parametric study and optimization of wedm parameters for ck45 steel
DESCRIPTION
http://www.seipub.org/ijepr/paperInfo.aspx?ID=3108 CK45 steel is a material that has a wide range of applications in military and automotive industry. It can be classified as a difficult-to-cut material, not suitable for traditional machining. The present paper presents a study of the optimum selection of wire electric discharge machining (WEDM) conditions for CK45 steel. Feeding speed, duty factor, water pressure, wire tension and wire speed have been considered the main factors affecting WEDM performance criteria. The process performances such as; material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) were evaluated. Response surface methodology (RSM) was employed to develop experimental models. The surface composition of the machined surface was examined through X-ray diffraction technique. The results reveal that the feeding speed and duty factor are the most vital factors controlling the MRR. Water pressure has been found a sensible effect of MRR espeTRANSCRIPT
www.seipub.org/ijepr International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013
156
Parametric Study and Optimization of WEDM
Parameters for CK45 Steel Taha A. El‐Taweel*1, Ahmed M. Hewidy2
Production Engineering and Mechanical Design Department
Faculty of Engineering, Menoufia University, Shebin El‐Kom, Egypt
Abstract
CK45 steel is a material that has a wide range of applications
in military and automotive industry. It can be classified as a
difficult‐to‐cut material, not suitable for traditional
machining. The present paper presents a study of the
optimum selection of wire electric discharge machining
(WEDM) conditions for CK45 steel. Feeding speed, duty
factor, water pressure, wire tension and wire speed have
been considered the main factors affecting WEDM
performance criteria. The process performances such as;
material removal rate (MRR), tool wear rate (TWR), and
surface roughness (SR) were evaluated. Response surface
methodology (RSM) was employed to develop experimental
models. The surface composition of the machined surface
was examined through X‐ray diffraction technique. The
results reveal that the feeding speed and duty factor are the
most vital factors controlling the MRR. Water pressure has
been found a sensible effect of MRR especially at a higher
feeding speed. Further, the minimum tool wear rate has
been obtained at the parametric combination of low feeding
speed and low duty factor. It has also been clarified that the
increase of wire tension and wire speed lead to a relatively
insignificant change in surface quality.
Keywords
WEDM, Material Removal Rate; Tool Wear Rate; Surface
Roughness; Response Surface Methodology; Optimization
Introduction
Electrical discharge machining (EDM) is one of the
nontraditional machining processes which has been
widely used to produce dies and molds, as well to
finish parts for aerospace and automotive industry
(Snoeys et al., 1986). This technique was developed in
the late 1940s of which the process is based on
removing material from a part by means of a series of
repeated electrical discharges between a tool called the
electrode and the workpiece in the presence of a
dielectric fluid [(Ho and Newman, 2003), (Bojorquez et
al., 2002)]. Wire electro‐discharge machining (WEDM)
uses the same principles of material removal as EDM,
but using a traveling, small‐diameter wire as the
electrode. The wire travels from a supply spool,
through the workpiece, and onto a take‐up spool.
Hasçalýk et al. (2004) presented an experimental
investigation of the machining characteristics of AISI
D5 tool steel using WEDM process. During their
experiments, parameters such as open circuit voltage,
pulse duration, wire speed and dielectric pressure
were changed to explore their effect on the surface
roughness and metallurgical structure, then using
optical and scanning electron microscopy, surface
roughness and microhardness tests to study the
characteristics of the machined specimens. They found
that the intensity of the process energy does affect the
amount of recast layer and surface roughness as well
as microcracking.
Wire electro‐discharge machining induced a new
phase in Ti–46Al–2Cr intermetallic alloy which has
been identified using X‐ray diffraction technique by
Qin et al, (2003). It was observed that a new phase was
found to be neither TiO2 nor Al2O3, but well consistent
with titanium hydride with the lattice parameter of
4.49–4.50 A˚. Further, the new phase exists in the
WEDM cut alloy surface layer limited to about 70 μm
thickness, much deeper than the WEDM induced
recast layer thickness of 2–3 μm. On processing,
vacuum annealled at 400–600˚C, has been proposed to
remove this hydride induced by WEDM. In addition,
it was shown that the WEDM induced microcracks
extensively happened on the EDM‐cut surface and
penetrated into a matrix of up to 10–30 μm.
Modeling in WEDM helps get a better understanding
of the complex process (Abbas, 2007, Puertas and Luis,
2003, Ho et al, 2004). Semi empirical models of surface
finish, MRR and wire wear [(Tsai and Wang, 2001),
(Poroś and Zaborski, 2009)] of the WEDM process for
various materials have been established by employing
dimensional analysis. Using the design of the
experiment method, the process parameter viz. peak
current, pulse duration, electric polarity and material
International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013 www.seipub.org/ijepr
157
properties are identified. The final results showed that
the average error between the experiment and
prediction was less than 10% for surface finish model
and less than 20% for MRR.
The modeling was first introduced by Jeswani (1979)
using dimensional analysis to predict the tool wear.
After that, various methods were introduced to
predict the output of EDM process. In addition, the
modeling of the WEDM process by means of
mathematical techniques has also been applied to
effectively relate the large number of process variables
to the different performance of the process (Yahya,
2004). Sharif and Noordin (2006) established an
empirical relation of machining responses using the
RSM technique while machining Ti‐6246 workpiece by
adding SiC powder in the dielectric. The results show
that MRR, tool wear, and surface quality are greatly
influenced by the current, voltage, and pulse on‐time,
and the surface roughness is highly influenced by the
presence of SiC additives.
Chiang et al. (2006) presented an approach for
optimization the WEDM process of Al2O3 particle
reinforced material (6061 alloy) with multiple
performance characteristics based on the grey
relational analysis and Taguchi technique. In their
study, the machining parameters, namely the cutting
radius of workpiece, on‐time, off‐time of discharging,
arc on‐time, arc off‐time of discharging voltage, wire
feed and water flow, are optimized with considerations
of multiple performance, such as MRR and SR.
El‐Taweel (2006) studied the effect and optimization of
machining parameters on the MRR and SR in the
WEDM process of some Al‐Cu‐TiC‐Si composite. The
variation of MRR and SR with machining parameters
was mathematically modeled by using the nonlinear
regression analysis method. The optimal search for
machining parameters for the objective of maximum
MRR and minimum SR was performed. Based on the
experimental confirmation, it was observed that the
material removal rate increased by 1.58 times and
surface roughness was decreased by 1.11 times.
The objective of using response surface methodology
(RSM) is to not only investigate the response over the
entire factor space, but also locate the region of interest
where the response reaches its optimum or near
optimal value. RSM technique has also been applied to
optimize the input of WEDM parameters for Inconel
601 by Hewidy el al. (2005)who reported that the
analysis of response parameters using RSM technique
had the advantage of explaining the effect of each
working parameter on the value of resultant response
parameter.
Derringer and Suich (1980) described a multiple
response method called desirability that is an
attractive method to industry for optimization of
multiple quality characteristic problems, making use
of an objective function, called the desirability function
and transforming an estimated response into a scale
free value called desirability. The desirable ranges are
from zero to one; of which one represents the ideal
case; while zero indicates that one or more responses
are outside their acceptable limits. Composite
desirability is the weighted geometric mean of the
individual desirability for the responses. The factor
settings with maximum total desirability are
considered to be the optimal parameter conditions
[(Castillo, 1996), El‐Taweel, 2009)].
CK45 steel, a material that has a wide range of
applications in military and automotive industries can
be classified as a difficult‐to‐cut material, not suitable
for traditional machining. The published literature has
indicated that few studies have been reported for the
optimization of process parameters in WEDM for
CK45 steel. El‐Taweel (2009) investigated the
correlation of process parameters in die sinking EDM
of CK45 steel with Al–Cu–Si–TiC composite electrode
produced using powder metallurgy (P/M) technique
and evaluated MRR and TWR. It has been found that
such electrodes are more sensitive to peak current and
on‐time than conventional electrodes. To achieve
maximum MRR, minimum TWR and SR, the process
parameters are optimized and experimental
verification were found to be in good agreement.
However, it has been found that there is no available
data of the WEDM process of this material. Therefore,
it is imperative to develop a suitable technology
guideline for optimum WEDM of CK45 steel. So the
aim of the present work is to develop models to
correlate the inter‐relationships of various WEDM
parameters of CK45 steel such as feeding speed (Fs),
duty factor (D), wire tension (T), wire speed (Ws) and
water pressure (P) on MRR, TWR and SR.
Experimental Details
Experimental Procedure and Measurments
In the present study, MRR, TWR and SR have been
considered to evaluate the machining performance.
MRR, TWR and SR are correlated with input
machining parameters such as feeding speed (Fs), duty
factor (D), wire Tension (T), wire Speed (Ws) and
water Pressure (P). The experiments were conducted
www.seipub.org/ijepr International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013
158
on a W‐B30J.S CNC wire electrical discharge machine.
Figure 1 shows the used experimental set up attached
with WEDM machine.
FIG. 1 THE USED EXPERIMENTAL SET UP
The wire electrode material used in the WEDM
process was made from Brass‐CuZn37 with diameter
0.25 mm. The workpiece material used in these
experiments was CK45 steel with dimensions 8×8×8
mm3. Table 1 shows the chemical composition of CK45
steel. The selection of this material was made, taking
into account its wide range of applications in military
and automotive industries (El‐Taweel, 2009). It can be
classified as a difficult‐to‐cut material, not suitable for
traditional machining. During machining, de‐ionized
water was circulated as the dielectric fluid around
wire (co‐axial) and with side flushing technique. The
machining time for each experiment was variable and
related to the machining conditions and fixed
parameters which are listed in Table 2. These were
chosen through reviews of experience, literature
surveys [(Abbas, 2007), (Puertas and Luis, 2003), (Ho
et al, 2004), (Tsai and Wang, 2001), (Poroś and
Zaborski, 2009), (Chiang et al. 2006)], and some
preliminary investigations.
TABLE 1 CHEMICAL COMPOSITION FOR CK45 STEEL (WT%)
C Si Mn P S Cr Mo
0.4771 0.2105 0.628 0.0109 0.0111 0.1079 0.0236
Al Co Cu W Sn Fe Ni
0.0073 0.0073 0.1896 <0.005 0.0103 98.23 0.0788
TABLE 2 WEDM MACHINING CONDITIONS
Working condition Value
Workpiece material CK45 steel
Tool (+) Polarity
Wire material Brass CuZn37
Feeding speed 1.7‐ 4.9 mm/min
Pulse‐on time 1‐9 μs
Pulse‐off time 9 μs
Wire tension 800‐2400 gf
Wire speed 5‐15 m/s
Water pressure 1‐3 MPa
Wire diameter 0.25 mm
Peak current Automatically selected
Dielectric fluid De‐ionized water
The specimen and wire were weighted before and
after machining using a digital balance (Sartorius, type
1712, 0.0001 g).
The metal removal rate was specified using the
following equation:
MRR = (Wb‐ Wa)/t (g/min) (1)
where Wb: specimen weight before machining (g); Wa:
specimen weight after machining (g).; t: machining
time (min).
The tool wear rate was evaluated through wire weight
for a length of one meter before and after machining
by using the following equation:
TWR = 60 (Tb‐ Ta)Ws (g/min) (2)
Where Tb: wire weight for one meter before machining
(g/m). Ta: wire weight for one meter after machining
(g/m). Ws: wire speed (m/s).
The roughness average (Ra) for each specimen was
measured by surface tester (Mitutoye, Surftest‐402).
Each surface roughness value was obtained by
averaging four measurements. The instrument is
adjusted to the following conditions during
measurement; measuring length (5mm), measuring
speed (0.5 mm/s) and sampling length (0.8 mm).
The microscopic investigations of the WEDM surface
are investigated by (JEOL JSM‐5200LV) scanning
electron microscope (SEM). X pert pro‐panalytical‐X
ray diffract meter (XRD) is used to investigate the
surface component after WEDM.
Experimental Design
Response surface methodology (RSM) is an interaction
of mathematical and statistical techniques for
modelling and optimizing the response variable
models involving quantitative independent variables
[(Montgomery, 2001), (Antony, 2003)]. In the present
work, the ranges of the feeding speed (Fs), duty factor
(D), wire tension (T), wire speed (Ws) and water
pressure (P) settings have been selected. Actual and
coded values of the input process parameters have
been listed in Table 3. Experiments have been carried
out according to the experimental plan based on
central composite half‐fraction second‐order rotatable
design (CCD) as shown in Table 4. This design
consists of 32 factorial runs, 16 axial runs and the
number of star points is 10. The extra seven points are
added at the center to give roughly equal precision for
Yu within a circle of radius 1. Yu is the uth response in
the experimentation. One important disadvantage of
using second order polynomial in RSM is that the size
International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013 www.seipub.org/ijepr
159
of experiments becomes too large and analysis
becomes too complicated with more than three X
variables or with more than three levels. Central
composite designs (CCD) are one of those means.
Proceeding a step ahead, central composite rotatable
designs of second order have been found to be the
most efficient tool in RSM to establish the
mathematical relation of the response surface using
the smallest possible number of experiments without
losing its accuracy.
Mathematical Modelling
Through utilizing the design of experiments and
applying regression analysis, the modelling of the
desired response to several independent variables can
be obtained [(Montgomery, 2001), (Antony, 2003)]. If
all variables are assumed to be measurable, the
quadratic response surface model of Yu can be written
as follows:
2
1 1
k k kY b b X b X X b Xu i i ij i ii io jj ii i
(3)
where Yu is the corresponding response function (or
response surface), X1, X2, X3.....Xk are coded values of
the machining process parameters and ε is the fitting
error of the uth observation. In this study, for five
variables under consideration (Fs, D, T, Ws and P), a
second order polynomial regression model, called
quadratic model, is proposed.
TABLE 3 CODED AND ACTUAL VALUES OF INPUT PARAMETERS
Input parameters Symbol Levels
‐2 ‐1 0 +1 +2
Feeding speed (Fs), mm/min X1 1.7 2.5 3.3 4.1 4.9
Duty factor (D) X2 0.1 0.2 0.3 0.4 0.5
Wire tension (T), gf X3 800 1200 1600 2000 2400
Wire speed (Ws), m/s X4 5 7.5 10 12.5 15
Water pressure (P), MPa X5 1 1.5 2 2.5 3
TABLE 4 EXPERIMENTAL DESIGN MATRIX AND RESULTS
Exp. No.
Input process parameters Experimental results
Fs, D T, Ws, P, MRR, g/min TWR, g/min Ra, μm
Coded Actual Coded Actual Coded Actual Coded Actual Coded Actual
1 ‐1 2.5 ‐1 0.2 ‐1 7.5 ‐1 1200 1 2.5 0.0391 0.0260 2.3
2 1 4.1 ‐1 0.2 ‐1 7.5 ‐1 1200 ‐1 1.5 0.0453 0.0440 2.4
3 ‐1 2.5 1 0.4 ‐1 7.5 ‐1 1200 ‐1 .1.5 0.0685 0.0470 2.5
4 1 4.1 1 0.4 ‐1 7.5 ‐1 1200 1 2.5 0.0735 0.0510 2.7
5 ‐1 2.5 ‐1 0.2 1 12.5 ‐1 1200 ‐1 1.5 0.0371 0.0360 2.8
6 1 4.1 ‐1 0.2 1 12.5 ‐1 1200 1 2.5 0.0426 0.0450 2
7 ‐1 2.5 1 0.4 1 12.5 ‐1 1200 1 2.5 0.0698 0.0480 2.5
8 1 4.1 1 0.4 1 12.5 ‐1 1200 ‐1 1.5 0.0739 0.0510 2.6
9 ‐1 2.5 ‐1 0.2 ‐1 7.5 1 2000 ‐1 1.5 0.0347 0.0340 2.7
10 1 4.1 ‐1 0.2 ‐1 7.5 1 2000 1 2.5 0.0650 0.0460 2.8
11 ‐1 2.5 1 0.4 ‐1 7.5 1 2000 1 2.5 0.0601 0.0490 3.3
12 1 4.1 1 0.4 ‐1 7.5 1 2000 ‐1 1.5 0.0755 0.0520 2.5
13 ‐1 2.5 ‐1 0.2 1 12.5 1 2000 1 2.5 0.0374 0.0410 2.7
14 1 4.1 ‐1 0.2 1 12.5 1 2000 ‐1 1.5 0.0569 0.0430 2.8
15 ‐1 2.5 1 0.4 1 12.5 1 2000 ‐1 1.5 0.0474 0.0470 2.6
16 1 4.1 1 0.4 1 12.5 1 2000 1 2.5 0.0943 0.0490 2.5
17 ‐2 1.7 0 0.3 0 10 0 1600 0 2 0.0423 0.0270 1.9
18 2 4.9 0 0.3 0 10 0 1600 0 2 0.0743 0.0460 4.9
19 0 3.3 ‐2 0.1 0 10 0 1600 0 2 0.0284 0.0240 2.5
20 0 3.3 2 0.5 0 10 0 1600 0 2 0.0943 0.0480 2.2
21 0 3.3 0 0.3 ‐2 5 0 1600 0 2 0.0619 0.0410 3.2
22 0 3.3 0 0.3 2 15 0 1600 0 2 0.0701 0.0280 2.2
23 0 3.3 0 0.3 0 10 ‐2 800 0 2 0.0609 0.0310 3.5
24 0 3.3 0 0.3 0 10 2 2400 0 2 0.0758 0.0380 2.1
25 0 3.3 0 0.3 0 10 0 1600 ‐2 1 0.0615 0.0460 3.4
26 0 3.3 0 0.3 0 10 0 1600 2 3 0.0705 0.0250 2.5
27 0 3.3 0 0.3 0 10 0 1600 0 2 0.0659 0.0270 2.4
28 0 3.3 0 0.3 0 10 0 1600 0 2 0.0653 0.0280 2.4
29 0 3.3 0 0.3 0 10 0 1600 0 2 0.0655 0.0270 2.5
30 0 3.3 0 0.3 0 10 0 1600 0 2 0.0639 0.0270 2.4
31 0 3.3 0 0.3 0 10 0 1600 0 2 0.0645 0.0290 2.4
32 0 3.3 0 0.3 0 10 0 1600 0 2 0.0652 0.0270 2.5
www.seipub.org/ijepr International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013
160
The coefficient bo is the free term, the coefficients bi are
the linear terms, the coefficients bij are the interaction
terms and the coefficients bii are the quadratic terms.
Using the results presented in Table 4, the full form of
the derived models can be presented. Based on Eq. (3),
the effect of input parameters on values of metal
removal rate, tool wear rate and surface roughness has
been evaluated by computing the values of various
constants in Table 4. The mathematical models of MRR,
TWR, and SR can be expressed in coded form as
follows:
1 2 4
2 25 1 2
MRR 0.0662 0.0082 0.014 0.0025
0.002 0.003 0.0021
X X X
X X X
(4)
21 2 1
2 2 2 22 3 4 5
TWR 0.026 0.004 0.005 0.0035
0.0034 0.003 0.003 0.0033
X X X
X X X X
(5)
1 2 3 5
1 2 1 3 1 5 2 3
2 2 22 5 1 4 5
SR 9.99 1.73 10.963 0.236 4.08
1.77 0.045 0.255 0.4
5.275 0.048 3.196 0.47
X X X X
X X X X X X X X
X X X X X
(6)
TABLE 5 ANOVA FOR MRR
Source SS DF MS F value P.value Prob>F
Model 0.007773 20 0.000389 14.3581 < 0.0001
X1 0.001615 1 0.001615 59.6767 < 0.0001
X2 0.004724 1 0.004724 174.501 < 0.0001
X3 8.28E‐06 1 8.28E‐06 0.30602 0.5912
X4 0.00011 1 0.00011 4.05087 0.0693
X5 0.000153 1 0.000153 5.63409 0.0369
X1X2 6.13E‐06 1 6.13E‐06 0.22629 0.6436
X1X3 2.28E‐05 1 2.28E‐05 0.84231 0.3784
X1X4 0.000521 1 0.000521 19.2462 0.0011
X1X5 1.63E‐06 1 1.63E‐06 0.06005 0.8109
X2X3 2E‐05 1 2E‐05 0.73979 0.4081
X2X4 9.17E‐05 1 9.17E‐05 3.3869 0.0928
X2X5 3.11E‐05 1 3.11E‐05 1.14819 0.3069
X3X4 8.56E‐07 1 8.56E‐07 0.0316 0.8621
X3X5 1.43E‐05 1 1.43E‐05 0.52645 0.4833
X4X5 0.000111 1 0.000111 4.09231 0.0681
X12 0.000231 1 0.000231 8.55102 0.0138
X22 0.000123 1 0.000123 4.53887 0.0565
X32 2.29E‐05 1 2.29E‐05 0.84699 0.3771
X42 2.58E‐06 1 2.58E‐06 0.09532 0.7633
X52 2.29E‐05 1 2.29E‐05 0.84699 0.3771
Residual 0.000298 11 2.71E‐05
Lack of fit 0.000295 6 4.92E‐05 12.43 < 0.0001
Pure error 2.64E‐06 5 5.27E‐07
Core Total 0.008071 31
DF: Degree of freedom; SS: Sum of squares; MS: Mean of squares
The adequacy of the models is checked using the
analysis of variance (ANOVA) technique. As per this
technique, if the calculated F ratios of the developed
models do not exceed the standard tabulated values of
F ratio for desired level of confidence, then the models
are considered to be at the confidence level
(Montgomery, 2001). The ANOVA tables for MRR,
TWR and SR are presented in Tables 5, 6 and 7,
respectively. It can be noted that there are some terms
omitted from the equations. These terms are non‐
significant terms according to the ANOVA of the
models.
TABLE 6 ANOVA FOR TWR
Source SS DF MS F value P.value Prob>F
Model 0.002449 20 0.00012 12.875 <0.0001
X1 0.000345 1 0.00035 8.102 <0.0001
X2 0.000672 1 0.00067 15.78 <0.0001
X3 9.37E‐06 1 9.4E‐06 0.22 0.1281
X4 3.04E‐05 1 3E‐05 0.713 0.0030
X5 7E‐05 1 7E‐05 1.645 0.0091
X1X2 5.26E‐05 1 5.3E‐05 1.234 0.0044
X1X3 2.76E‐05 1 2.8E‐05 0.647 0.1495
X1X4 1.41E‐05 1 1.4E‐05 0.33 0.1026
X1X5 6.25E‐08 1 6.3E‐08 0.001 0.2307
X2X3 2.26E‐05 1 2.3E‐05 0.53 0.1007
X2X4 1.06E‐05 1 1.1E‐05 0.248 0.3279
X2X5 6.25E‐08 1 6.2E‐08 0.001 0.6188
X3X4 1.06E‐05 1 1.1E‐05 0.248 0.3279
X3X5 7.56E‐06 1 7.6E‐06 0.178 0.6188
X4X5 1.81E‐05 1 1.8E‐05 0.424 0.168
X12 0.000362 1 0.00036 8.493 <0.0001
X22 0.000336 1 0.00034 7.899 <0.0001
X32 0.000266 1 0.00027 6.246 <0.0001
X42 0.000266 1 0.00027 6.246 <0.0001
X52 0.000312 1 0.00031 7.326 <0.0001
Residual 0.000468 11 4.3E‐05
Lack of fit 0.000465 6 7.7E‐05 10.7 0.0140
Pure error 3.5E‐06 5 7E‐07
Core Total 0.002918 31
TABLE 7 ANOVA FOR SR
Source SS DF MS F value P. value Prob>F
Model 6.08 20 0.3 18.68 <0.0001
X1 6.08 20 0.3 18.68 <0.0001
X2 0.47 1 0.47 28.9 0.0002
X3 0.09 1 0.09 5.55 0.038
X4 1.17 1 1.17 72.18 <0.0001
X5 0.23 1 0.23 14.19 0.0031
X1X2 0.85 1 0.85 52.44 <0.0001
X1X3 0.32 1 0.32 19.67 0.001
X1X4 0.13 1 0.13 8.06 0.0161
X1X5 0.092 1 0.09 5.65 0.0367
X2X3 0.177 1 0.17 10.22 0.0085
X2X4 0.17 1 0.17 10.33 0.0082
X2X5 0.12 1 0.12 7.31 0.0205
X3X4 1.11 1 1.11 68.4 <0.0001
X3X5 0.02 1 0.02 1.2 0.2959
X4X5 0.022 1 0.022 1.38 0.2644
X12 0.028 1 0.47 28.84 0.0002
X22 0.03 1 0.028 1.75 0.2131
X32 0.09 1 0.03 1.84 0.2021
X42 0.1 1 0.09 5.56 0.038
X52 0.41 1 0.1 6.33 0.0287
Residual 0.18 11 0.41 25.1 0.0004
Lack of fit 0.17 6 0.016
Pure error 0.013 5 0.028 10.25 0.0107
Core Total 6.26 31 2.667 E‐003
International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013 www.seipub.org/ijepr
161
Results and Discussion
Effect of Machining Parameters on MRR
Metal removal rate in the WEDM process is a vital and
significant factor due to its effect on the industrial
economy. Based on the RSM model, Fig. 2 shows the
effect of feeding speed on MRR at various duty factors,
while keeping the other parameters at centre level.
Figure 2 reflects that MRR increases as the feeding
speed of the wire increases. This result has been
attributed to the increase of the consumed applied
current which leads to the increase in the rate of the
heat energy and, hence, in the rate of melting and
evaporation (El‐Taweel, 2006). Furthermore, Fig. 2
shows that MRR also increases with the increase of the
duty factor. This trend is due to the fact that the
increase of the duty factor means the application of the
same heating temp erature for a long time on the
machined surface. In addition to that, Fig. 2 shows that
the effect of the duty factor is more pronounced than
the feeding speed. This phenomenon reflects that the
increase in peak current, as a function to the increase
of the feeding speed, leads to arcing, which relatively
limits the discharge number and machining efficiency.
1.7
2.5
3.3
4.1
4.9
5.0
7.5
10.0
12.5
15.0
0.034
0.044
0.055
0.066
0.076
MR
R, g
/min
Feeding speed, mm/min Wire speed, m/s
FIG. 2 EFFECT OF FEEDING SPEED ON MRR AT VARIOUS DUTY
FACTORS(T=1600 gf, Ws=10 m/s, P=2 MPa, working cond. Table 2)
Figure 3 shows the effect of feeding speed on MRR at
various wire speeds, and it is reflected that wire speed
generally has almost no effect on the MRR. However,
if the wire speed is reduced to a certain limit (5 m/s),
the cutting process becomes non‐uniform and the wire
electrode breaks down several times because of the
increase of the number of discharge per unit length of
wire. This result has also been reported by Kinoshita
(1976) who deduced that the wire winding speed had
almost no effect on the increase of the feeding rate.
Figure 4 shows the effect of feeding speed on MRR at
various wire tensions, and that the wire tension has a
limited effect on the MRR value at the center values of
feeding speed and the other machining parameters.
This figure also reveals that the combination of higher
feeding speed and higher wire tension leads to larger
MRR. This phenomenon could be attributed to the
reduction of the wire oscillation which usually
decreases the dynamic conditions and, consequently,
enhances the stability of the process. This explanation
has also been reported by Yan and Pin (2004) during
their attempt to control the wire tension in the wire
EDM process.
1.7
2.5
3.3
4.1
4.9
800
1200
1600
2000
2400
0.021
0.041
0.060
0.080
0.100
MR
R, g
/min
Feeding speed, mm/min Wire tension, gf
FIG. 3 EFFECT OF FEEDING SPEED ON MRR AT VARIOUS WIRE
SPEEDS(D= 0.3, T=1600 gf, P=2 MPa, working cond. Table 2)
1.7
2.5
3.3
4.1
4.9
0.1
0.2
0.3
0.4
0.5
0.005
0.027
0.049
0.072
0.094
MR
R, g
/min
Feeding speed, mm/min Duty Factor
FIG. 4 EFFECT OF FEEDING SPEED ON MRR AT VARIOUS WIRE
TENSIONS(D= 0.3, Ws=10 m/s, P=2 MPa, working cond. Table 2)
Figure 5 shows the effect of feeding speed on MRR at
various water pressures,as well as the nature of the
variation of the MRR value at different water
pressures ranging from 1 to 3 MPa. A limited effect
has been observed of water pressure at the low
feeding speed. However, this effect was significant
and effective at the higher values of the higher feeding
speeds. This result has been attributed to the fact that
the increase in feeding speed usually leads to an
increase in the rate of heat energy and the rate of
melting and evaporation. So, it is very necessary of the
increase of water pressure to decrease the tendency of
arcing and also to overcome the problem of the
www.seipub.org/ijepr International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013
162
increase in the number of gas bubbles and the bigger
volume of the molten metal (Luo, 1999).
1.7
2.5
3.3
4.1
4.9
1.0
1.5
2.0
2.5
3.0
0.039
0.050
0.061
0.072
0.083
MR
R, g
/min
Feeding speed, mm/min Water pressure, MPa
FIG. 5 EFFECT OF FEEDING SPEED ON MRR AT VARIOUS
WATER PRESSURES(D= 0.3, Ws= 10 m/s, T=1600 gf, working cond.
Table 2)
1.7
2.5
3.3
4.1
4.9
0.1
0.2
0.3
0.4
0.5
0.019
0.030
0.040
0.051
0.062
TW
R, g
/min
Feeding speed, mm/min Duty Factor
FIG. 6 EFFECT OF FEEDING SPEED ON TWR AT VARIOUS DUTY
FACTORS(T= 1600 gf, Ws= 10 m/s, P= 2 MPa, working cond. Table 2)
Effect of Machining Parameters on TWR
Tool wear rate in the WEDM process is a vital and
significant factor due to its effect on the industrial
economy of the machining process. It has also been
stated that it is impossible to achieve a higher MRR
and good surface phenomena simultaneously (Luo,
1999). The decrease of TWR is due to the drop of the
produced spark discharge energy at these conditions.
Figure 6 showing the effect of feeding speed on the
TWR at various duty factors indicates that the
minimum TWR has been obtained at the combination
of low feeding speed and low duty factor; while
Figure 7 showing the effect of feeding speed on the
TWR at various wire speeds indicates that wire
tension around 2000 gf and feeding speed about 3
mm/min represent the optimum TWR at certain wire
speed, wire tension and water pressure. The trends of
the above figure were nearly similar to the trend in Fig.
8. Furthermore, the optimum conditions of each figure
have been clarified through the 3‐D graphs. Figure 6
showing the effect of duty factor on the TWR at
various wire speeds, displays that min TWR has been
obtained at the combination of low Duty factor and
moderate values of wire speed. The decrease of TWR
is due to the drop of the length of the spark discharge
energy at these conditions.
1.7
2.5
3.3
4.1
4.9
5.0
7.5
10.0
12.5
15.0
0.025
0.035
0.045
0.055
0.065
TW
R, g
/min
Feeding speed, mm/min Wire speed, m/s
FIG. 7 EFFECT OF FEEDING SPEED ON TWR AT VARIOUS WIRE
SPEEDS(T=1600 gf, D= 0.3, P= 2 MPa, working cond. Table 2)
1.7
2.5
3.3
4.1
4.9
800
1200
1600
2000
2400
0.024
0.032
0.040
0.048
0.056
TW
R, g
/min
Feeding speed, mm/min Wire tension, gf
FIG. 8 EFFECT OF FEEDING SPEED ON TWR AT VARIOUS WIRE
TENSIONS(D= 0.3, Ws=10 m/s, P= 2 MPa, working cond. Table 2)
1.7
2.5
3.3
4.1
4.9
1.0
1.5
2.0
2.5
3.0
0.024
0.034
0.044
0.054
0.064
TW
R, g
/min
Feeding speed, mm/min Water pressure, MPa
FIG. 9 EFFECT OF FEEDING SPEED ON TWR AT VARIOUS
WATER PRESSURES (D= 0.3,T= 1600 gf, Ws= 10 m/s, working cond.
Table 2)
Figure 7 showing the effect of wire speed on the TWR
at various wire tensions indicates that the min. TWR
International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013 www.seipub.org/ijepr
163
has been achieved at wire tension of 1600 gf, wire
speed of 12.5 m/s and at water pressure of 2.5 MPa. In
Figs. (7‐9), it has also been observed that the increase
of wire speed, wire tension and water pressure leads
to the reduction of the TWR. This result is due to the
improvement of the flushing conditions and the
stability of the process. After wire speed, wire tension
and water pressure exceeded 10 m/s, 2000 gf and 2.5
MPa, respectively, it has been noticed that TWR starts
to increase, which consequently, implies the presence
of arcing. Obviously, these conditions may lead to
vigorous sparking on the wire surface and the
propagation of wear at the wire surface. It has also
been reported that wire tension and spark pressure are
the two major causes for wire rupture (Luo, 1999).
Effect of Machining Parameters on SR
Surface finish of the machined surface plays an
important role in production, and it becomes more
desirable so as to produce a better surface when
hardened materials are machined, requiring no
subsequent polishing. Surface finish is also important
in case of tools and dies for molding as well as
drawing operations. Surface roughness and
dimensional accuracy of a spark eroded work material
depend on pulse energy, water pressure, wire tension
and wire speed. Furthermore, it could be extended to
include wire polarity, wire material, work piece
material and work piece thickness.
Figures (10‐12) show the relationship between duty
factor and both wire speed and wire tension. The
lowest value of surface roughness has been achieved
at low duty factor, lowest on‐time, lowest pulse
energy, and the higher values of wire tension and wire
speed. The value of surface roughness (Ra) has reached
1.45 μm. It should be noted that wire vibration usually
becomes more serious as wire tension decreases. This
result infers that a fine surface can be achieved by
using stable and large wire tension and higher wire
speed values. This trend is consistent with the same
trend which has been obtained by Liao et al. (1997).
Figure 10 shows that the best parametric combination
to get the optimum surface roughness (less than 2 μm)
has been achieved at low duty factor and low feeding
speed. It should be noted that the reduction of on‐time
decreases the spark energy. It is spark energy that
controls the depth of the discharge pits, which directly
relates to the surface finish. However, the off‐time is
the pause between discharge that allows the debris to
solidify and be flushed away by the dielectric prior to
the next discharge. So, it has been avoided to increase
off‐time that can dramatically increase the cutting
speed, by allowing more productive discharges per
unit of time. Furthermore, reducing off‐time can
overload the wire, causing wire breaks and instability
of the cut by not allowing enough time to evacuate the
debris before the next discharge.
1.7
2.5
3.3
4.1
4.9
0.1
0.2
0.3
0.4
0.5
1.70
2.15
2.60
3.05
3.50
SR
, µ
m
Feeding speed, mm/min Duty Factor
FIG. 10 EFFECT OF FEEDING SPEED ON SR AT A VARIOUS
DUTY FACTORS(T=1600 gf, Ws=10 m/s, P=2 MPa, working cond.
Table 2)
1.7
2.5
3.3
4.1
4.9
5.0
7.5
10.0
12.5
15.0
2.20
2.63
3.05
3.48
3.90
SR
, µ
m
Feeding speed, mm/min Wire speed, m/s
FIG. 11 EFFECT OF FEEDING SPEED ON SR AT VARIOUS WIRE
SPEEDS(T= 1600 gf, D= 0.3, P=2 MPa, Working cond. Table 2)
1.7
2.5
3.3
4.1
4.9
800
1200
1600
2000
2400
2.20
2.55
2.90
3.25
3.60
SR
, µ
m
Feeding speed, mm/min Wire tension, gf
FIG. 12 EFFECT OF DUTY FACTOR ON SR AT VARIOUS WIRE
TENSIONS(D= 0.3, Ws= 10 m/s, P= 2 MPa, working cond. Table 2)
It is in Figure 13 anticipated that the optimum
relationship between water pressure and feeding speed
www.seipub.org/ijepr International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013
164
has been obtained at a water pressure of 2 MPa and
feeding speed about 2.5 mm/min (low pulse energy
and moderate water pressure). This phenomenon
reflects the stability of the WEDM action at these
conditions. The results obtained in the present work
about surface roughness values are similar to, or less
than, those reported by Gokler and Ozanogu (2000).
1.7
2.5
3.3
4.1
4.9
1.0
1.5
2.0
2.5
3.0
2.20
2.68
3.15
3.63
4.10
SR
, µ
m
Feeding speed, mm/min Water pressure, MPa
FIG.13 EFFECT OF DUTY FACTOR ON SR AT VARIOUS WATER
PRESSURES(D= 0.3, T=1600 gf, Ws= 10 m/s, working cond. Table 2)
Investigation of Surface Modification
In the present study, the effects of the EDM process on
the surface generated were studied using SEM
microscope. Figure 14a shows that surfaces produced
at higher feed rates have a characteristically matte
appearance comparing microscopic craters. The craters
are partially superimposed, as pools of molten metal
have been ejected from a series of overlapping craters
as shown in Fig. 14a. Figure 14b shows a SEM
micrograph of EDMed surface at low feeding speed of
1.7 mm/min. It can be observed that the actual eroding
pattern and overlapping of craters in case of 4.9
mm/min (Fig. 14‐b) are much wider and deeper than
that in the case of 1.7 mm/min., as well shows an
overlapping of the re‐solidified layerswhich have been
attributed to the effect of the partially superimposed
adjacent discharges [30]. This is due to the large
amount of heat energy liberated as the feeding speed
increases.
Figure 15 (a, b) shows SEM micrographs comparison
of EDMed surface at low duty cycle of 10% and high
duty cycle of 50%. It can be observed that the surface
is relatively irregular and also shows some turbulence
at a duty cycle of 50%. The eroded surface is nearly
free of pock marks. In fact, bubbles of entrapped gas
come from the gaseous phase which has reached the
surface. Furthermore, due to the effect of the long
pulse duration, the gas bubbles grow larger, causing
the larger bock marks at the surface and great
turbulence beneath it. At higher magnification of X750,
a crack has been observed and propagated for duty
factor equal to 50%. It is also clear that spark crater is
usually associated with fusion and plastic deformation.
a ‐ Fs= 4.9 mm/min
b‐ Fs= 1.7 mm/min
FIG. 14 SEM MICROGRAPHS OF WEDM SURFACE (X750)
(D= 0.3, T= 1600 gf, P= 2 MPa, Ws= 10 m/s, working cond. Table 2)
a ‐ D = 0.1
b ‐ D = 0.5
FIG. 15 SEM MICROGRAPHS OF WEDM SURFACE (X750)(Fs = 3.3
mm/min, T=1600 gf, P= 2 MPa, Ws=10 m/s, working cond. Table 2)
Figure 16 (a, b) shows SEM micrographs of CK45
EDMed surface at lower and upper wire speed. It has
b
a
a
b
International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013 www.seipub.org/ijepr
165
been observed that the increase of the wire speed
could lead to the increase of pock marks on surfaces
machined at 15 m/s. On the other hand, the EDMed
surface at 5 m/s produced shallow craters and less re‐
solidified material around the crater base.
a ‐ Ws = 5 m/s
b‐ Ws = 15 m/s
FIG. 16 SEM MICROGRAPHS OF WEDM SURFACE (X150)
(Fs= 3.3 mm/min, D= 0.3, P= 2 MPa, T=1600 gf,working cond. Table 2)
Figure 17 shows a higher magnification for a single
spark crater on surface, to observe more details of the
crater. Tosun et al. (2003) reported that increasing the
wire speed increases both the depth and the diameter
of crater. They have estimated the contribution of this
factor by 4%. It is believed that the cause of this
condition is due to the poor flushing condition of the
water in the gap at the higher speed of the wire.
Furthermore, it could be also related to the instability
of the process at these conditions.
FIG. 17 SEM MICROGRAPH OF WEDM SURFACE (X350)
(Ws=15 m/s, Fs= 3.3 mm/min, D= 0.3, T= 1600 gf, P= 2 MPa, working
cond. Table 2)
Figure 18 (a, b) shows SEM photomicrographs of
EDMed surface in order to assess the effect of water
pressure on the resultant surface appearance. Fusion
and plastic deformation, cracking, solidified deposits,
and micro droplets were seen in the above figures.
However, the intensity of those characteristics
decreases as the water pressure increases. The surface
characteristics show a significant improvement. These
results are consistent with those obtained in MRR and
SR sections. This result can be explained by the cooling
effect of increasing the flushing pressure on the wire
surface. The higher heat transfer rate from the crater
zone and the body results in smaller molten craters.
a ‐ P=1 MPa
b ‐ P= 3 MPa
FIG. 18 SEM MICROGRAPHS OF WEDM SURFACE (X750)
(Fs=3.3 mm/min, D=0.3, T=1600 gf, Ws=10 m/s, working cond. Table 2)
Recast layer greatly affects fatigue strength and
shortens service life of WEDM‐ed component.
Damaged surface often removed in a post machining
process, greatly increases fabricating time and cost.
The occurrence of the white layer depends on two
main factors, namely, the initial carbon content of the
workpiece and the type of dielectric fluid used. If these
two conditions are met, then the resulting thickness of
the white layer will depend upon the magnitude of the
pulse energy. Also, the hardness is still higher than
that of the base material, because of the fine grain
structure resulting from high cooling and nucleation
rates (Tsai et al., 2003).
Optimization of the Process
In the present study, three responses i.e. MRR, TWR
a
b
b
a
www.seipub.org/ijepr International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013
166
and SR have been optimized simultaneously using the
developed models, i.e. Eqs. (4‐6) based on composite
desirability optimization technique [(Castillo et al.,
1996), (Derringer and Suich, 1980), (El‐Taweel, 2009)].
In response to optimization, a measure of how the
solution has satisfied the combined goals for all
responses must be assured. The optimization is
accomplished by:
Obtaining the individual desirability (d) for
each response.
Combining the individual desirabilities to
obtain the composite desirability (D).
Maximizing the composite desirability and
identifying the optimal input variable settings.
If it is desirable to maximize a response, the individual
desirability is calculated as:
di = 0 i < Li
di = (i ‐ Li)/( Ti ‐ Li)ri Li < i < (7)
di = 1 i > Ti
If it is desirable to minimize a response, the individual
desirability is calculated as:
di = 0 i > Hi
di = (Hi ‐ i)/(Hi ‐ Ti)ri Ti < i < Hi (8)
di = 1 i < Ti
If it is desirable to target a response, the individual
desirability is calculated as:
di = (i ‐ Li))/(Ti ‐ Li)ri Li < i < Ti (9)
di = (Hi ‐ i)/(Hi ‐ Ti)ri Ti < i < Hi (10)
di = 0 i < Li
di = 0 i > Hi
where; i = predicted value of ith response, Ti = target
value for ith response, Li = lowest acceptable value for
ith response, Hi = highest acceptable value for ith
response, di = desirability for ith response, D =
composite desirability, ri = weight of desirability
function of ith response, wi = importance of ith response
and W = wi
If the importance is the same for each response, the
composite desirability is:
D = (d1d2… dn)1/n = (diwi)1/W (11) where n = number of responses. To reflect the possible
difference in the importance of different responses,
where the weight wi satisfies 0< wi <1 and w1+ w2+
w3+ ……+ wn = 1
The optimality solution is to evaluate the input
parameters in experiment range for maximizing MRR,
minimizing TWR and SR through the MINITAB‐15
software. Minitab determines optimal settings for
input variables by maximizing the composite
desirability. The importance and the weight of
desirability function of for each response is taken as
the same.
The numeric values of the optimum conditions are
presented in Tables 8. Figure 21 represents the
optimized graphs of the three responses (MRR, TWR
and SR) and also the optimization results. The vertical
lines inside the cells represent current optimal
parametric settings, and the horizontal dotted lines
represent the current response values. The value of
composite desirability (D) is taken as 0. 7466.
FIG. 19 SEM MICROGRAPH OF THE RECAST LAYER (X750)
(Ws=15 m/s, Fs= 3.3 mm/min, D= 0.3, T= 1600 gf, P= 2 MPa, working
cond. Table 2)
Position [? Theta] (Copper (Cu))
20 30 40 50 60 70 80 90
Counts
0
50
100
150
Sample No.18
a‐ P = 3 MPa
a
Base metal
Recast layer
International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013 www.seipub.org/ijepr
167
b‐ P = 1 MPa
FIG. 20 X‐RAY DIFFRACTION ANALYSIS OF THE RECAST LAYER SURFACE
(Fs= 3.3 mm/min, D= 0.3 mm/min, T= 1600 gf, Ws= 10 m/s, working cond. Table 2)
FIG. 21 MULTI‐RESPONSE OPTIMIZATION RESULTS FOR MAXIMUM MRR, MINIMUM TWR AND SR
Confirmation Experiment
Conducting the experiment is a crucial final step of the
optimization results with the purpose to confirm that
the optimum conditions suggested by the optimization
process do indeed give the projected improvement, as
well to validate the accuracy of the proposed RSM
models. Conducting tests with the optimal levels
combination of the cutting parameters concluded
previously entails the confirmation experiments. The
predicted values of the confirmation tests using Eqs.
4–6 and the experimental observations at the optimum
levels of the process parameters are shown in Table 9.
It is observed that the predictions based on the
purposed regression models are quite close to the
experimental observation. The error between the
experimental results with the optimum settings and
the predicted values for MRR, TWR and SR lies within
3%, 6.6%, and 2.6%, respectively. Clearly, this confirms
the excellent reproducibility of the experimental
conclusions.
TABLE 8 CONSTRAINTS OF PARAMETERS AND OPTIMUM VALUES
Parameter Constrains Optimum value
Feeding speed (Fs), mm/min 1.7 ‐ 4.9 3.43
Duty factor (D) 0.1‐ 0.5 0.33
Wire tension (T), gf 800 ‐ 2400 1646.39
Wire speed (Ws), m/s 5 ‐15 11.28
Water pressure (P), MPa 1‐3 2.19
Hi
Lo 0.7466
D
Optimal
Cur
d = 0.77739
Minimum
SR
d = 0.89123
Minimum
TWR
d = 0.47759
Maximum
MRR
y = 2.4452
y = 0.0270
y = 0.0656
1
3
0.1
0.5
1.7
4.9 D P Fs
3.43 0.33 2.19
800
2400T
1646.3
5
15
Ws
11.28
b
www.seipub.org/ijepr International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013
168
TABLE 9 PREDICTED AND RESULTS OF THE CONFIRMATION EXPERIMENTS
Response Goal Predicted Experimental Error %
MRR, g/min Maximize 0.072 0.0698 3
TWR, g/min Minimize 0.03 0.032 6.6
SR, μm Minimize 2.30 2.36 2.6
Conclusions
The results concluded from the present work can be
summarized as follows:
1. The WEDM process has proved its adequacy to
machine CK45 steel material under acceptable
metal removal rate and fine surface finish (Ra)
less than 1.5 μm.
2. The metal removal rate generally increases
with the increase of feeding speed value, duty
factor and water pressure. Further, the effect of
wire tension and wire speed on metal removal
rate is limited.
3. Lower tool wear rate has been obtained at the
parametric combination of lower feeding speed
and lower duty factor. It has also been found
that the increase of water pressure, wire
tension and wire speed decreases tool wear.
This trend has been valid up to the generation
of arcing.
4. Fine surface quality has been obtained at lower
feeding speed, lower duty factor and higher
water pressure.
5. Microscopic investigations of the specimens
have revealed that the intensity of pulse energy
is an efficient factor in controlling surface
texture. Plastic deformation, deep craters, and
cracks have been observed at a higher feeding
speed and higher duty factor.
6. X‐ray diffraction technique has explored the
presence of some deposited wire material on a
workpiece surface under certain conditions.
These conditions exist at lower water pressure
equals 1 MPa.
7. An optimization study has also been submitted
to get maximum MRR or minimum TWR or
minimum SR under the different working
conditions.
Finally, the present work facilitates a guideline for
optimum selection of the machining parameters to the
production engineer in WEDM process of CK45 steel.
REFERENCES
Antony J. ʺDesign of experiments for engineers and
scientists.ʺ Linacre House, Jordan Hill, Oxford OX2 8DP,
2003.
Bojorquez B, Marloth R.T, Es‐Said O.S. ʺFormation of a crater
in the workpiece on an electrical discharge machine.ʺ
Engineering Failure Analysis, 9 (2002): 93‐97.
Chiang K.T, Chang F.P, Tsai D.C. ʺModeling and analysis of
the resolidified layer of SG cast iron in the EDM process
through response surface methodology.ʺ J. Mater.
Process. Technol,182 (2007): 525‐533.
Castillo E, Montgomery D, McCarville D. ʺModified
desirability functions for multiple response optimization.ʺ J.
of Quality Technl, 28 (1996): 337‐345.
Derringer G, Suich R. ʺSimultaneous optimization of several
response variables.ʺ J. of Quality Techanl, 12 (1980): 214‐
219.
El‐Taweel T.A. ʺParametric study and optimization of wire
electrical discharge machining of Al‐Cu‐TiC‐Si P/M
composite.ʺ Int. J. Machining and Machinability of
Materials (IJMMM), Vol. 1, No. 4 (2006): 380‐395.
El‐Taweel T. ʺMulti‐response optimization of EDM with Al–
Cu–Si–TiC P/M composite electrode.ʺ Int J Adv Manuf
Technol, 44 (2009): 100‐113.
Gokler M.I, Ozanozgu A.M. ʺExperimental investigation of
effects of cutting parameters on surface roughness in
WEDM process.ʺ Int. J. Machine Tools Manufac, 40
(2000): 1831‐1848.
Hasçalýk A, Caydas V. ʺExperimental study of wire
electrical discharge machining of AISI D5 tool steel.ʺ J.
Mater. Process. Technol, 148 (2004): 362‐367.
Hewidy M.S, El‐Taweel T.A, El‐Safty M.F. ʺModeling the
machining parameters of wire electrical discharge
machining of Inconel 601 using RSM.ʺ J Mater Process.
Technol 169 (2005): 328‐336.
Ho K.H., Newman S.T. ʺState of the art electrical discharge
machining (EDM).ʺ Int. J. Machine Tools Manufac, 43
(2003): 1287‐1300.
Ho K.H, Newman S.T, Rahimifard S, Allen R.D. ʺState of the
art in wire electrical discharge machining (WEDM)ʺ Int. J.
Machine Tools Manufac, 44 (2004): 1247‐1259.
Jeswani M.L. ʺDimensional analysis of tool wear in electrical
discharge machining.ʺ Wear 55 (1979): 153‐161.
Kanlayasiri K., Boonumung S. ʺAn investigation on effects of
wire EDM machining parameters on surface roughness
of newly developed DC 53 die steel.ʺ J. Mater. Processing
International Journal of Engineering Practical Research (IJEPR) Volume 2 Issue 4, November 2013 www.seipub.org/ijepr
169
Technol, 187‐188 (2007): 26‐29.
Kinoshita N, Fukui M, Shichida H, Gamo G. ʺStudy on EDM
with wire electrode; Gap Phenomena.ʺ Annals of CIRP
25/1, (1976).
Ko‐Ta Chiang, Fu‐Ping Chang, ʺOptimization of the WEDM
process of particle‐reinforced material with multiple
performance characteristics using grey relational
analysis.ʺ J. Mater. Processing Technol, Vol. 180, Issues 1‐
3, 2006, 96‐101.
Liao Y.S, , Huang J.T, Sv H.C. ʺA study on the machining
parameters optimization of wire electrical discharge
machining.ʺ J. Mater. Process. Technol, 71(1997): 487‐493.
Luo Y.F. ʺRupture failure and mechanical strength of the
electrode wire used in wire EDM.ʺ J. Mater. Process.
Technol, 94 (1999): 208‐215.
Montgomery D.C. ʺDesign and analysis of experiments.ʺ
Wiley, New York, 2001.
Norliana M. A, Darius G. S, Fuad B. ʺA review on current
research trends in electrical discharge machining
(EDM).ʺ Int. J. Machine Tools Manufac, 47 (2007): 1214‐
1228.
Puertas C.J, Luis. ʺA study on the machining parameters
optimization of electrical discharge machining.ʺ J. Mater.
Process. Technol. 143 (2003): 521‐526.
Puri A.B, Bhattacharyya B. ʺModeling and analysis of white
layer depth in a wire‐cut EDM process through response
surface methodology.ʺ Int J Adv Manuf Technol, 25
(2005): 301‐307.
Qin G.W, Oikawaa K, Smithb G.D.W, Hao S.M. ʺWire
electric discharge machining induced titanium hydride
in Ti–46Al–2Cr alloy.ʺ Intermetallics 11 (2003): 907‐910.
Sharif S, Noordin R.M. ʺMachinability modeling in powder
mixed dielectric EDM of titanium alloy Ti‐6246.ʺ
Proceedings of the First International Conference and
Seventh AUN/SEED‐Net Fieldwise Seminar on
Manufacturing and Material Processing, Kuala Lumpur,
(2006): 133‐138.
Snoeys R, Staelens F, , Dekeyser W. ʺCurrent trends in non‐
conventional material removal processes.” Annals of
CIRP, Vol. 35/2 (1986): 467‐480.
Spedding T, Wang Z. ʺParametric optimization and surface
characterization of wire electrical discharge machining
process.ʺ Precision Engineering, 20 (1997): 5‐15.
Tsai K.M, Wang P.J. ʺSemi‐empirical model of surface finish
on electrical discharge machining.ʺ Int. J. Machine Tools
Manufac, 41 (2001): 1455‐1477.
Yahya A, Manning C.D. ʺDetermination of material removal
rate of an electro‐discharge machine using dimensional
analysis.ʺ Journal of Physics D: Applied Physics 37 (2004):
1467‐1471.
Yan M.T, Pin. ʺAccuracy improvement of wire‐EDM by real
time wire tension control.ʺ, Int. J. Machine Tools
Manufac, (2004): 807‐814.
Taha Ali El‐Taweel
He was born in El‐Gharbia, Egypt in
1956. He received his B.Sc. and the M. Sc.
degrees in in the field of non‐traditional
machining from Menoufiya University,
Egypt in 1979 and 1985 respectively. He
joined the department of production
engineering and mechanical design,
faculty of engineering, Menoufiya University, Egypt in 1979
to 1989 as a demonstrator and assistant lecturer; then he
received his Ph.D. degree in production engineering, from
Gorgian Technical University in 1992, Gorgian Republic,
USSR. From 1992, he joined the department of production
engineering and mechanical design, Menoufiya University,
as an Associate Professor in 1998. He joined as an Professor
in 2008 and head of the department of production
engineering and mechanical design, Menoufiya University
in 2011 till now. He has published many articles in
international journals and many books. His current research
interests are traditional, non‐traditional machining,
optimization teqniques and tribology of composite materials.