum with
the composite materials used in the aerospace industry with which it forms a galvanic couple,
titanium alloys are considered to be the replacement for aluminum in space applications[4]. The most
common among thirty eight grades of titanium alloys and having very wide industrial applications is
the titanium grade 5 alloys[5] which are otherwise called Ti6Al4V alloy.
II. EXPERIMENT DETAILS [6]
The work piece material used for this study was a commercially available titanium grade 5
alloy sheet which was cut to the dimension 100x50x3mm using wire cut EDM. 3 such sheets were
cut from a large sheet to avoid any variation in properties. Brass was used as a tool electrode
material because of its suitability [7]. Three geometrical shapes such as square(8x8mm),
circular(8mm diameter) and equilateral triangle shapes(8mm) were used. While circular shape was
obtained by conventional turning the other two were obtained by milling process. In each geometry,
many electrodes were produced and nine were selected with their dimensions checked for
consistency.
In total four experimental parameters were considered. Among that three are electrical
parameters which are pulse current, pulse on time and pulse off time and the fourth one is tool
geometry and its area of cross section. Table 1 gives details about the parameters and its three level
values. Electrical parameter levels were decided based on the work already done by Ahmet Hascalik
& Ulas Caydas [8] in their study on surface properties of EDM machined Ti6Al4V. They found
optimum results lies within this range of values.
TABLE I EXPERIMENTAL PARAMETERS
Parameters Level1 Level 2 Level 3
Pulse Current (A) 15 20 25
Pulse on time(µs) 50 100 200
Pulse off time(µs) 50 100 200
Geometry
( area in mm2
)
Triangle
(27.7)
Circle
(50.26)
Square
(64)

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Taguchi’s orthogonal array model was used to design the experiment to reduce the number of
replications in the experiment. The orthogonal array chosen was L27 with interaction between
parameters where each row corresponds to a particular experiment (treatment combination) and each
column identifies settings of design parameters. In the first run, for example, the three design
variables are set at their lower level (level 1). Table 2 shows the experiment model with coded
parameters where 1 indicates level 1 value, 2 indicates level 2 values and 3 indicates level 3 values
of experimental parameters.
TABLE 2 TAUGUCHI EXPERIMENT MODEL (L27 ARRAY WITH INTERACTION
BETWEEN FACTORS)
Exp.
No
Pulse
Current
Pulse on
time
Pulse
off time
Geometry
1 1 1 1 3
2 1 1 2 1
3 1 1 3 2
4 1 2 1 1
5 1 2 2 2
6 1 2 3 3
7 1 3 1 2
8 1 3 2 3
9 1 3 3 1
10 2 1 1 1
11 2 1 2 2
12 2 1 3 3
13 2 2 1 2
14 2 2 2 3
15 2 2 3 1
16 2 3 1 3
17 2 3 2 1
18 2 3 3 2
19 3 1 1 2
20 3 1 2 3
21 3 1 3 1
22 3 2 1 3
23 3 2 2 1
24 3 2 3 2
25 3 3 1 1
26 3 3 2 2
27 3 3 3 3
The experiments were performed in a ‘V5030’ EDM machine, manufactured by Electronica
Corporation of India. Dielectric fluid used was kerosene with side flushing at a pressure of 0.6MPa.
The work piece top and bottom faces were ground to a surface finish using a surface grinding
machine before conducting the experiments. The initial weights of the work piece and the tool were
weighed using a electronic balance. During the experiment the work piece was held on the machine
table using a specially designed fixture with which the work piece was gas welded. The work piece

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and tool were connected to positive and negative terminals of power supply, respectively. At the end
of each experiment, the work piece and tool were removed, washed, dried and weighed on an
electronic balance. The machining time was determined using a digital stop-watch. New brass tool
electrode was used in every experiment. Nine experiments with different geometries (3 each) in a
random order were performed on each one of three work pieces. Fig. 1 shows the completed work
piece with some brass electrodes.
Figure 1 Work piece with electrodes
After every hole was made the MRR in cubic millimeters per minute was calculated using the
equation (1) [6]
MRR = MRW / (ρw x t) (1)
Where MRW is the metal removal weight in grams, ρw is the density of the work piece in
gram per cubic millimeter and t is the machining time in minutes. Metal removal weight was
calculated by finding the difference of weight of the work piece before and after a hole was
made[6].
Similarly after every hole was made the TWR in cubic millimeters per minute was calculated
using the equation (2)
TWR = TWW / (ρt x t) (2)
Where TWW is the tool wear weight in grams, ρt is the density of the tool in gram per cubic
millimeter and t is the machining time in minutes. Tool wear weight was calculated by finding the
difference of weight of the Brass tool before and after a hole was made.
Surface roughness was measured using a surface roughness gauge and the values obtained by
allowing a ball on the plunger of the gauge to touch the inner surface of the hole made on the work
piece. After setting the gauge to zero, it showed the surface roughness value in terms of µm.
The deviation between entrance and exit (EED), otherwise known as taper angle was
measured using a co-ordinate measuring machine (CMM). The CMM was used to calculate the
length and width of the hole at the entrance and then to calculate the length and width of the hole at
the exit. For a circular hole, it was used to calculate the diameter at the entrance and then, the
diameter at the exit. The taper between these two dimensions were calculated after drafting them, in

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‘Solid Works’ software, the taper angle was observed in degrees. Lesser the deviation between the
entrance and exit of a hole better is the machining process.
While recording the data, to reduce the gauge error, whenever measurements were taken
using a gauge like weight measurement, coordinate measurement and surface roughness
measurement, three measurements were taken and averaged. The results data of the experiment is
given in Table 3.
TABLE 3 RESULTS DATA
Exp. No MRR
(mm3
/min)
TWR
(mm3
/min)
SR
(µm)
EED
(º)
1 0.849346 0.238417 4.04096 1.02527
2 0.926048 0.231563 3.32928 0.865872
3 0.455003 0.152276 1.80096 0.360528
4 0.969758 0.236379 3.49312 0.773472
5 1.009213 0.221744 2.708352 0.617652
6 0.658673 0.48165 2.72016 0.44709
7 1.237598 0.260462 3.81312 0.75768
8 1.39128 0.30344 3.00288 0.415464
9 1.110978 0.27254 2.386944 0.14616
10 0.87528 0.50115 4.02336 1.53468
11 1.02696 0.54093 3.86784 1.07541
12 0.54936 0.46683 2.36016 0.70812
13 1.181928 0.604461 4.666464 1.398474
14 1.23984 0.56316 3.48048 1.09956
15 0.76272 0.198959 2.32832 0.363048
16 1.35504 0.58188 4.6152 0.94542
17 1.51632 0.59241 3.71088 0.82299
18 1.03488 0.53976 2.35296 0.37695
19 0.975893 0.85995 4.8336 1.400562
20 1.144935 0.854217 4.2368 1.359369
21 0.67716 0.773546 3.1584 0.961101
22 1.195178 0.863636 5.2416 1.490607
23 1.36125 0.911138 4.0032 1.039761
24 0.863775 0.826781 2.9888 0.739197
25 1.480545 0.927108 4.84 1.217781
26 1.6434 0.945126 4.6608 1.101861
27 1.154588 0.853398 2.7264 0.377154
The experiment results were processed using Minitab software. Taguchi analysis based on
analysis of means (ANOM) & analysis of variance (ANOVA) was used to find out the process
parameters influence over the machining responses. P values of ANOVA and adjusted R2
values
were used to find out the goodness of the model. For all responses, adjusted R2
values were more
than 95% . So the experiment model was found to be good and produced highly reliable results.

76
III. RESULTS
The results are explained here with two type of figures. One type of figure (Main effects plot)
is showing the increasing or decreasing trend of responses with respect to different factor levels. The
other type of figure (Influence Graphs) is providing the delta values of influence of factors with
respect to a response and their ranking inside the brackets besides the factor.
A. Factors Effect on MRR, Delta Values & Influence Ranking
Results showed that pulse current and tool cross sectional area are directly proportional to
MRR. As the increase in these two factor values increased the MRR as shown in fig 3. Whereas
pulse on time at around 100µs resulted the maximum MRR. After that increase in pulse on time has
negative influence over MRR. Pulse off time is influencing very little the MRR which confirms the
earlier findings[3][9].
Fig 4 shows the delta values of the influence of various factors over MRR. As per that pulse
on time is the rank one factor having maximum influence and is closely followed by tool cross
sectional area at second rank. Current is at third rank and pulse off time the least ranked having
negligible influence comparatively.
MeanofMeans
252015
1.4
1.2
1.0
0.8
20010050
20010050
1.4
1.2
1.0
0.8
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Current Pulse on time
Pulse off time Geometry
Main Effects Plot (data means) for Means
Figure 3 Factors effect on MRR
Figure 4 Factors Extent of Influence and Ranking in MRR

77
B. Factors Effect on TWR, Delta Values & Influence Ranking
Pulse current is the single most important factor that has direct relationship with TWR as
shown in fig 5. Where as other fators are showing similar tendencies as in the case of MRR. Pulse
off time is again proving in significant.
Fig 6, shows the influence values and ranking of factors as per the influence. The single most
influential factor here is pulse current which is followed at distance by pulse on time and tool cross
sectional area.
MeanofMeans
252015
0.8
0.6
0.4
0.2
20010050
20010050
0.8
0.6
0.4
0.2
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Figure 5 Factors effect on TWR
Figure 6 Factors Extent of Influence and Ranking in TWR
C. Factors Effect on SR, Delta Values & Influence Ranking
As shown in fig 7, current when increases, increasing the surface roughness value where as
increase in pulse on time value reduces the surface roughness value in a very fast rate. As shown in
fig 8, pulse on time is the rank 1 factor influencing the surface roughness favorably and is followed
by current at distance in rank 2. Increase in pulse off time reduces the surface roughness. Its rank of
influence is 3. The area of cross section of the tool do not show any influence over the surface
roughness as the graph is almost a horizontal line.

78
MeanofMeans
252015
4.5
4.0
3.5
3.0
2.5
20010050
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4.0
3.5
3.0
2.5
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Figure 7 Factors effect on SR
Figure 8 Factors Extent of Influence and Ranking in SR
D. Factors Effect On EED, Delta Values & Influence Ranking
In case of EED, as shown in fig 9, only increase in current increases the deviation which is
un wanted. Where as pulse on time, pulse off time and area of cross section of tool when increased
reducing the deviation between entry and exit. In influence ranking, pulse on time is at rank 1,
followed by tool cross section at rank 2, current at rank 3 and pulse off time at rank 4 as shown in fig
10.
MeanofMeans
252015
1.2
1.0
0.8
0.6
0.4
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1.0
0.8
0.6
0.4
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Figure 9 Factors effect on EED

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Figure 10 Factors Extent of Influence and Ranking in EED
IV. CONCLUSIONS
In general the results are conforming the findings of [6][10]11].
• Results proves that the single most important factor that affects all the responses with
considerable ranking is the pulse on time.
• The second important factor is the pulse current. Except MRR all other responses are
negatively affected by increasing current value.
• Tool cross section is the third important factor. However it does not have any influence over
the surface roughness.
• Pulse off time is the least influential factor as it does not have any significant influence on the
responses except on EED.
REFERENCES
[1] W. Konig, D.F. Dauw, G. Levy, U. Panten, EDM—future steps towards the machining of
ceramics, Ann. CIRP 37 (2) (1988) 623–631.
[2] ASM hand book, Properties and Selection: Nonferrous alloys and Special Purpose Materials,
1993,2.
[3] Armendia M., Garay, A., Iriarte, L. M. and Arrazola, P.J. Comparison of the machinability of
Ti6Al4V and TIMETAL®54M using uncoated WC-Co tools. J. Mater. Process. Tecnol.,
2010, 210, 197-203.
[4] Lutjering, G. and Williams, J. C. Titanium. Springer, 2007.
[5] Rahman, M. M., Ashikur Rahman Khan, Md., Kadirgama, K., Noor, M.M. and Rosli Bakar,
A. Modeling of material removal on machining of Ti-6Al-4V through EDM using brass
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[6] Saravanan P Sivam, Antony L MichaelRaj, Satish Kumar S, Varahamoorthy R and
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[10] Pradhan,B.B., and Bhattacharyya, B. Modeling of Micro-EDM during machining of Titanium
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