SlideShare a Scribd company logo
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 727
STUDY OF SURFACE ROUGHNESS FOR DISCONTINUOUS
ULTRASONIC VIBRATION ASSISTED ELECTRIC DISCHARGE
MACHINING USING RSM
Jujhar Singh1
, Sukhvir Singh2
, Gurpreet Singh3
1, 2, 3
Mechanical Engineering Department, Rayat-Bahra Institute of Engineering & Bio-Technology,
Kharar, Distt. Mohali, Punjab, India
jujharsingh2085@gmail.com, sukhvir.1991@gmail.com, hanspal89@gmail.com
Abstract
The objective of this paper is to study the effects of response parameters on the performance characteristics in the Ultrasonic
vibration Assisted Electric Discharge Machining (UEDM) Process. Response Surface Methodology (RSM) is used to investigate the
effect of amplitude of vibration, peak current, pulse on-time, machining time and flushing pressure. To study the proposed second
order polynomial model for surface roughness (SR), a Central Composite Design (CCD) is used for the estimation of the model
coefficients of five factors, which are believed to influence the SR in UEDM process. Experiments are conducted on Aluminum alloy
6063 with copper electrode. The response is modeled on experimental data by using RSM. The separable influence of individual
machining parameters and the interaction between these parameters are also investigated by using analysis of variance (ANOVA). It
is found that amplitude of vibration, peak current; pulse-on time, flushing pressure and most of their interactions have significant
affect on SR.
Keywords: Central Composite Design, SR, UEDM, RSM, Aluminum alloy (Al6063)
-----------------------------------------------------------------------***---------------------------------------------------------------------
1. INTRODUCTION
In today's world, there is an essential requirement of advance
materials such as high strength alloys, ceramics, fiber-
reinforced composites etc. These materials are very hard in
nature and are difficult to machine. To get rid from this
machining problem, Electric Discharge Machining (EDM)
was invented. EDM works on the principle of spark erosion,
which removes the material by eroding the work piece. The
main equipments of the Electric Discharge Machine are D.C.
supply unit, EDM circuit, servomechanism, dielectric unit etc.
There is a gap between the tool (electrode) and the work piece,
which is known as spark gap. To complete the circuit the
current is passed through the tool and the work piece through
dielectric fluid. The current tends to break the dielectric fluid
into ions, which start moving from work piece to tool. The
movement of ions and electrons between tool and work piece
occurs at such a high speed that it seems as a spark. This
transfer of ions and electrons increases the temperature, which
melts the work piece. The spark melts a small material volume
on each of the electrodes. The dielectric fluid that fills the gap
between the electrodes removes part of this material.
The circulation of dielectric fluid and the removal of
machined-debris is very difficult, especially when the hole or
the cavity becomes deep, which reduces the machining
efficiency due to poor circulation of the dielectric fluid from
working gap. This poor flushing ends up with stagnation of
dielectric and builds-up machining residues which apart from
low material removal rate (MRR) also lead to short circuits
and arcs. To improve the machining performance of EDM, a
combined method of ultrasonic vibration and Electric
Discharge Machining has been developed by some
researchers. Response-surface methodology comprises a body
of methods for exploring for optimum operating conditions
through experimental methods. Typically, this involves doing
several experiments, using the results of one experiment to
provide direction for what to do next. This next action could
be to focus the experiment around a different set of conditions,
or to collect more data in the current experimental region in
order to fit a higher-order model [1]. Shabgard et al. concluded
that the regression technique is an important tool for
representing the relation between machining characteristic and
EDM process input parameters. The results show, that the
CCD is a powerful tool for providing experimental diagrams
and statistical-mathematical models, to perform the
experiments appropriately and economically [2]. Asif and
khan used RSM to investigate the relationships and parametric
interactions between three controllable variables on the
material removal rate(MRR), electrode wear rate (EWR) and
SR in EDM milling of AISI 304 steel. Developed models can
be used to get the desired responses within the experimental
IJRET: International Journal of Research in Engineering and Technology
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @
range[3]. Sahoo et al. demonstrated the effect of most
influencing parameters on SR using RSM for different work
piece materials in EDM [4]. Kanagarajan et al. developed
models for the MRR and SR over the most influencing process
parameters in EDM of WC/30% Co composites. The RSM
methodology is used to identify the most influential
parameters for maximizing metal removal rate and for
minimizing the SR[5]. Shabgard and Shotorbani suggested
mathematical models for relating the MRR, TWR and
machining parameters. RSM approach is used to determine the
relationship between various process parameters and
machining criteria of FW4 welded steel[6]. Pradhan et al. used
RSM method to investigate the effect of input p
SR in EDM of AISI D2 tool steel. It was found that the
developed models can be used effectively in prediction of
responses. In this work, mathematical models have been
developed for relating the SR to machining parameters like
discharge current, pulse-on time and pulse
varied over wide range from roughing region to nearly
finishing conditions[7]. Kuppan at el. derived mathematical
model for MRR and average SR of deep hole drilling of
Inconel 718. The experiments were planned usin
RSM was used to model the same. It revealed that MRR is
more influenced by peak current and duty factor, and the
parameters were optimized for maximum MRR with the
desired SR value using desirability function approach [8].
Jaharah et al. investigated the machining performance such as
SR, electrode wear rate and MRR with copper electrode and
AISI H3 tool steel work piece and the input parameters taken
are Ip, Ton, and Toff. The optimum condition for SR was
obtained at low Ip, low Ton, and Toff and concluded that the
Ip was the major factor effecting both the responses, MRR and
SR [9]. Chiang had explained the influences of Ip, Ton, duty
factor and voltage on the responses; MRR, electrodes wear
ratio, and SR. The experiments were planned according t
CCD and the influence of parameters and their interactions
were investigated using ANOVA. A mathematical model was
developed and claimed to fit and predict MRR accurately with
a 95% confidence. Results show that the main two significant
factors affecting the response are the Ip and the duty
factor[10].
Literature reviewed shows that, the two
factors on the value of the SR are the discharge current and
the duty factor. Statistical models have been developed using
RSM based on experimental results considering the machining
parameters, viz., amplitude of vibration (A, µm), peak current
(IP, A), pulse-on time (Ton, μs), machining time (Mt, min.)
and flushing pressure (Pf, kgf/cm2) as independent variables.
Finally, an attempt has been made to obtain optimum
machining conditions with respect to SR parameter considered
in the present study with the help of response optimization
technique.
IJRET: International Journal of Research in Engineering and Technology eISSN:
__________________________________________________________________________________________
2013, Available @ http://www.ijret.org
range[3]. Sahoo et al. demonstrated the effect of most
for different work
piece materials in EDM [4]. Kanagarajan et al. developed
influencing process
parameters in EDM of WC/30% Co composites. The RSM
methodology is used to identify the most influential
parameters for maximizing metal removal rate and for
[5]. Shabgard and Shotorbani suggested
, TWR and SR to
approach is used to determine the
relationship between various process parameters and
machining criteria of FW4 welded steel[6]. Pradhan et al. used
RSM method to investigate the effect of input parameters on
SR in EDM of AISI D2 tool steel. It was found that the
developed models can be used effectively in prediction of
responses. In this work, mathematical models have been
developed for relating the SR to machining parameters like
on time and pulse-off time which
varied over wide range from roughing region to nearly
finishing conditions[7]. Kuppan at el. derived mathematical
model for MRR and average SR of deep hole drilling of
Inconel 718. The experiments were planned using CCD and
RSM was used to model the same. It revealed that MRR is
more influenced by peak current and duty factor, and the
parameters were optimized for maximum MRR with the
desired SR value using desirability function approach [8].
gated the machining performance such as
SR, electrode wear rate and MRR with copper electrode and
and the input parameters taken
are Ip, Ton, and Toff. The optimum condition for SR was
concluded that the
Ip was the major factor effecting both the responses, MRR and
SR [9]. Chiang had explained the influences of Ip, Ton, duty
factor and voltage on the responses; MRR, electrodes wear
ratio, and SR. The experiments were planned according to a
CCD and the influence of parameters and their interactions
were investigated using ANOVA. A mathematical model was
developed and claimed to fit and predict MRR accurately with
a 95% confidence. Results show that the main two significant
ng the response are the Ip and the duty
main significant
are the discharge current and
the duty factor. Statistical models have been developed using
tal results considering the machining
parameters, viz., amplitude of vibration (A, µm), peak current
s), machining time (Mt, min.)
and flushing pressure (Pf, kgf/cm2) as independent variables.
to obtain optimum
machining conditions with respect to SR parameter considered
in the present study with the help of response optimization
2. EXPERIMENTATION
The method of assisting EDM process with discontinuous
ultrasonic vibrations is designed and developed. The ultrasonic
vibrations of 25000 Hz are achieved through developed Piezo
electric transducer. A piezo
EDM machine to give continuous and discontinuous ultrasonic
vibrations to work piece at frequency of 25000 Hz with
amplitude range of 1 µm to 10 µm.
Fig- 1: Line diagram of UEDM set
An ultrasonic panel is used to control the amplitude and mode
(continuous and discontinuous) of vibration. In case of
discontinuous vibrations, the high frequency electrical impulse
from the generator to the transducer is discontinuous but
interval time is adjusted in terms of frequency
finally frequency is maintai
the schematic diagram of the develo
Figure 2 shows the circuit diagram of discontinuous vibration
through piezoelectric transducer.
study is Aluminum alloy 6063 with cylindrical shape
(Diameter 10mm and Height 30mm). Table 1 shows the
chemical composition of work
Fig- 2: Circuit diagram of discontinuous vibration
eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
728
2. EXPERIMENTATION
The method of assisting EDM process with discontinuous
ultrasonic vibrations is designed and developed. The ultrasonic
vibrations of 25000 Hz are achieved through developed Piezo-
electric transducer. A piezo-electric transducer is attached with
to give continuous and discontinuous ultrasonic
vibrations to work piece at frequency of 25000 Hz with
amplitude range of 1 µm to 10 µm.
Line diagram of UEDM set-up
An ultrasonic panel is used to control the amplitude and mode
nd discontinuous) of vibration. In case of
discontinuous vibrations, the high frequency electrical impulse
from the generator to the transducer is discontinuous but
interval time is adjusted in terms of frequency-adjustment and
finally frequency is maintained at 25000Hz. Figure 1 shows
schematic diagram of the developed UEDM set-up and
Figure 2 shows the circuit diagram of discontinuous vibration
through piezoelectric transducer. The work piece used for
study is Aluminum alloy 6063 with cylindrical shape
(Diameter 10mm and Height 30mm). Table 1 shows the
chemical composition of work-piece used for study.
Circuit diagram of discontinuous vibration
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 729
Table -1: Chemical Composition of Al 6063
Element %
Si 0.3
Fe 0.35
Cu 0.09
Mn 0.1
Mg 0.7
Zn 0.1
Ti 0.1
Cr 0.1
Al 98.16
The experiments are conducted using the commercial Elektra
EMS 5535 model Die Sinking Ram EDM. For each
experiment, a separate Copper electrode (Diameter 5mm and
Height 30mm) has been used.
3. DESIGN OF EXPERIMENTS
The design factors, response variable as well as the
methodology employed for the experimentation is described
below.
3.1 Design Factor
The design factors considered in the present work were
amplitude of vibration, peak current, pulse-on time, machining
time and flushing pressure. The selection of these five factors
has been made because they are the most important and
widely used by researchers in the die sinking EDM field.
3.2 Response Variables
SR is the measure of the fine surface irregularities in the
surface texture. These are the results of the EDM process
employed to create the surface. SR is related as the arithmetic
average deviation of the surface valleys and peaks expressed
in micro-meters.
The parameter mostly used for general SR is SR. It measures
average roughness by comparing all the peaks and valleys to
the mean line, and then averaging them all over the entire cut-
off length. Cut-off length is the length for which the stylus is
dragged across the surface. A longer cut-off length will give a
more average value, and a shorter cut-off length might give a
less accurate result over a shorter stretch of surface. In this
work, the SR is measured by Mitutoyo surftest SJ-400. The
surf test is a shop–floor type SR measuring instrument, which
traces the surface of various machine parts and calculates the
SR based on roughness standards, and displays the results in
μm. The work piece is attached to the detector unit of the SJ-
400 which traces the minute irregularities of the work piece
surface. The vertical stylus displacement during the trace is
processed and digitally displayed on the display of the
instrument. The surf test has a resolution varying from 0.01
μm to 0.4 μm depending on the measurement range. The
roughness values are taken by averaging at least three
measurements per specimen at different locations of
specimens.
3.3 Factorial Design Employed
Experiments were designed on the basis of design of
experiments. The design finally chosen was a factorial design
24 with six central points, consequently carrying out a total of
32 experiments. Based on the CCD, experiments were
conducted to develop empirical models for SR in terms of the
five input variables: amplitude of vibration, peak current,
pulse-on time, machining time and flushing pressure. Each
input variable (factor) was varied over five levels: ±1, 0 and
±α. Table 2 shows the relationship between the machining
parameters and their corresponding selected variation levels,
taking into account the entire range of machine parameters.
The experimental data as shown in Table 2 is utilized to obtain
the relation between the EDM process parameters and the
output SR. RSM utilizes statistical design of experiment
technique and least-square fitting method in the model
generation phase. An equation consisting of values of a
dependent response variable and independent variables is
derived for SR characteristics.
Table -2: Process Parameters and their Levels
S.
No.
Symbol and
Units
Parameters Levels
(-2) (-1) (0) (+1) (+2)
1 A (µm) Amplitude of Vibration 0 2 4 6 8
2 IP (A) Peak Current 5 15 25 35 45
3 Ton (µs) Pulse-on Time 50 100 150 200 250
4 Mt (Min.) Machining Time 20 25 30 35 40
5 Pf (Kgf/cm2) Flushing Pressure 0.2 0.4 0.6 0.8 1.0
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 730
Regression equations are found out using software for
statistical analysis called “Design Expert (DX-8)”. ANOVA
and Fisher’s statistical test (F-test) is performed to check the
adequacy of the model as well as significance of individual
parameters.
4. RESPONSE SURFACE METHDOLOGY
Response surface methodology is a collection of mathematical
and statistical techniques that are useful for modelling and
analysis of problems in which a response of interest is
influenced by several variables and the objective is to optimize
the response. Response Surface Method adopts both
mathematical and statistical techniques [11,12]. RSM helps in
analyzing the influence of the independent variables on a
specific dependent variable (response) by quantifying the
relationships amongst one or more measured responses and
the vital input factors. The first step in RSM is to find a
suitable approximation for the true functional relationship
between response of interest ‘y’ and a set of controllable
variables {x1, x2, ……xn}. Usually when the response
function is not known or non-linear, a second order model is
utilized [13] in the form:
Y=
r
2
2ji
jiij
k
1i
2
iii
k
1i
ii exxbxbxbb ±+++ ∑∑∑ =<==
o
….1
Where, ε represents the noise or error observed in the response
y such that the expected response is (y -ε ) and b’s are the
regression coefficients to be estimated. The least square
technique is being used to fit a model equation containing the
input variables by minimizing the residual error measured by
the sum of square deviations between the actual and estimated
responses. The calculated coefficients or the model equations
however need to be tested for statistical significance and thus
the test is performed.
4.1 Significance Test of the Regression Model
Analysis of Variance (ANOVA) is used to check the adequacy
of the model for the responses in the experimentation. ANOVA
calculates the F-ratio, which is the ratio between the
regression mean square and the mean square error. The F-
ratio, also called the variance ratio, is the ratio of variance due
to the effect of a factor (the model) and variance due to the
error term. This ratio is used to measure the significance of the
model under investigation with respect to the variance of all
the terms included in the error term at the desired significance
level (α). If the calculated value of F-ratio is higher than the
tabulated value of F-ratio for roughness, then the model is
adequate at desired α level to represent the relationship
between machining response and the machining parameters.
5. EXPERIMENTAL RESULTS
Table 3 shows the design matrix developed for the proposed
model as well as the machining characteristics values obtained
in the experiments for SR.
5.1 Model Adequacy Test for SR
The ANOVA and Fisher's statistical test (F- test) were
performed to check the adequacy of the model as well as the
significance of individual parameters. Table 4 shows the pre-
ANOVA model summary statistics for SR. It can be seen that
standard deviation of quadratic model is 0.49, which is
much better as compared with lower order model for R-
squared. Hence the quadratic model suggested is most
appropriate. Table 5 shows the variance analysis results of the
proposed model of SR. The ANOVA Table includes Sum of
Squares (SS), Degrees of Freedom (DF), Mean Square (MS),
F-value and P- value. The MS was obtained by dividing the
SS of each of the sources of variation by the respective DF.
The P-value is the smallest level of significance at which the
data are significant. The F-value is the ratio of MS of the
model terms to the MS of the residual. In this analysis,
insignificant model terms were eliminated to adjust the fitted
mathematical model. As seen from Table 5, the P-values for
developed model of SR is less than 0.0001, which indicates
that model is significant at 92% confidence level.
Table -3: Experimental conditions and results of response characteristics for SR
Std.
Order
Run
order
Factor A Factor B Factor C Factor D Factor E Response
Amplitude of
Vibration
(µm)
Peak
Current
(A)
Pulse-on
time
(µs)
Time of
Machining
(min.)
Flushing
Pressure
(kg/cm2)
Surface
Roughness
(µm)
1 15 2 15 100 25 0.8 7.69
2 32 6 15 100 25 0.4 10.63
3 4 2 35 100 25 0.8 10.1
4 6 6 35 100 25 0.4 10
5 26 2 15 200 25 0.8 10.38
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 731
6 24 6 15 200 25 0.4 11.85
7 9 2 35 200 25 0.8 9.5
8 20 6 35 200 25 0.4 9.42
9 27 2 15 100 35 0.8 10.89
10 25 6 15 100 35 0.4 9.31
11 22 2 35 100 35 0.8 10.9
12 19 6 35 100 35 0.4 9.97
13 29 2 15 200 35 0.8 9.76
14 10 6 15 200 35 0.4 10.71
15 16 2 35 200 35 0.8 10.37
16 17 6 35 200 35 0.4 12.63
17 5 0 25 150 30 0.6 11.47
18 30 8 25 150 30 0.6 12.67
19 12 4 5 150 30 0.6 6.52
20 11 4 45 150 30 0.6 7.02
21 28 4 25 50 30 0.6 10.95
22 23 4 25 250 30 0.6 11.65
23 21 4 25 150 20 0.6 10.35
24 31 4 25 150 40 0.6 11.13
25 7 4 25 150 30 0.2 10.39
26 18 4 25 150 30 1.0 11.67
27 2 4 25 150 30 0.6 9.37
28 13 4 25 150 30 0.6 9.38
29 3 4 25 150 30 0.6 8.45
30 8 4 25 150 30 0.6 9.67
31 1 4 25 150 30 0.6 9.56
32 14 4 25 150 30 0.6 9.78
Table -4: Model summary Statistics for SR
Model summary Statistics
Source Standard
Deviation
R-Squared Adjusted R-
Squared
Predicted
R-Squared
PRESS
Linear 1.45 0.1021 -0.0705 -0.3993 85.13
2FI 1.60 0.3259 -0.3061 -0.5057 91.60
Quadratic 0.49 0.9558 0.8754 0.3309 40.70 Suggested
Cubic 0.54 0.9710 0.8500 -10.1824 680.29 Aliased
Table -5: ANOVA for quadratic model of Surface roughness
Source Sum of
Squares
DOF Mean square F-Value P-Value
Prob> F
Model 55.91 16 3.49 10.65 <0.0001 Significant
A-Amplitude of vibration 2.24 1 2.24 6.82 0.0196
B-Peak Current 0.30 1 0.30 0.91 0.3564
C-Pulse-on Time 1.78 1 1.78 5.42 0.0344
D-Machining Time 1.78 1 1.78 5.42 0.0344
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 732
E-Flushing Pressure 0.12 1 0.12 0.38 0.5468
AD 0.78 1 0.78 2.37 0.1442
AE 3.02 1 3.02 9.20 0.0084
BD 1.40 1 1.40 4.26 0.0567
BE 3.21 1 3.21 9.79 0.0069
CE 2.68 1 2.68 8.17 0.0120
DE 0.29 1 0.29 0.88 0.3629
A2 10.79 1 10.79 32.90 <0.0001
B2 15.14 1 15.14 46.14 <0.0001
C2 5.03 1 5.03 15.33 0.0014
D2 2.20 1 2.20 6.72 0.0204
E2 3.52 1 3.52 10.74 0.0051
Residual 2.69 11 0.24
Lack of Fit 1.55 6 0.26 1.66 0.3006 Not Significant
Pure Error 1.44 5 0.23
Cor Total 60.84 31
It was noted that MS of the model (3.49) is many times larger
than MS of the residual (0.24), thus the computed F-value of
the model (F=3.49/0.24) of 14.54 implies that the model is
significant.
Table 6 shows the R-Squared (R2), "Adjusted R-Squared
(Adj. R2)" and "Predicted R- Squared (Pred. R2)" statistics.
The R-Squared is defined as the ratio of variability explained
by the model to the total variability in the actual data and is
used as a measure of the goodness of fit. The more R2
approaches unity, the better the model fits the experimental
data. For instance, the obtained value of 0.9191 for R2 in the
case of SR (Table 6) implies that the model explains variations
in the SR to the extent of 91.91% in the current experiment
and thus the model is adequate to represent the process.
The "Predicted R2" of 0.5433 is in reasonable agreement with
the "Adjusted R2" of 0.8328 because the difference between
the adjusted and predicted R2 is within 0.28 as recommended
for model to be adequate.
The value of Pred. R2" of 0.5433 indicates the prediction
capability of the regression model. It means that the model
explains about 54.33% of the variability in predicting new
observations as compared to the 91.91% of the variability in
the original data explained by the least square fit. "Adeq
Precision" measures the signal to noise ratio. A ratio greater
than 4 is desirable. The ratio of 15.697 indicates an adequate
signal. Thus, the overall prediction capability of the model
based on these criteria seems very satisfactory.
Table -6: Post ANOVA Model adequacy for SR
R-Squared 0.9191
Adj R-Squared 0.8328
Pred R-Squared 0.5433
Adeq Precision 15.697
6. ANALYSIS AND DISCUSSION OF RESULTS
The set of experiments is designed and conducted by
employing RSM using discontinuous nature of vibration. The
selection of appropriate model and the development of
response surface models have been carried out by using
statistical software “Design Expert (DX-8)”. The regression
equations for the selected model are obtained for the response
characteristics, viz., SR. These regression equations are
developed using the experimental data presented in Table 3.
The response surfaces are plotted to investigate the effect of
input process parameters, amplitude of vibration, peak current,
pulse-on time, machining time and flushing pressure together
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 733
with their second order interaction on the response
characteristics. The Analysis of Variance (ANOVA) is
performed to statistically analyze the results.
6.1 Selection of Adequate Model
Table 7 displays different tests to select the adequate model to
be fit for SR. The “sequential model sum of squares” test in
each Table7 shows how the terms of increasing complexity
contribute to the model. It can be observed that for all the
responses/characteristics, the quadratic model is appropriate
[because Prob>F Value, 0.0001 (Table 7), is less than 0.05].
The addition of cubic terms does not significantly improve the
lack of fit because these terms are aliased for CCD (even if
these were significant). The “lack of fit” test compares the
residual error to the pure error from the replicated design
points.
Table -7: Selection of Adequate Model for surface roughness
1 Sequential Model sum of Squares
Source Sum of Squares DOF Mean Square F-value Prob > F
Mean 3283.34 1 3283.34
Linear 6.21 5 1.24 0.59 0.7065
2FI 13.61 10 1.36 0.53 0.8441
Quadratic 38.32z 5 7.66 31.33 <0.0001 Suggested
Cubic 0.92 5 0.18 0.63 0.6867 Aliased
Residual 1.77 6 0.29
Total 3344.17 32 104.51
2 Lack of Fit Tests
Source Sum of Squares DOF Mean Square F-Value Prob > F
Linear 53.48 21 2.55 11.16 0.0070
2FI 39.87 11 3.62 15.89 0.0034
Quadratic 1.55 6 0.26 1.13 0.4555 Suggested
Cubic 0.63 1 0.63 2.74 0.1587 Aliased
Pure Error 1.14 5 0.23
3 Model summary Statistics
Source Standard Deviation R-Squared Adjusted R-
Squared
Predicted R-
Squared
PRESS
Linear 1.45 0.1021 -0.0705 -0.3993 85.13
2FI 1.60 0.3259 -0.3061 -0.5057 91.60
Quadratic 0.49 0.9558 0.8754 0.3309 40.70 Suggested
Cubic 0.54 0.9710 0.8500 -10.1824 680.29 Aliased
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 734
The Table 7 indicates that the quadratic model in all the
response characteristics does not show significant lack of fit,
hence the adequacy of quadratic model is confirmed. Another
test “model summary statistics” given in the following Table
further confirms that the quadratic model is the best to fix as it
exhibits low standard deviation, high “R-Squared” values, and
a low “PRESS” (Adeq Precision).
fLN = N–1 …2
Where,
fLN = Total degrees of freedom of an OA
LN = OA designation
N = Number of trials
The regression coefficients of the second order equation
(Equation 3) are obtained by using the experimental data in
Table 3. The regression equations for the response
characteristic as a function of five process parameters
considered in this experiment are given below. The
insignificant coefficients (identified from ANOVA) have been
omitted from the equation.
SR = +33.28–1.24*A+0.15*IP–0.06*Po–0.73*Mt–
30.15*Pf–8.21E–003*A*IP+2.66E–003*A*Ton–
0.02*A*M+1.08*A*Pf–4.03E–004*IP*Ton+5.91E–
003*P*Mt+0.22*IP*Pf–8.25E–
005*Ton*Mt+0.04*Ton*Pf+0.13*Mt*Pf+0.15*A2–
7.18E–003*IP2+1.65E–
004*Ton
2+0.01*Mt
2+8.66*Pf
2 3
Where A: Amplitude of vibration, IP: Peak Current, Ton:
Pulse-on Time, Mt: Machining Time, Pf: Flushing Pressure.
Using this equation, the response surfaces have been plotted to
study the effect of process parameters on the performance
characteristics.
Figure 3 shows the combined effect of amplitude of vibration
and peak current on SR. At the lower value of peak current
against amplitude of vibration, the SR is less but it increases
with increase in the peak current. SR decreases with increase
in the amplitude of vibration with respect to peak current up to
a certain level. This happens due to intensity of vibration, as
amplitude of vibration at its lower level allows less debris to
get removed and at higher level, the ideal time is more which
result in increased surface roughness.
Fig -3: Response Surface for the Combined Effect of
Amplitude of Vibration and Peak Current on SR
Figure 4 shows the combined effect of amplitude of vibration
and pulse-on time on SR. The amplitude of vibration and pulse
on-time have similar effect on SR..SR is minimum at the
average value of both parameter. The response increases when
the value of both factors increase, as with the increased
amplitude of vibration, more craters are created at the work
piece and with increased pulse on time as lesser time is
available to eject the eroded material from working zone.
Fig -4: Response Surface for the Combined Effect of
Amplitude of Vibration and Pulse-on Time on SR
Figure 5 shows the combined effect of amplitude of vibration
and machining time on SR. Lower value of machining time
and amplitude of vibration cause lesser SR while SR increases
with their increasing values, as the machining time is
increased, deeper craters are formed, which result in rougher
surfaces. The maximum value of response occurs at the lower
value of machining time and higher value of amplitude of
vibration.
15.00
20.00
25.00
30.00
35.00
2.00
3.00
4.00
5.00
6.00
8
8.5
9
9.5
10
10.5
SurfaceRoughness
A: Amplitude of Vibration
B: Peak Current
100.00
125.00
150.00
175.00
200.00
2.00
3.00
4.00
5.00
6.00
8
9
10
11
12
SurfaceRoughness
A: Amplitude of Vibration
C: Pulse-on Time
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 735
Fig -5: Response Surface for the Combined Effect of
Amplitude of Vibration and Machining Time on SR
Figure 6 shows the combined effect of amplitude of vibration
and flushing pressure on SR. At higher values of flushing
pressure and amplitude of vibration, SR is much more as
compared to the any other values.. On the lower value of
amplitude of vibration and increasing value of flushing
pressure the SR decrease while at the higher value of
amplitude of vibration with increasing value of flushing
pressure the SR increase. The SR is higher at lower and higher
value of amplitude of vibration with respect to flushing
pressure, while SR decreases at its average value.
Fig -6: Response Surface for the Combined Effect of
Amplitude of Vibration and Flushing Pressure on SR
Figure 7 shows the combined effect of peak current and pulse-
on time on SR. With rise in peak current, SR increases up to a
certain level and then decreases. At lower value of peak
current and increasing value of pulse on time, SR is much
lower then any other level. The response is maximum at
higher value of pulse on time and average value of peak
current. The pulse on-time causes the increased size of spark
crater with aid of peak current which results in increased SR.
Fig -7: Response Surface for the Combined Effect of Peak
Current and Pulse-on Time on SR
Figure 8 shows the combined effect of peak current and
machining time on SR. The SR decreases when the machining
time is maximum and peak current is minimum or vice versa.
The SR is maximum at average value of peak current and
higher value of machining time. The higher machining time
and peak current gave less time for the removal of debris
which results in increase in SR.
Fig -8: Response Surface for the Combined Effect of Peak
Current and Machining Time on SR
Figure 9 shows the combined effect of peak current and
flushing pressure on SR. The increase in peak current with
respect to flushing pressure increases the SR. The SR is lesser
when the flushing pressure is increasing and peak current is
minimum. When the peak current increases, the size of crater
increases which causes more erosion of the work piece. This
eroded work piece can only be removed with higher flushing
pressure otherwise the SR would increase.
25.00
27.00
29.00
31.00
33.00
35.00
2.00
3.00
4.00
5.00
6.00
8
9
10
11
12
13
SurfaceRoughness
A: Amplitude of Vibration
D: Machining Time
0.40
0.50
0.60
0.70
0.80
2.00
3.00
4.00
5.00
6.00
8
9
10
11
12
13
SurfaceRoughness
A: Amplitude of Vibration
E: Flushing Pressure
100.00
125.00
150.00
175.00
200.00
15.00
20.00
25.00
30.00
35.00
8
9
10
11
12
13
SurfaceRoughness
B: Peak Current
C: Pulse-on Time
25.00
27.00
29.00
31.00
33.00
35.00
15.00
20.00
25.00
30.00
35.00
8
9
10
11
12
13
SurfaceRoughness
B: Peak CurrentD: Machining Time
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 736
Fig -9: Response Surface for the Combined Effect of Peak
Current and Flushing Pressure on SR
Figure 10 shows the combined effect of pulse-on time and
machining time on SR. The SR is maximum at higher values
of pulse on time and machining time. The response is
minimum at the average values of both parameters. The pulse
on-time and machining time collectively effect the SR because
machining time is totally dependant on pulse on-time.
Fig -10: Response Surface for the Combined Effect of Pulse-
on Time and Machining Time on SR
Figure 11 shows the effect of pulse-on time and flushing
pressure on SR. SR increases at lower and higher levels of
both parameters collectively. SR decreases at minimum value
of pulse on time and higher value of flushing pressure or vice
versa. Pulse on time allows the current to erode the work piece
by heating it. The higher flushing pressure quench the work
piece while the lower did not remove debris properly, which
result higher SR at their higher and lower level.
Fig -11: Response Surface for the Combined Effect of Pulse-
on Time and Flushing Pressure on SR
Figure 12 shows the combined effect of machining time and
flushing pressure on SR. The SR is maximum when the values
of both the parameters are at higher level. With increase in
flushing pressure with respect to machining time, the SR
decreases. The increase in machining time with flushing
pressure causes, firstly reduction and then increase in SR. The
more flushing pressure with less machining time cause proper
elimination of debris which result in lower SR.
Fig -12: Response Surface for the Combined Effect of
Machining Time and Flushing Pressure on SR
CONCLUSIONS
Using Al6063 as work piece, the effects on SR is found by
five parameters i.e. amplitude of vibration, pulse-on time,
peak current, machining time and flushing pressure by using
RSM. It is concluded that all the five parameters have their
different effect on SR when used with each other. The
intensity of SR may be reduced by increasing flushing
pressure as this leads to easy removal of debris. Also, SR
increases with increase in amplitude of vibration, peak current
and pulse on-time.
0.40
0.50
0.60
0.70
0.80
15.00
20.00
25.00
30.00
35.00
8
9
10
11
12
13
SurfaceRoughness
B: Peak Current
E: Flushing Pressure
25.00
27.00
29.00
31.00
33.00
35.00
100.00
125.00
150.00
175.00
200.00
8
9
10
11
12
13
SurfaceRoughness
C: Pulse-on TimeD: Machining Time
0.40
0.50
0.60
0.70
0.80
100.00
125.00
150.00
175.00
200.008
9
10
11
12
13
SurfaceRoughness
C: Pulse-on Time
E: Flushing Pressure
0.40
0.50
0.60
0.70
0.80
25.00
27.00
29.00
31.00
33.00
35.00
8
9
10
11
12
13
SurfaceRoughness
D: Machining Time
E: Flushing Pressure
IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 737
So, it is concluded that at lower values of amplitude of
vibration, peak current & pulse on-time and higher value of
flushing pressure leads to reduction in SR.
REFERENCES
[1]. Russell V. Lenth, “Response-Surface Methods in R, Using
RSM,” Journal of Statistical Software, Vol. 32 (7), 2009
[2]. M.R. Shabgard and R.M. Shotorbani, “Mathematical
Modeling of Machining Parameters in Electrical Discharge
Machining of FW4 Welded Steel,” World Academy of Science,
Engineering and Technology, Vol. 52 (63), pp. 403-409, 2009
[3]. K. M. Iqbal Asif and Ahsan Ali Khan, “Modeling and
analysis of MRR, EWR and Surface Roughness in EDM
Milling through Response Surface Methodology,” American
Journal of Engineering and Applied Sciences, Vol. 3 (4), pp.
611-619, 2010
[4]. P. Sahoo, Routara B.C. and A. Bandyopadhyay,
“Roughness modeling and optimization in EDM using
response surface method for different work piece materials,”
International Journal of Machining and Machinability of
Materials, Vol. 5, (2-3), pp. 321- 346, 2009.
[5]. D. Kanagarajan, R. Karthikeyan, K. Palanikumar and P.
Sivaraj, “Influence of process parameters on electric
discharge machining of WC30%Co composites,” Proceedings
of the Institution of Mechanical Engineers, Part B: Journal of
Engineering Manufacture, Vol. 222, pp. 807-815, 2008.
[6]. M.R. Shabgard and R.M. Shotorbani, “Mathematical
Modeling of Machining Parameters in Electrical Discharge
Machining of FW4 Welded Steel” World Academy of Science,
Engineering and Technology, Vol. 52 (63), pp. 403-409, 2009.
[7]. M.K. Pradhan and C.K. Biswas, “Modeling & analysis of
process parameters on surface roughness in EDM of AISI D2
tool steel by RSM approach,” International Journal of
Mathematical, Physical and Engineering Sciences, Vol. 3 (1),
pp. 66-71, 2009
[8]. P. Kuppan, A. Rajadurai, and S. Narayanan, “Influence of
EDM process parameters in deep hole drilling of inconel
718,” International Journal of Advance Manufacturing
Technology, Vol. 38, pp. 74–84, 2007.
[9]. Jaharah, C. Liang, A. Wahid, S.Z., M. Rahman, and C.
Che Hassan, “Performance of copper electrode in electical
discharge machining of AISI H13 harden steel,” International
Journal of Mechanical and Materials Engineering, Vol. 3 (1),
pp. 25–29, 2008.
[10]. K. Chiang, “Modeling and analysis of the effects of
machining parameters on the performance characteristics in
the edm process of Al2O3+tic mixed ceramic,” International
Journal of Advanced Manufacturing Technology, Vol. 37 (5-
6), pp. 523–533, 2008.
[11]. D.C.Montgomery, “Design and Analysis of
Experiments,” Wiley-India, 2007.
[12]. Myers, D.C. Montgomery and Anderson-Cook,
“Response Surface Methodology,” John Wiley-New York,
2009.
[13]. D. C. Montgomery, “Design and analysis of
experiments,” John wiley and Sons Inc., 2001.
BIOGRAPHIES
Dr. Jujhar Singh has completed his Ph.D.
in 2012 from PEC University of
Technology, Chandigarh, he received his
B.Tech. (Mechanical Engineering) in 2000
from Guru Nanak Dev Engineering
College, Ludhiana, India and M.E.
(Rotodynamic Machines) in 2004 from
PEC University of Technology,
Chandigarh, India. He has 12 years of experience in teaching
and research. His area of research interest includes modeling
and analysis and optimization of manufacturing processes. He
has published more than 11 research papers in international
Journal/Conferences. He is life member of ISTE. Currently he
is working as Associate Professor and Vice-Principal with
Rayat and Bahra Institute of Engineering and Bio-Technology,
Kharar, Punjab, India.
Sukhvir Singh is a student of M.Tech
(Regular) in Rayat and Bahra Institute of
Engineering and Bio-Technology, Kharar,
Punjab, India. He received his B.Tech
(Mechanical Engineeering) from Rayat and
Bahra Institute of Engineering and Bio-
Technology, Kharar, Punjab, India in the
year 2012. His area of research interest
includes optimization of machining processes.
Gurpreet Singh is a student of M.Tech
(Regular) in Rayat and Bahra Institute of
Engineering and Bio-Technology, Kharar,
Punjab, India.. He received his degree in
Mechanical Engineering in 2012 from
Rayat and Bahra Institute of Engineering
and Bio-Technology, India, which is
affiliated to the Punjab Technical
University, India. He has been a dedicated,
consistent and hardworking student throughout his studies. He
has keen interest in the machining processes with a main focus
on the optimization techniques involved in it.

More Related Content

What's hot

International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
IJERD Editor
 
Effect of Process Parameters on MRR for HCHCr in WEDM using RSM methodology
Effect of Process Parameters on MRR for HCHCr in WEDM using RSM methodologyEffect of Process Parameters on MRR for HCHCr in WEDM using RSM methodology
Effect of Process Parameters on MRR for HCHCr in WEDM using RSM methodology
International Journal of Advanced Engineering Research and Applications
 
ELECTRO CHEMICAL MACHINING AND ELECTRICAL DISCHARGE MACHINING PROCESSES MICRO...
ELECTRO CHEMICAL MACHINING AND ELECTRICAL DISCHARGE MACHINING PROCESSES MICRO...ELECTRO CHEMICAL MACHINING AND ELECTRICAL DISCHARGE MACHINING PROCESSES MICRO...
ELECTRO CHEMICAL MACHINING AND ELECTRICAL DISCHARGE MACHINING PROCESSES MICRO...
ijiert bestjournal
 
Optimization of edm process parameters using taguchi method a review
Optimization of edm process parameters using taguchi method  a reviewOptimization of edm process parameters using taguchi method  a review
Optimization of edm process parameters using taguchi method a review
eSAT Journals
 
30120140502006
3012014050200630120140502006
30120140502006
IAEME Publication
 
Development of a Taguchi-based framework for optimizing two quality character...
Development of a Taguchi-based framework for optimizing two quality character...Development of a Taguchi-based framework for optimizing two quality character...
Development of a Taguchi-based framework for optimizing two quality character...
IJERA Editor
 
Multiple Optimization of Wire EDM Machining Parameters Using Grey Based Taguc...
Multiple Optimization of Wire EDM Machining Parameters Using Grey Based Taguc...Multiple Optimization of Wire EDM Machining Parameters Using Grey Based Taguc...
Multiple Optimization of Wire EDM Machining Parameters Using Grey Based Taguc...
IJMER
 
Wire EDM Parameters for Surface Roughness in Straight Gear Manufacturing: An ...
Wire EDM Parameters for Surface Roughness in Straight Gear Manufacturing: An ...Wire EDM Parameters for Surface Roughness in Straight Gear Manufacturing: An ...
Wire EDM Parameters for Surface Roughness in Straight Gear Manufacturing: An ...
Dr. Amarjeet Singh
 
Av4201331336
Av4201331336Av4201331336
Av4201331336
IJERA Editor
 
Optimization of Cutting Rate for EN 1010 Low Alloy Steel on WEDM Using Respon...
Optimization of Cutting Rate for EN 1010 Low Alloy Steel on WEDM Using Respon...Optimization of Cutting Rate for EN 1010 Low Alloy Steel on WEDM Using Respon...
Optimization of Cutting Rate for EN 1010 Low Alloy Steel on WEDM Using Respon...
IJAEMSJORNAL
 
30120140505019
3012014050501930120140505019
30120140505019
IAEME Publication
 
Optimization of MRR and SR by employing Taguchis and ANOVA method in EDM
Optimization of MRR and SR by employing Taguchis and ANOVA method in EDMOptimization of MRR and SR by employing Taguchis and ANOVA method in EDM
Optimization of MRR and SR by employing Taguchis and ANOVA method in EDM
IRJET Journal
 
Optimization of Electrical Discharge Machining Process Parameters using SCM42...
Optimization of Electrical Discharge Machining Process Parameters using SCM42...Optimization of Electrical Discharge Machining Process Parameters using SCM42...
Optimization of Electrical Discharge Machining Process Parameters using SCM42...
IOSRJMCE
 
Investigation on process response and parameters in wire electrical discharge...
Investigation on process response and parameters in wire electrical discharge...Investigation on process response and parameters in wire electrical discharge...
Investigation on process response and parameters in wire electrical discharge...
IAEME Publication
 
IRJET-Experimental Analysis Optimization of Process Parameters of Wire EDM on...
IRJET-Experimental Analysis Optimization of Process Parameters of Wire EDM on...IRJET-Experimental Analysis Optimization of Process Parameters of Wire EDM on...
IRJET-Experimental Analysis Optimization of Process Parameters of Wire EDM on...
IRJET Journal
 
Effect Of Wire Edm Process Parameters On Surface Roughness Of Aisi-4140 (Carb...
Effect Of Wire Edm Process Parameters On Surface Roughness Of Aisi-4140 (Carb...Effect Of Wire Edm Process Parameters On Surface Roughness Of Aisi-4140 (Carb...
Effect Of Wire Edm Process Parameters On Surface Roughness Of Aisi-4140 (Carb...
dbpublications
 
Investigation on process response and parameters in wire electrical
Investigation on process response and parameters in wire electricalInvestigation on process response and parameters in wire electrical
Investigation on process response and parameters in wire electrical
iaemedu
 
IJRRA-02-04-26
IJRRA-02-04-26IJRRA-02-04-26
IJRRA-02-04-26
Chetan Bhardwaj
 

What's hot (18)

International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
 
Effect of Process Parameters on MRR for HCHCr in WEDM using RSM methodology
Effect of Process Parameters on MRR for HCHCr in WEDM using RSM methodologyEffect of Process Parameters on MRR for HCHCr in WEDM using RSM methodology
Effect of Process Parameters on MRR for HCHCr in WEDM using RSM methodology
 
ELECTRO CHEMICAL MACHINING AND ELECTRICAL DISCHARGE MACHINING PROCESSES MICRO...
ELECTRO CHEMICAL MACHINING AND ELECTRICAL DISCHARGE MACHINING PROCESSES MICRO...ELECTRO CHEMICAL MACHINING AND ELECTRICAL DISCHARGE MACHINING PROCESSES MICRO...
ELECTRO CHEMICAL MACHINING AND ELECTRICAL DISCHARGE MACHINING PROCESSES MICRO...
 
Optimization of edm process parameters using taguchi method a review
Optimization of edm process parameters using taguchi method  a reviewOptimization of edm process parameters using taguchi method  a review
Optimization of edm process parameters using taguchi method a review
 
30120140502006
3012014050200630120140502006
30120140502006
 
Development of a Taguchi-based framework for optimizing two quality character...
Development of a Taguchi-based framework for optimizing two quality character...Development of a Taguchi-based framework for optimizing two quality character...
Development of a Taguchi-based framework for optimizing two quality character...
 
Multiple Optimization of Wire EDM Machining Parameters Using Grey Based Taguc...
Multiple Optimization of Wire EDM Machining Parameters Using Grey Based Taguc...Multiple Optimization of Wire EDM Machining Parameters Using Grey Based Taguc...
Multiple Optimization of Wire EDM Machining Parameters Using Grey Based Taguc...
 
Wire EDM Parameters for Surface Roughness in Straight Gear Manufacturing: An ...
Wire EDM Parameters for Surface Roughness in Straight Gear Manufacturing: An ...Wire EDM Parameters for Surface Roughness in Straight Gear Manufacturing: An ...
Wire EDM Parameters for Surface Roughness in Straight Gear Manufacturing: An ...
 
Av4201331336
Av4201331336Av4201331336
Av4201331336
 
Optimization of Cutting Rate for EN 1010 Low Alloy Steel on WEDM Using Respon...
Optimization of Cutting Rate for EN 1010 Low Alloy Steel on WEDM Using Respon...Optimization of Cutting Rate for EN 1010 Low Alloy Steel on WEDM Using Respon...
Optimization of Cutting Rate for EN 1010 Low Alloy Steel on WEDM Using Respon...
 
30120140505019
3012014050501930120140505019
30120140505019
 
Optimization of MRR and SR by employing Taguchis and ANOVA method in EDM
Optimization of MRR and SR by employing Taguchis and ANOVA method in EDMOptimization of MRR and SR by employing Taguchis and ANOVA method in EDM
Optimization of MRR and SR by employing Taguchis and ANOVA method in EDM
 
Optimization of Electrical Discharge Machining Process Parameters using SCM42...
Optimization of Electrical Discharge Machining Process Parameters using SCM42...Optimization of Electrical Discharge Machining Process Parameters using SCM42...
Optimization of Electrical Discharge Machining Process Parameters using SCM42...
 
Investigation on process response and parameters in wire electrical discharge...
Investigation on process response and parameters in wire electrical discharge...Investigation on process response and parameters in wire electrical discharge...
Investigation on process response and parameters in wire electrical discharge...
 
IRJET-Experimental Analysis Optimization of Process Parameters of Wire EDM on...
IRJET-Experimental Analysis Optimization of Process Parameters of Wire EDM on...IRJET-Experimental Analysis Optimization of Process Parameters of Wire EDM on...
IRJET-Experimental Analysis Optimization of Process Parameters of Wire EDM on...
 
Effect Of Wire Edm Process Parameters On Surface Roughness Of Aisi-4140 (Carb...
Effect Of Wire Edm Process Parameters On Surface Roughness Of Aisi-4140 (Carb...Effect Of Wire Edm Process Parameters On Surface Roughness Of Aisi-4140 (Carb...
Effect Of Wire Edm Process Parameters On Surface Roughness Of Aisi-4140 (Carb...
 
Investigation on process response and parameters in wire electrical
Investigation on process response and parameters in wire electricalInvestigation on process response and parameters in wire electrical
Investigation on process response and parameters in wire electrical
 
IJRRA-02-04-26
IJRRA-02-04-26IJRRA-02-04-26
IJRRA-02-04-26
 

Viewers also liked

Face detection for video summary using enhancement based fusion strategy
Face detection for video summary using enhancement based fusion strategyFace detection for video summary using enhancement based fusion strategy
Face detection for video summary using enhancement based fusion strategy
eSAT Publishing House
 
Enhanced bandwidth multiband slot antenna for portable devices
Enhanced bandwidth multiband slot antenna for portable devicesEnhanced bandwidth multiband slot antenna for portable devices
Enhanced bandwidth multiband slot antenna for portable devices
eSAT Publishing House
 
Hybrid fingerprint matching algorithm for high accuracy and reliability
Hybrid fingerprint matching algorithm for high accuracy and reliabilityHybrid fingerprint matching algorithm for high accuracy and reliability
Hybrid fingerprint matching algorithm for high accuracy and reliability
eSAT Publishing House
 
Cast metal flow analysis of intermediate flange
Cast metal flow analysis of intermediate flangeCast metal flow analysis of intermediate flange
Cast metal flow analysis of intermediate flange
eSAT Publishing House
 
Counter based design of dpll for wireless communication
Counter based design of dpll for wireless communicationCounter based design of dpll for wireless communication
Counter based design of dpll for wireless communication
eSAT Publishing House
 
Exploring the preferred temperature on occupants thermal comfort in the humid...
Exploring the preferred temperature on occupants thermal comfort in the humid...Exploring the preferred temperature on occupants thermal comfort in the humid...
Exploring the preferred temperature on occupants thermal comfort in the humid...
eSAT Publishing House
 
Design and implementation of a fuzzy based tsunami warning system
Design and implementation of a fuzzy based tsunami warning systemDesign and implementation of a fuzzy based tsunami warning system
Design and implementation of a fuzzy based tsunami warning system
eSAT Publishing House
 
Factors affecting def and asr in the concrete dam at vrané nad vltavou
Factors affecting def and asr in the concrete dam at vrané nad vltavouFactors affecting def and asr in the concrete dam at vrané nad vltavou
Factors affecting def and asr in the concrete dam at vrané nad vltavou
eSAT Publishing House
 
Soft subgrade stabilization with quarry dust an industrial waste
Soft subgrade stabilization with quarry dust an industrial wasteSoft subgrade stabilization with quarry dust an industrial waste
Soft subgrade stabilization with quarry dust an industrial waste
eSAT Publishing House
 
Performance of blended corrosion inhibitors for reinforced concrete
Performance of blended corrosion inhibitors for reinforced concretePerformance of blended corrosion inhibitors for reinforced concrete
Performance of blended corrosion inhibitors for reinforced concrete
eSAT Publishing House
 
Effect of spikes integrated to airfoil at supersonic
Effect of spikes integrated to airfoil at supersonicEffect of spikes integrated to airfoil at supersonic
Effect of spikes integrated to airfoil at supersonic
eSAT Publishing House
 
Hazard object reporting to respective authorities
Hazard object reporting to respective authoritiesHazard object reporting to respective authorities
Hazard object reporting to respective authorities
eSAT Publishing House
 
A review on glitch reduction techniques
A review on glitch reduction techniquesA review on glitch reduction techniques
A review on glitch reduction techniques
eSAT Publishing House
 
Performance investigation of electricial power supply to owerri for higher pr...
Performance investigation of electricial power supply to owerri for higher pr...Performance investigation of electricial power supply to owerri for higher pr...
Performance investigation of electricial power supply to owerri for higher pr...
eSAT Publishing House
 
Humidity intrusion effects on the properties of sound
Humidity intrusion effects on the properties of soundHumidity intrusion effects on the properties of sound
Humidity intrusion effects on the properties of sound
eSAT Publishing House
 
Surveillance and tracking of elephants using vocal spectral information
Surveillance and tracking of elephants using vocal spectral informationSurveillance and tracking of elephants using vocal spectral information
Surveillance and tracking of elephants using vocal spectral information
eSAT Publishing House
 
Comparison of symmetrical and asymmetrical cascaded
Comparison of symmetrical and asymmetrical cascadedComparison of symmetrical and asymmetrical cascaded
Comparison of symmetrical and asymmetrical cascaded
eSAT Publishing House
 
Effect of prism height on strength of reinforced hollow concrete block masonry
Effect of prism height on strength of reinforced hollow concrete block masonryEffect of prism height on strength of reinforced hollow concrete block masonry
Effect of prism height on strength of reinforced hollow concrete block masonry
eSAT Publishing House
 
Mri brain image segmentatin and classification by
Mri brain image segmentatin and classification byMri brain image segmentatin and classification by
Mri brain image segmentatin and classification by
eSAT Publishing House
 
Sdci scalable distributed cache indexing for cache consistency for mobile env...
Sdci scalable distributed cache indexing for cache consistency for mobile env...Sdci scalable distributed cache indexing for cache consistency for mobile env...
Sdci scalable distributed cache indexing for cache consistency for mobile env...
eSAT Publishing House
 

Viewers also liked (20)

Face detection for video summary using enhancement based fusion strategy
Face detection for video summary using enhancement based fusion strategyFace detection for video summary using enhancement based fusion strategy
Face detection for video summary using enhancement based fusion strategy
 
Enhanced bandwidth multiband slot antenna for portable devices
Enhanced bandwidth multiband slot antenna for portable devicesEnhanced bandwidth multiband slot antenna for portable devices
Enhanced bandwidth multiband slot antenna for portable devices
 
Hybrid fingerprint matching algorithm for high accuracy and reliability
Hybrid fingerprint matching algorithm for high accuracy and reliabilityHybrid fingerprint matching algorithm for high accuracy and reliability
Hybrid fingerprint matching algorithm for high accuracy and reliability
 
Cast metal flow analysis of intermediate flange
Cast metal flow analysis of intermediate flangeCast metal flow analysis of intermediate flange
Cast metal flow analysis of intermediate flange
 
Counter based design of dpll for wireless communication
Counter based design of dpll for wireless communicationCounter based design of dpll for wireless communication
Counter based design of dpll for wireless communication
 
Exploring the preferred temperature on occupants thermal comfort in the humid...
Exploring the preferred temperature on occupants thermal comfort in the humid...Exploring the preferred temperature on occupants thermal comfort in the humid...
Exploring the preferred temperature on occupants thermal comfort in the humid...
 
Design and implementation of a fuzzy based tsunami warning system
Design and implementation of a fuzzy based tsunami warning systemDesign and implementation of a fuzzy based tsunami warning system
Design and implementation of a fuzzy based tsunami warning system
 
Factors affecting def and asr in the concrete dam at vrané nad vltavou
Factors affecting def and asr in the concrete dam at vrané nad vltavouFactors affecting def and asr in the concrete dam at vrané nad vltavou
Factors affecting def and asr in the concrete dam at vrané nad vltavou
 
Soft subgrade stabilization with quarry dust an industrial waste
Soft subgrade stabilization with quarry dust an industrial wasteSoft subgrade stabilization with quarry dust an industrial waste
Soft subgrade stabilization with quarry dust an industrial waste
 
Performance of blended corrosion inhibitors for reinforced concrete
Performance of blended corrosion inhibitors for reinforced concretePerformance of blended corrosion inhibitors for reinforced concrete
Performance of blended corrosion inhibitors for reinforced concrete
 
Effect of spikes integrated to airfoil at supersonic
Effect of spikes integrated to airfoil at supersonicEffect of spikes integrated to airfoil at supersonic
Effect of spikes integrated to airfoil at supersonic
 
Hazard object reporting to respective authorities
Hazard object reporting to respective authoritiesHazard object reporting to respective authorities
Hazard object reporting to respective authorities
 
A review on glitch reduction techniques
A review on glitch reduction techniquesA review on glitch reduction techniques
A review on glitch reduction techniques
 
Performance investigation of electricial power supply to owerri for higher pr...
Performance investigation of electricial power supply to owerri for higher pr...Performance investigation of electricial power supply to owerri for higher pr...
Performance investigation of electricial power supply to owerri for higher pr...
 
Humidity intrusion effects on the properties of sound
Humidity intrusion effects on the properties of soundHumidity intrusion effects on the properties of sound
Humidity intrusion effects on the properties of sound
 
Surveillance and tracking of elephants using vocal spectral information
Surveillance and tracking of elephants using vocal spectral informationSurveillance and tracking of elephants using vocal spectral information
Surveillance and tracking of elephants using vocal spectral information
 
Comparison of symmetrical and asymmetrical cascaded
Comparison of symmetrical and asymmetrical cascadedComparison of symmetrical and asymmetrical cascaded
Comparison of symmetrical and asymmetrical cascaded
 
Effect of prism height on strength of reinforced hollow concrete block masonry
Effect of prism height on strength of reinforced hollow concrete block masonryEffect of prism height on strength of reinforced hollow concrete block masonry
Effect of prism height on strength of reinforced hollow concrete block masonry
 
Mri brain image segmentatin and classification by
Mri brain image segmentatin and classification byMri brain image segmentatin and classification by
Mri brain image segmentatin and classification by
 
Sdci scalable distributed cache indexing for cache consistency for mobile env...
Sdci scalable distributed cache indexing for cache consistency for mobile env...Sdci scalable distributed cache indexing for cache consistency for mobile env...
Sdci scalable distributed cache indexing for cache consistency for mobile env...
 

Similar to Study of surface roughness for discontinuous

Influence of process parameters on MRR in EDM of AISI D2 Steel: a RSM approach
Influence of process parameters on MRR in EDM of AISI D2  Steel: a RSM approachInfluence of process parameters on MRR in EDM of AISI D2  Steel: a RSM approach
Influence of process parameters on MRR in EDM of AISI D2 Steel: a RSM approach
Mohan Kumar Pradhan
 
Modeling and Analysis of process parameters on Surface Roughness in EDM of AI...
Modeling and Analysis of process parameters on Surface Roughness in EDM of AI...Modeling and Analysis of process parameters on Surface Roughness in EDM of AI...
Modeling and Analysis of process parameters on Surface Roughness in EDM of AI...
Mohan Kumar Pradhan
 
Modeling and optimization of EDM Process Parameters on Machining of Inconel ...
Modeling and optimization of EDM Process Parameters on Machining  of Inconel ...Modeling and optimization of EDM Process Parameters on Machining  of Inconel ...
Modeling and optimization of EDM Process Parameters on Machining of Inconel ...
ROEVER GROUPS
 
MODELING AND OPTIMIZATION OF EDM PROCESS PARAMETERS: A REVIEW
MODELING AND OPTIMIZATION OF EDM PROCESS PARAMETERS: A REVIEWMODELING AND OPTIMIZATION OF EDM PROCESS PARAMETERS: A REVIEW
MODELING AND OPTIMIZATION OF EDM PROCESS PARAMETERS: A REVIEW
IAEME Publication
 
MULTI OBJECTIVE OPTIMIZATION OF NEAR-DRY EDM PROCESS USING RESPONSE SURFACE M...
MULTI OBJECTIVE OPTIMIZATION OF NEAR-DRY EDM PROCESS USING RESPONSE SURFACE M...MULTI OBJECTIVE OPTIMIZATION OF NEAR-DRY EDM PROCESS USING RESPONSE SURFACE M...
MULTI OBJECTIVE OPTIMIZATION OF NEAR-DRY EDM PROCESS USING RESPONSE SURFACE M...
IAEME Publication
 
THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM...
THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM...THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM...
THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM...
IAEME Publication
 
Process Parameter Optimization of WEDM for AISI M2 & AISI H13 by Anova & Anal...
Process Parameter Optimization of WEDM for AISI M2 & AISI H13 by Anova & Anal...Process Parameter Optimization of WEDM for AISI M2 & AISI H13 by Anova & Anal...
Process Parameter Optimization of WEDM for AISI M2 & AISI H13 by Anova & Anal...
IJERA Editor
 
Gray Relational Basedanalysis of Tool Steel
Gray Relational Basedanalysis of Tool SteelGray Relational Basedanalysis of Tool Steel
Gray Relational Basedanalysis of Tool Steel
IRJET Journal
 
Optimization of EDM Process Parameters using Response Surface Methodology for...
Optimization of EDM Process Parameters using Response Surface Methodology for...Optimization of EDM Process Parameters using Response Surface Methodology for...
Optimization of EDM Process Parameters using Response Surface Methodology for...
ijtsrd
 
Optimization of EDM Process of (Cu-W) EDM Electrodes on Different Progression
Optimization of EDM Process of (Cu-W) EDM Electrodes on Different ProgressionOptimization of EDM Process of (Cu-W) EDM Electrodes on Different Progression
Optimization of EDM Process of (Cu-W) EDM Electrodes on Different Progression
IJERA Editor
 
THE EFFECTS OF MACHINING PARAMETERS ON SURFACE ROUGHNESS OF MATERIAL EN-31 IN...
THE EFFECTS OF MACHINING PARAMETERS ON SURFACE ROUGHNESS OF MATERIAL EN-31 IN...THE EFFECTS OF MACHINING PARAMETERS ON SURFACE ROUGHNESS OF MATERIAL EN-31 IN...
THE EFFECTS OF MACHINING PARAMETERS ON SURFACE ROUGHNESS OF MATERIAL EN-31 IN...
ijiert bestjournal
 
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...
IJAEMSJORNAL
 
Machinability evaluation of cryogenically tempered die steel in electric disc...
Machinability evaluation of cryogenically tempered die steel in electric disc...Machinability evaluation of cryogenically tempered die steel in electric disc...
Machinability evaluation of cryogenically tempered die steel in electric disc...
eSAT Journals
 
Machinability evaluation of cryogenically tempered die steel in electric disc...
Machinability evaluation of cryogenically tempered die steel in electric disc...Machinability evaluation of cryogenically tempered die steel in electric disc...
Machinability evaluation of cryogenically tempered die steel in electric disc...
eSAT Publishing House
 
Development of a Taguchi-based framework for optimizing two quality character...
Development of a Taguchi-based framework for optimizing two quality character...Development of a Taguchi-based framework for optimizing two quality character...
Development of a Taguchi-based framework for optimizing two quality character...
IJERA Editor
 
Comparison of fuzzy logic and neural network for modelling surface roughness ...
Comparison of fuzzy logic and neural network for modelling surface roughness ...Comparison of fuzzy logic and neural network for modelling surface roughness ...
Comparison of fuzzy logic and neural network for modelling surface roughness ...
ijmech
 
F013134455
F013134455F013134455
F013134455
IOSR Journals
 
Trends in Wire Electrical Discharge Machining (WEDM): A Review
Trends in Wire Electrical Discharge Machining (WEDM): A ReviewTrends in Wire Electrical Discharge Machining (WEDM): A Review
Trends in Wire Electrical Discharge Machining (WEDM): A Review
IJERA Editor
 

Similar to Study of surface roughness for discontinuous (18)

Influence of process parameters on MRR in EDM of AISI D2 Steel: a RSM approach
Influence of process parameters on MRR in EDM of AISI D2  Steel: a RSM approachInfluence of process parameters on MRR in EDM of AISI D2  Steel: a RSM approach
Influence of process parameters on MRR in EDM of AISI D2 Steel: a RSM approach
 
Modeling and Analysis of process parameters on Surface Roughness in EDM of AI...
Modeling and Analysis of process parameters on Surface Roughness in EDM of AI...Modeling and Analysis of process parameters on Surface Roughness in EDM of AI...
Modeling and Analysis of process parameters on Surface Roughness in EDM of AI...
 
Modeling and optimization of EDM Process Parameters on Machining of Inconel ...
Modeling and optimization of EDM Process Parameters on Machining  of Inconel ...Modeling and optimization of EDM Process Parameters on Machining  of Inconel ...
Modeling and optimization of EDM Process Parameters on Machining of Inconel ...
 
MODELING AND OPTIMIZATION OF EDM PROCESS PARAMETERS: A REVIEW
MODELING AND OPTIMIZATION OF EDM PROCESS PARAMETERS: A REVIEWMODELING AND OPTIMIZATION OF EDM PROCESS PARAMETERS: A REVIEW
MODELING AND OPTIMIZATION OF EDM PROCESS PARAMETERS: A REVIEW
 
MULTI OBJECTIVE OPTIMIZATION OF NEAR-DRY EDM PROCESS USING RESPONSE SURFACE M...
MULTI OBJECTIVE OPTIMIZATION OF NEAR-DRY EDM PROCESS USING RESPONSE SURFACE M...MULTI OBJECTIVE OPTIMIZATION OF NEAR-DRY EDM PROCESS USING RESPONSE SURFACE M...
MULTI OBJECTIVE OPTIMIZATION OF NEAR-DRY EDM PROCESS USING RESPONSE SURFACE M...
 
THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM...
THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM...THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM...
THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM...
 
Process Parameter Optimization of WEDM for AISI M2 & AISI H13 by Anova & Anal...
Process Parameter Optimization of WEDM for AISI M2 & AISI H13 by Anova & Anal...Process Parameter Optimization of WEDM for AISI M2 & AISI H13 by Anova & Anal...
Process Parameter Optimization of WEDM for AISI M2 & AISI H13 by Anova & Anal...
 
Gray Relational Basedanalysis of Tool Steel
Gray Relational Basedanalysis of Tool SteelGray Relational Basedanalysis of Tool Steel
Gray Relational Basedanalysis of Tool Steel
 
Optimization of EDM Process Parameters using Response Surface Methodology for...
Optimization of EDM Process Parameters using Response Surface Methodology for...Optimization of EDM Process Parameters using Response Surface Methodology for...
Optimization of EDM Process Parameters using Response Surface Methodology for...
 
Optimization of EDM Process of (Cu-W) EDM Electrodes on Different Progression
Optimization of EDM Process of (Cu-W) EDM Electrodes on Different ProgressionOptimization of EDM Process of (Cu-W) EDM Electrodes on Different Progression
Optimization of EDM Process of (Cu-W) EDM Electrodes on Different Progression
 
THE EFFECTS OF MACHINING PARAMETERS ON SURFACE ROUGHNESS OF MATERIAL EN-31 IN...
THE EFFECTS OF MACHINING PARAMETERS ON SURFACE ROUGHNESS OF MATERIAL EN-31 IN...THE EFFECTS OF MACHINING PARAMETERS ON SURFACE ROUGHNESS OF MATERIAL EN-31 IN...
THE EFFECTS OF MACHINING PARAMETERS ON SURFACE ROUGHNESS OF MATERIAL EN-31 IN...
 
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...
 
Machinability evaluation of cryogenically tempered die steel in electric disc...
Machinability evaluation of cryogenically tempered die steel in electric disc...Machinability evaluation of cryogenically tempered die steel in electric disc...
Machinability evaluation of cryogenically tempered die steel in electric disc...
 
Machinability evaluation of cryogenically tempered die steel in electric disc...
Machinability evaluation of cryogenically tempered die steel in electric disc...Machinability evaluation of cryogenically tempered die steel in electric disc...
Machinability evaluation of cryogenically tempered die steel in electric disc...
 
Development of a Taguchi-based framework for optimizing two quality character...
Development of a Taguchi-based framework for optimizing two quality character...Development of a Taguchi-based framework for optimizing two quality character...
Development of a Taguchi-based framework for optimizing two quality character...
 
Comparison of fuzzy logic and neural network for modelling surface roughness ...
Comparison of fuzzy logic and neural network for modelling surface roughness ...Comparison of fuzzy logic and neural network for modelling surface roughness ...
Comparison of fuzzy logic and neural network for modelling surface roughness ...
 
F013134455
F013134455F013134455
F013134455
 
Trends in Wire Electrical Discharge Machining (WEDM): A Review
Trends in Wire Electrical Discharge Machining (WEDM): A ReviewTrends in Wire Electrical Discharge Machining (WEDM): A Review
Trends in Wire Electrical Discharge Machining (WEDM): A Review
 

More from eSAT Publishing House

Likely impacts of hudhud on the environment of visakhapatnam
Likely impacts of hudhud on the environment of visakhapatnamLikely impacts of hudhud on the environment of visakhapatnam
Likely impacts of hudhud on the environment of visakhapatnam
eSAT Publishing House
 
Impact of flood disaster in a drought prone area – case study of alampur vill...
Impact of flood disaster in a drought prone area – case study of alampur vill...Impact of flood disaster in a drought prone area – case study of alampur vill...
Impact of flood disaster in a drought prone area – case study of alampur vill...
eSAT Publishing House
 
Hudhud cyclone – a severe disaster in visakhapatnam
Hudhud cyclone – a severe disaster in visakhapatnamHudhud cyclone – a severe disaster in visakhapatnam
Hudhud cyclone – a severe disaster in visakhapatnam
eSAT Publishing House
 
Groundwater investigation using geophysical methods a case study of pydibhim...
Groundwater investigation using geophysical methods  a case study of pydibhim...Groundwater investigation using geophysical methods  a case study of pydibhim...
Groundwater investigation using geophysical methods a case study of pydibhim...
eSAT Publishing House
 
Flood related disasters concerned to urban flooding in bangalore, india
Flood related disasters concerned to urban flooding in bangalore, indiaFlood related disasters concerned to urban flooding in bangalore, india
Flood related disasters concerned to urban flooding in bangalore, india
eSAT Publishing House
 
Enhancing post disaster recovery by optimal infrastructure capacity building
Enhancing post disaster recovery by optimal infrastructure capacity buildingEnhancing post disaster recovery by optimal infrastructure capacity building
Enhancing post disaster recovery by optimal infrastructure capacity building
eSAT Publishing House
 
Effect of lintel and lintel band on the global performance of reinforced conc...
Effect of lintel and lintel band on the global performance of reinforced conc...Effect of lintel and lintel band on the global performance of reinforced conc...
Effect of lintel and lintel band on the global performance of reinforced conc...
eSAT Publishing House
 
Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...
Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...
Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...
eSAT Publishing House
 
Wind damage to buildings, infrastrucuture and landscape elements along the be...
Wind damage to buildings, infrastrucuture and landscape elements along the be...Wind damage to buildings, infrastrucuture and landscape elements along the be...
Wind damage to buildings, infrastrucuture and landscape elements along the be...
eSAT Publishing House
 
Shear strength of rc deep beam panels – a review
Shear strength of rc deep beam panels – a reviewShear strength of rc deep beam panels – a review
Shear strength of rc deep beam panels – a review
eSAT Publishing House
 
Role of voluntary teams of professional engineers in dissater management – ex...
Role of voluntary teams of professional engineers in dissater management – ex...Role of voluntary teams of professional engineers in dissater management – ex...
Role of voluntary teams of professional engineers in dissater management – ex...
eSAT Publishing House
 
Risk analysis and environmental hazard management
Risk analysis and environmental hazard managementRisk analysis and environmental hazard management
Risk analysis and environmental hazard management
eSAT Publishing House
 
Review study on performance of seismically tested repaired shear walls
Review study on performance of seismically tested repaired shear wallsReview study on performance of seismically tested repaired shear walls
Review study on performance of seismically tested repaired shear walls
eSAT Publishing House
 
Monitoring and assessment of air quality with reference to dust particles (pm...
Monitoring and assessment of air quality with reference to dust particles (pm...Monitoring and assessment of air quality with reference to dust particles (pm...
Monitoring and assessment of air quality with reference to dust particles (pm...
eSAT Publishing House
 
Low cost wireless sensor networks and smartphone applications for disaster ma...
Low cost wireless sensor networks and smartphone applications for disaster ma...Low cost wireless sensor networks and smartphone applications for disaster ma...
Low cost wireless sensor networks and smartphone applications for disaster ma...
eSAT Publishing House
 
Coastal zones – seismic vulnerability an analysis from east coast of india
Coastal zones – seismic vulnerability an analysis from east coast of indiaCoastal zones – seismic vulnerability an analysis from east coast of india
Coastal zones – seismic vulnerability an analysis from east coast of india
eSAT Publishing House
 
Can fracture mechanics predict damage due disaster of structures
Can fracture mechanics predict damage due disaster of structuresCan fracture mechanics predict damage due disaster of structures
Can fracture mechanics predict damage due disaster of structures
eSAT Publishing House
 
Assessment of seismic susceptibility of rc buildings
Assessment of seismic susceptibility of rc buildingsAssessment of seismic susceptibility of rc buildings
Assessment of seismic susceptibility of rc buildings
eSAT Publishing House
 
A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...
A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...
A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...
eSAT Publishing House
 
Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...
Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...
Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...
eSAT Publishing House
 

More from eSAT Publishing House (20)

Likely impacts of hudhud on the environment of visakhapatnam
Likely impacts of hudhud on the environment of visakhapatnamLikely impacts of hudhud on the environment of visakhapatnam
Likely impacts of hudhud on the environment of visakhapatnam
 
Impact of flood disaster in a drought prone area – case study of alampur vill...
Impact of flood disaster in a drought prone area – case study of alampur vill...Impact of flood disaster in a drought prone area – case study of alampur vill...
Impact of flood disaster in a drought prone area – case study of alampur vill...
 
Hudhud cyclone – a severe disaster in visakhapatnam
Hudhud cyclone – a severe disaster in visakhapatnamHudhud cyclone – a severe disaster in visakhapatnam
Hudhud cyclone – a severe disaster in visakhapatnam
 
Groundwater investigation using geophysical methods a case study of pydibhim...
Groundwater investigation using geophysical methods  a case study of pydibhim...Groundwater investigation using geophysical methods  a case study of pydibhim...
Groundwater investigation using geophysical methods a case study of pydibhim...
 
Flood related disasters concerned to urban flooding in bangalore, india
Flood related disasters concerned to urban flooding in bangalore, indiaFlood related disasters concerned to urban flooding in bangalore, india
Flood related disasters concerned to urban flooding in bangalore, india
 
Enhancing post disaster recovery by optimal infrastructure capacity building
Enhancing post disaster recovery by optimal infrastructure capacity buildingEnhancing post disaster recovery by optimal infrastructure capacity building
Enhancing post disaster recovery by optimal infrastructure capacity building
 
Effect of lintel and lintel band on the global performance of reinforced conc...
Effect of lintel and lintel band on the global performance of reinforced conc...Effect of lintel and lintel band on the global performance of reinforced conc...
Effect of lintel and lintel band on the global performance of reinforced conc...
 
Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...
Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...
Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...
 
Wind damage to buildings, infrastrucuture and landscape elements along the be...
Wind damage to buildings, infrastrucuture and landscape elements along the be...Wind damage to buildings, infrastrucuture and landscape elements along the be...
Wind damage to buildings, infrastrucuture and landscape elements along the be...
 
Shear strength of rc deep beam panels – a review
Shear strength of rc deep beam panels – a reviewShear strength of rc deep beam panels – a review
Shear strength of rc deep beam panels – a review
 
Role of voluntary teams of professional engineers in dissater management – ex...
Role of voluntary teams of professional engineers in dissater management – ex...Role of voluntary teams of professional engineers in dissater management – ex...
Role of voluntary teams of professional engineers in dissater management – ex...
 
Risk analysis and environmental hazard management
Risk analysis and environmental hazard managementRisk analysis and environmental hazard management
Risk analysis and environmental hazard management
 
Review study on performance of seismically tested repaired shear walls
Review study on performance of seismically tested repaired shear wallsReview study on performance of seismically tested repaired shear walls
Review study on performance of seismically tested repaired shear walls
 
Monitoring and assessment of air quality with reference to dust particles (pm...
Monitoring and assessment of air quality with reference to dust particles (pm...Monitoring and assessment of air quality with reference to dust particles (pm...
Monitoring and assessment of air quality with reference to dust particles (pm...
 
Low cost wireless sensor networks and smartphone applications for disaster ma...
Low cost wireless sensor networks and smartphone applications for disaster ma...Low cost wireless sensor networks and smartphone applications for disaster ma...
Low cost wireless sensor networks and smartphone applications for disaster ma...
 
Coastal zones – seismic vulnerability an analysis from east coast of india
Coastal zones – seismic vulnerability an analysis from east coast of indiaCoastal zones – seismic vulnerability an analysis from east coast of india
Coastal zones – seismic vulnerability an analysis from east coast of india
 
Can fracture mechanics predict damage due disaster of structures
Can fracture mechanics predict damage due disaster of structuresCan fracture mechanics predict damage due disaster of structures
Can fracture mechanics predict damage due disaster of structures
 
Assessment of seismic susceptibility of rc buildings
Assessment of seismic susceptibility of rc buildingsAssessment of seismic susceptibility of rc buildings
Assessment of seismic susceptibility of rc buildings
 
A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...
A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...
A geophysical insight of earthquake occurred on 21 st may 2014 off paradip, b...
 
Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...
Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...
Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...
 

Recently uploaded

Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoringEmbedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
IJECEIAES
 
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
insn4465
 
132/33KV substation case study Presentation
132/33KV substation case study Presentation132/33KV substation case study Presentation
132/33KV substation case study Presentation
kandramariana6
 
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...
bijceesjournal
 
CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS
CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURSCompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS
CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS
RamonNovais6
 
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENTNATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
Addu25809
 
22CYT12-Unit-V-E Waste and its Management.ppt
22CYT12-Unit-V-E Waste and its Management.ppt22CYT12-Unit-V-E Waste and its Management.ppt
22CYT12-Unit-V-E Waste and its Management.ppt
KrishnaveniKrishnara1
 
spirit beverages ppt without graphics.pptx
spirit beverages ppt without graphics.pptxspirit beverages ppt without graphics.pptx
spirit beverages ppt without graphics.pptx
Madan Karki
 
Understanding Inductive Bias in Machine Learning
Understanding Inductive Bias in Machine LearningUnderstanding Inductive Bias in Machine Learning
Understanding Inductive Bias in Machine Learning
SUTEJAS
 
Properties Railway Sleepers and Test.pptx
Properties Railway Sleepers and Test.pptxProperties Railway Sleepers and Test.pptx
Properties Railway Sleepers and Test.pptx
MDSABBIROJJAMANPAYEL
 
Textile Chemical Processing and Dyeing.pdf
Textile Chemical Processing and Dyeing.pdfTextile Chemical Processing and Dyeing.pdf
Textile Chemical Processing and Dyeing.pdf
NazakatAliKhoso2
 
BRAIN TUMOR DETECTION for seminar ppt.pdf
BRAIN TUMOR DETECTION for seminar ppt.pdfBRAIN TUMOR DETECTION for seminar ppt.pdf
BRAIN TUMOR DETECTION for seminar ppt.pdf
LAXMAREDDY22
 
Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...
IJECEIAES
 
Certificates - Mahmoud Mohamed Moursi Ahmed
Certificates - Mahmoud Mohamed Moursi AhmedCertificates - Mahmoud Mohamed Moursi Ahmed
Certificates - Mahmoud Mohamed Moursi Ahmed
Mahmoud Morsy
 
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Sinan KOZAK
 
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELDEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
gerogepatton
 
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdfBPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
MIGUELANGEL966976
 
The Python for beginners. This is an advance computer language.
The Python for beginners. This is an advance computer language.The Python for beginners. This is an advance computer language.
The Python for beginners. This is an advance computer language.
sachin chaurasia
 
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
ISPM 15 Heat Treated Wood Stamps and why your shipping must have oneISPM 15 Heat Treated Wood Stamps and why your shipping must have one
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
Las Vegas Warehouse
 
Curve Fitting in Numerical Methods Regression
Curve Fitting in Numerical Methods RegressionCurve Fitting in Numerical Methods Regression
Curve Fitting in Numerical Methods Regression
Nada Hikmah
 

Recently uploaded (20)

Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoringEmbedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
 
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
 
132/33KV substation case study Presentation
132/33KV substation case study Presentation132/33KV substation case study Presentation
132/33KV substation case study Presentation
 
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...
 
CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS
CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURSCompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS
CompEx~Manual~1210 (2).pdf COMPEX GAS AND VAPOURS
 
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENTNATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
NATURAL DEEP EUTECTIC SOLVENTS AS ANTI-FREEZING AGENT
 
22CYT12-Unit-V-E Waste and its Management.ppt
22CYT12-Unit-V-E Waste and its Management.ppt22CYT12-Unit-V-E Waste and its Management.ppt
22CYT12-Unit-V-E Waste and its Management.ppt
 
spirit beverages ppt without graphics.pptx
spirit beverages ppt without graphics.pptxspirit beverages ppt without graphics.pptx
spirit beverages ppt without graphics.pptx
 
Understanding Inductive Bias in Machine Learning
Understanding Inductive Bias in Machine LearningUnderstanding Inductive Bias in Machine Learning
Understanding Inductive Bias in Machine Learning
 
Properties Railway Sleepers and Test.pptx
Properties Railway Sleepers and Test.pptxProperties Railway Sleepers and Test.pptx
Properties Railway Sleepers and Test.pptx
 
Textile Chemical Processing and Dyeing.pdf
Textile Chemical Processing and Dyeing.pdfTextile Chemical Processing and Dyeing.pdf
Textile Chemical Processing and Dyeing.pdf
 
BRAIN TUMOR DETECTION for seminar ppt.pdf
BRAIN TUMOR DETECTION for seminar ppt.pdfBRAIN TUMOR DETECTION for seminar ppt.pdf
BRAIN TUMOR DETECTION for seminar ppt.pdf
 
Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...
 
Certificates - Mahmoud Mohamed Moursi Ahmed
Certificates - Mahmoud Mohamed Moursi AhmedCertificates - Mahmoud Mohamed Moursi Ahmed
Certificates - Mahmoud Mohamed Moursi Ahmed
 
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024
 
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELDEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
 
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdfBPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
 
The Python for beginners. This is an advance computer language.
The Python for beginners. This is an advance computer language.The Python for beginners. This is an advance computer language.
The Python for beginners. This is an advance computer language.
 
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
ISPM 15 Heat Treated Wood Stamps and why your shipping must have oneISPM 15 Heat Treated Wood Stamps and why your shipping must have one
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
 
Curve Fitting in Numerical Methods Regression
Curve Fitting in Numerical Methods RegressionCurve Fitting in Numerical Methods Regression
Curve Fitting in Numerical Methods Regression
 

Study of surface roughness for discontinuous

  • 1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 727 STUDY OF SURFACE ROUGHNESS FOR DISCONTINUOUS ULTRASONIC VIBRATION ASSISTED ELECTRIC DISCHARGE MACHINING USING RSM Jujhar Singh1 , Sukhvir Singh2 , Gurpreet Singh3 1, 2, 3 Mechanical Engineering Department, Rayat-Bahra Institute of Engineering & Bio-Technology, Kharar, Distt. Mohali, Punjab, India jujharsingh2085@gmail.com, sukhvir.1991@gmail.com, hanspal89@gmail.com Abstract The objective of this paper is to study the effects of response parameters on the performance characteristics in the Ultrasonic vibration Assisted Electric Discharge Machining (UEDM) Process. Response Surface Methodology (RSM) is used to investigate the effect of amplitude of vibration, peak current, pulse on-time, machining time and flushing pressure. To study the proposed second order polynomial model for surface roughness (SR), a Central Composite Design (CCD) is used for the estimation of the model coefficients of five factors, which are believed to influence the SR in UEDM process. Experiments are conducted on Aluminum alloy 6063 with copper electrode. The response is modeled on experimental data by using RSM. The separable influence of individual machining parameters and the interaction between these parameters are also investigated by using analysis of variance (ANOVA). It is found that amplitude of vibration, peak current; pulse-on time, flushing pressure and most of their interactions have significant affect on SR. Keywords: Central Composite Design, SR, UEDM, RSM, Aluminum alloy (Al6063) -----------------------------------------------------------------------***--------------------------------------------------------------------- 1. INTRODUCTION In today's world, there is an essential requirement of advance materials such as high strength alloys, ceramics, fiber- reinforced composites etc. These materials are very hard in nature and are difficult to machine. To get rid from this machining problem, Electric Discharge Machining (EDM) was invented. EDM works on the principle of spark erosion, which removes the material by eroding the work piece. The main equipments of the Electric Discharge Machine are D.C. supply unit, EDM circuit, servomechanism, dielectric unit etc. There is a gap between the tool (electrode) and the work piece, which is known as spark gap. To complete the circuit the current is passed through the tool and the work piece through dielectric fluid. The current tends to break the dielectric fluid into ions, which start moving from work piece to tool. The movement of ions and electrons between tool and work piece occurs at such a high speed that it seems as a spark. This transfer of ions and electrons increases the temperature, which melts the work piece. The spark melts a small material volume on each of the electrodes. The dielectric fluid that fills the gap between the electrodes removes part of this material. The circulation of dielectric fluid and the removal of machined-debris is very difficult, especially when the hole or the cavity becomes deep, which reduces the machining efficiency due to poor circulation of the dielectric fluid from working gap. This poor flushing ends up with stagnation of dielectric and builds-up machining residues which apart from low material removal rate (MRR) also lead to short circuits and arcs. To improve the machining performance of EDM, a combined method of ultrasonic vibration and Electric Discharge Machining has been developed by some researchers. Response-surface methodology comprises a body of methods for exploring for optimum operating conditions through experimental methods. Typically, this involves doing several experiments, using the results of one experiment to provide direction for what to do next. This next action could be to focus the experiment around a different set of conditions, or to collect more data in the current experimental region in order to fit a higher-order model [1]. Shabgard et al. concluded that the regression technique is an important tool for representing the relation between machining characteristic and EDM process input parameters. The results show, that the CCD is a powerful tool for providing experimental diagrams and statistical-mathematical models, to perform the experiments appropriately and economically [2]. Asif and khan used RSM to investigate the relationships and parametric interactions between three controllable variables on the material removal rate(MRR), electrode wear rate (EWR) and SR in EDM milling of AISI 304 steel. Developed models can be used to get the desired responses within the experimental
  • 2. IJRET: International Journal of Research in Engineering and Technology __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ range[3]. Sahoo et al. demonstrated the effect of most influencing parameters on SR using RSM for different work piece materials in EDM [4]. Kanagarajan et al. developed models for the MRR and SR over the most influencing process parameters in EDM of WC/30% Co composites. The RSM methodology is used to identify the most influential parameters for maximizing metal removal rate and for minimizing the SR[5]. Shabgard and Shotorbani suggested mathematical models for relating the MRR, TWR and machining parameters. RSM approach is used to determine the relationship between various process parameters and machining criteria of FW4 welded steel[6]. Pradhan et al. used RSM method to investigate the effect of input p SR in EDM of AISI D2 tool steel. It was found that the developed models can be used effectively in prediction of responses. In this work, mathematical models have been developed for relating the SR to machining parameters like discharge current, pulse-on time and pulse varied over wide range from roughing region to nearly finishing conditions[7]. Kuppan at el. derived mathematical model for MRR and average SR of deep hole drilling of Inconel 718. The experiments were planned usin RSM was used to model the same. It revealed that MRR is more influenced by peak current and duty factor, and the parameters were optimized for maximum MRR with the desired SR value using desirability function approach [8]. Jaharah et al. investigated the machining performance such as SR, electrode wear rate and MRR with copper electrode and AISI H3 tool steel work piece and the input parameters taken are Ip, Ton, and Toff. The optimum condition for SR was obtained at low Ip, low Ton, and Toff and concluded that the Ip was the major factor effecting both the responses, MRR and SR [9]. Chiang had explained the influences of Ip, Ton, duty factor and voltage on the responses; MRR, electrodes wear ratio, and SR. The experiments were planned according t CCD and the influence of parameters and their interactions were investigated using ANOVA. A mathematical model was developed and claimed to fit and predict MRR accurately with a 95% confidence. Results show that the main two significant factors affecting the response are the Ip and the duty factor[10]. Literature reviewed shows that, the two factors on the value of the SR are the discharge current and the duty factor. Statistical models have been developed using RSM based on experimental results considering the machining parameters, viz., amplitude of vibration (A, µm), peak current (IP, A), pulse-on time (Ton, μs), machining time (Mt, min.) and flushing pressure (Pf, kgf/cm2) as independent variables. Finally, an attempt has been made to obtain optimum machining conditions with respect to SR parameter considered in the present study with the help of response optimization technique. IJRET: International Journal of Research in Engineering and Technology eISSN: __________________________________________________________________________________________ 2013, Available @ http://www.ijret.org range[3]. Sahoo et al. demonstrated the effect of most for different work piece materials in EDM [4]. Kanagarajan et al. developed influencing process parameters in EDM of WC/30% Co composites. The RSM methodology is used to identify the most influential parameters for maximizing metal removal rate and for [5]. Shabgard and Shotorbani suggested , TWR and SR to approach is used to determine the relationship between various process parameters and machining criteria of FW4 welded steel[6]. Pradhan et al. used RSM method to investigate the effect of input parameters on SR in EDM of AISI D2 tool steel. It was found that the developed models can be used effectively in prediction of responses. In this work, mathematical models have been developed for relating the SR to machining parameters like on time and pulse-off time which varied over wide range from roughing region to nearly finishing conditions[7]. Kuppan at el. derived mathematical model for MRR and average SR of deep hole drilling of Inconel 718. The experiments were planned using CCD and RSM was used to model the same. It revealed that MRR is more influenced by peak current and duty factor, and the parameters were optimized for maximum MRR with the desired SR value using desirability function approach [8]. gated the machining performance such as SR, electrode wear rate and MRR with copper electrode and and the input parameters taken are Ip, Ton, and Toff. The optimum condition for SR was concluded that the Ip was the major factor effecting both the responses, MRR and SR [9]. Chiang had explained the influences of Ip, Ton, duty factor and voltage on the responses; MRR, electrodes wear ratio, and SR. The experiments were planned according to a CCD and the influence of parameters and their interactions were investigated using ANOVA. A mathematical model was developed and claimed to fit and predict MRR accurately with a 95% confidence. Results show that the main two significant ng the response are the Ip and the duty main significant are the discharge current and the duty factor. Statistical models have been developed using tal results considering the machining parameters, viz., amplitude of vibration (A, µm), peak current s), machining time (Mt, min.) and flushing pressure (Pf, kgf/cm2) as independent variables. to obtain optimum machining conditions with respect to SR parameter considered in the present study with the help of response optimization 2. EXPERIMENTATION The method of assisting EDM process with discontinuous ultrasonic vibrations is designed and developed. The ultrasonic vibrations of 25000 Hz are achieved through developed Piezo electric transducer. A piezo EDM machine to give continuous and discontinuous ultrasonic vibrations to work piece at frequency of 25000 Hz with amplitude range of 1 µm to 10 µm. Fig- 1: Line diagram of UEDM set An ultrasonic panel is used to control the amplitude and mode (continuous and discontinuous) of vibration. In case of discontinuous vibrations, the high frequency electrical impulse from the generator to the transducer is discontinuous but interval time is adjusted in terms of frequency finally frequency is maintai the schematic diagram of the develo Figure 2 shows the circuit diagram of discontinuous vibration through piezoelectric transducer. study is Aluminum alloy 6063 with cylindrical shape (Diameter 10mm and Height 30mm). Table 1 shows the chemical composition of work Fig- 2: Circuit diagram of discontinuous vibration eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ 728 2. EXPERIMENTATION The method of assisting EDM process with discontinuous ultrasonic vibrations is designed and developed. The ultrasonic vibrations of 25000 Hz are achieved through developed Piezo- electric transducer. A piezo-electric transducer is attached with to give continuous and discontinuous ultrasonic vibrations to work piece at frequency of 25000 Hz with amplitude range of 1 µm to 10 µm. Line diagram of UEDM set-up An ultrasonic panel is used to control the amplitude and mode nd discontinuous) of vibration. In case of discontinuous vibrations, the high frequency electrical impulse from the generator to the transducer is discontinuous but interval time is adjusted in terms of frequency-adjustment and finally frequency is maintained at 25000Hz. Figure 1 shows schematic diagram of the developed UEDM set-up and Figure 2 shows the circuit diagram of discontinuous vibration through piezoelectric transducer. The work piece used for study is Aluminum alloy 6063 with cylindrical shape (Diameter 10mm and Height 30mm). Table 1 shows the chemical composition of work-piece used for study. Circuit diagram of discontinuous vibration
  • 3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 729 Table -1: Chemical Composition of Al 6063 Element % Si 0.3 Fe 0.35 Cu 0.09 Mn 0.1 Mg 0.7 Zn 0.1 Ti 0.1 Cr 0.1 Al 98.16 The experiments are conducted using the commercial Elektra EMS 5535 model Die Sinking Ram EDM. For each experiment, a separate Copper electrode (Diameter 5mm and Height 30mm) has been used. 3. DESIGN OF EXPERIMENTS The design factors, response variable as well as the methodology employed for the experimentation is described below. 3.1 Design Factor The design factors considered in the present work were amplitude of vibration, peak current, pulse-on time, machining time and flushing pressure. The selection of these five factors has been made because they are the most important and widely used by researchers in the die sinking EDM field. 3.2 Response Variables SR is the measure of the fine surface irregularities in the surface texture. These are the results of the EDM process employed to create the surface. SR is related as the arithmetic average deviation of the surface valleys and peaks expressed in micro-meters. The parameter mostly used for general SR is SR. It measures average roughness by comparing all the peaks and valleys to the mean line, and then averaging them all over the entire cut- off length. Cut-off length is the length for which the stylus is dragged across the surface. A longer cut-off length will give a more average value, and a shorter cut-off length might give a less accurate result over a shorter stretch of surface. In this work, the SR is measured by Mitutoyo surftest SJ-400. The surf test is a shop–floor type SR measuring instrument, which traces the surface of various machine parts and calculates the SR based on roughness standards, and displays the results in μm. The work piece is attached to the detector unit of the SJ- 400 which traces the minute irregularities of the work piece surface. The vertical stylus displacement during the trace is processed and digitally displayed on the display of the instrument. The surf test has a resolution varying from 0.01 μm to 0.4 μm depending on the measurement range. The roughness values are taken by averaging at least three measurements per specimen at different locations of specimens. 3.3 Factorial Design Employed Experiments were designed on the basis of design of experiments. The design finally chosen was a factorial design 24 with six central points, consequently carrying out a total of 32 experiments. Based on the CCD, experiments were conducted to develop empirical models for SR in terms of the five input variables: amplitude of vibration, peak current, pulse-on time, machining time and flushing pressure. Each input variable (factor) was varied over five levels: ±1, 0 and ±α. Table 2 shows the relationship between the machining parameters and their corresponding selected variation levels, taking into account the entire range of machine parameters. The experimental data as shown in Table 2 is utilized to obtain the relation between the EDM process parameters and the output SR. RSM utilizes statistical design of experiment technique and least-square fitting method in the model generation phase. An equation consisting of values of a dependent response variable and independent variables is derived for SR characteristics. Table -2: Process Parameters and their Levels S. No. Symbol and Units Parameters Levels (-2) (-1) (0) (+1) (+2) 1 A (µm) Amplitude of Vibration 0 2 4 6 8 2 IP (A) Peak Current 5 15 25 35 45 3 Ton (µs) Pulse-on Time 50 100 150 200 250 4 Mt (Min.) Machining Time 20 25 30 35 40 5 Pf (Kgf/cm2) Flushing Pressure 0.2 0.4 0.6 0.8 1.0
  • 4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 730 Regression equations are found out using software for statistical analysis called “Design Expert (DX-8)”. ANOVA and Fisher’s statistical test (F-test) is performed to check the adequacy of the model as well as significance of individual parameters. 4. RESPONSE SURFACE METHDOLOGY Response surface methodology is a collection of mathematical and statistical techniques that are useful for modelling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize the response. Response Surface Method adopts both mathematical and statistical techniques [11,12]. RSM helps in analyzing the influence of the independent variables on a specific dependent variable (response) by quantifying the relationships amongst one or more measured responses and the vital input factors. The first step in RSM is to find a suitable approximation for the true functional relationship between response of interest ‘y’ and a set of controllable variables {x1, x2, ……xn}. Usually when the response function is not known or non-linear, a second order model is utilized [13] in the form: Y= r 2 2ji jiij k 1i 2 iii k 1i ii exxbxbxbb ±+++ ∑∑∑ =<== o ….1 Where, ε represents the noise or error observed in the response y such that the expected response is (y -ε ) and b’s are the regression coefficients to be estimated. The least square technique is being used to fit a model equation containing the input variables by minimizing the residual error measured by the sum of square deviations between the actual and estimated responses. The calculated coefficients or the model equations however need to be tested for statistical significance and thus the test is performed. 4.1 Significance Test of the Regression Model Analysis of Variance (ANOVA) is used to check the adequacy of the model for the responses in the experimentation. ANOVA calculates the F-ratio, which is the ratio between the regression mean square and the mean square error. The F- ratio, also called the variance ratio, is the ratio of variance due to the effect of a factor (the model) and variance due to the error term. This ratio is used to measure the significance of the model under investigation with respect to the variance of all the terms included in the error term at the desired significance level (α). If the calculated value of F-ratio is higher than the tabulated value of F-ratio for roughness, then the model is adequate at desired α level to represent the relationship between machining response and the machining parameters. 5. EXPERIMENTAL RESULTS Table 3 shows the design matrix developed for the proposed model as well as the machining characteristics values obtained in the experiments for SR. 5.1 Model Adequacy Test for SR The ANOVA and Fisher's statistical test (F- test) were performed to check the adequacy of the model as well as the significance of individual parameters. Table 4 shows the pre- ANOVA model summary statistics for SR. It can be seen that standard deviation of quadratic model is 0.49, which is much better as compared with lower order model for R- squared. Hence the quadratic model suggested is most appropriate. Table 5 shows the variance analysis results of the proposed model of SR. The ANOVA Table includes Sum of Squares (SS), Degrees of Freedom (DF), Mean Square (MS), F-value and P- value. The MS was obtained by dividing the SS of each of the sources of variation by the respective DF. The P-value is the smallest level of significance at which the data are significant. The F-value is the ratio of MS of the model terms to the MS of the residual. In this analysis, insignificant model terms were eliminated to adjust the fitted mathematical model. As seen from Table 5, the P-values for developed model of SR is less than 0.0001, which indicates that model is significant at 92% confidence level. Table -3: Experimental conditions and results of response characteristics for SR Std. Order Run order Factor A Factor B Factor C Factor D Factor E Response Amplitude of Vibration (µm) Peak Current (A) Pulse-on time (µs) Time of Machining (min.) Flushing Pressure (kg/cm2) Surface Roughness (µm) 1 15 2 15 100 25 0.8 7.69 2 32 6 15 100 25 0.4 10.63 3 4 2 35 100 25 0.8 10.1 4 6 6 35 100 25 0.4 10 5 26 2 15 200 25 0.8 10.38
  • 5. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 731 6 24 6 15 200 25 0.4 11.85 7 9 2 35 200 25 0.8 9.5 8 20 6 35 200 25 0.4 9.42 9 27 2 15 100 35 0.8 10.89 10 25 6 15 100 35 0.4 9.31 11 22 2 35 100 35 0.8 10.9 12 19 6 35 100 35 0.4 9.97 13 29 2 15 200 35 0.8 9.76 14 10 6 15 200 35 0.4 10.71 15 16 2 35 200 35 0.8 10.37 16 17 6 35 200 35 0.4 12.63 17 5 0 25 150 30 0.6 11.47 18 30 8 25 150 30 0.6 12.67 19 12 4 5 150 30 0.6 6.52 20 11 4 45 150 30 0.6 7.02 21 28 4 25 50 30 0.6 10.95 22 23 4 25 250 30 0.6 11.65 23 21 4 25 150 20 0.6 10.35 24 31 4 25 150 40 0.6 11.13 25 7 4 25 150 30 0.2 10.39 26 18 4 25 150 30 1.0 11.67 27 2 4 25 150 30 0.6 9.37 28 13 4 25 150 30 0.6 9.38 29 3 4 25 150 30 0.6 8.45 30 8 4 25 150 30 0.6 9.67 31 1 4 25 150 30 0.6 9.56 32 14 4 25 150 30 0.6 9.78 Table -4: Model summary Statistics for SR Model summary Statistics Source Standard Deviation R-Squared Adjusted R- Squared Predicted R-Squared PRESS Linear 1.45 0.1021 -0.0705 -0.3993 85.13 2FI 1.60 0.3259 -0.3061 -0.5057 91.60 Quadratic 0.49 0.9558 0.8754 0.3309 40.70 Suggested Cubic 0.54 0.9710 0.8500 -10.1824 680.29 Aliased Table -5: ANOVA for quadratic model of Surface roughness Source Sum of Squares DOF Mean square F-Value P-Value Prob> F Model 55.91 16 3.49 10.65 <0.0001 Significant A-Amplitude of vibration 2.24 1 2.24 6.82 0.0196 B-Peak Current 0.30 1 0.30 0.91 0.3564 C-Pulse-on Time 1.78 1 1.78 5.42 0.0344 D-Machining Time 1.78 1 1.78 5.42 0.0344
  • 6. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 732 E-Flushing Pressure 0.12 1 0.12 0.38 0.5468 AD 0.78 1 0.78 2.37 0.1442 AE 3.02 1 3.02 9.20 0.0084 BD 1.40 1 1.40 4.26 0.0567 BE 3.21 1 3.21 9.79 0.0069 CE 2.68 1 2.68 8.17 0.0120 DE 0.29 1 0.29 0.88 0.3629 A2 10.79 1 10.79 32.90 <0.0001 B2 15.14 1 15.14 46.14 <0.0001 C2 5.03 1 5.03 15.33 0.0014 D2 2.20 1 2.20 6.72 0.0204 E2 3.52 1 3.52 10.74 0.0051 Residual 2.69 11 0.24 Lack of Fit 1.55 6 0.26 1.66 0.3006 Not Significant Pure Error 1.44 5 0.23 Cor Total 60.84 31 It was noted that MS of the model (3.49) is many times larger than MS of the residual (0.24), thus the computed F-value of the model (F=3.49/0.24) of 14.54 implies that the model is significant. Table 6 shows the R-Squared (R2), "Adjusted R-Squared (Adj. R2)" and "Predicted R- Squared (Pred. R2)" statistics. The R-Squared is defined as the ratio of variability explained by the model to the total variability in the actual data and is used as a measure of the goodness of fit. The more R2 approaches unity, the better the model fits the experimental data. For instance, the obtained value of 0.9191 for R2 in the case of SR (Table 6) implies that the model explains variations in the SR to the extent of 91.91% in the current experiment and thus the model is adequate to represent the process. The "Predicted R2" of 0.5433 is in reasonable agreement with the "Adjusted R2" of 0.8328 because the difference between the adjusted and predicted R2 is within 0.28 as recommended for model to be adequate. The value of Pred. R2" of 0.5433 indicates the prediction capability of the regression model. It means that the model explains about 54.33% of the variability in predicting new observations as compared to the 91.91% of the variability in the original data explained by the least square fit. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of 15.697 indicates an adequate signal. Thus, the overall prediction capability of the model based on these criteria seems very satisfactory. Table -6: Post ANOVA Model adequacy for SR R-Squared 0.9191 Adj R-Squared 0.8328 Pred R-Squared 0.5433 Adeq Precision 15.697 6. ANALYSIS AND DISCUSSION OF RESULTS The set of experiments is designed and conducted by employing RSM using discontinuous nature of vibration. The selection of appropriate model and the development of response surface models have been carried out by using statistical software “Design Expert (DX-8)”. The regression equations for the selected model are obtained for the response characteristics, viz., SR. These regression equations are developed using the experimental data presented in Table 3. The response surfaces are plotted to investigate the effect of input process parameters, amplitude of vibration, peak current, pulse-on time, machining time and flushing pressure together
  • 7. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 733 with their second order interaction on the response characteristics. The Analysis of Variance (ANOVA) is performed to statistically analyze the results. 6.1 Selection of Adequate Model Table 7 displays different tests to select the adequate model to be fit for SR. The “sequential model sum of squares” test in each Table7 shows how the terms of increasing complexity contribute to the model. It can be observed that for all the responses/characteristics, the quadratic model is appropriate [because Prob>F Value, 0.0001 (Table 7), is less than 0.05]. The addition of cubic terms does not significantly improve the lack of fit because these terms are aliased for CCD (even if these were significant). The “lack of fit” test compares the residual error to the pure error from the replicated design points. Table -7: Selection of Adequate Model for surface roughness 1 Sequential Model sum of Squares Source Sum of Squares DOF Mean Square F-value Prob > F Mean 3283.34 1 3283.34 Linear 6.21 5 1.24 0.59 0.7065 2FI 13.61 10 1.36 0.53 0.8441 Quadratic 38.32z 5 7.66 31.33 <0.0001 Suggested Cubic 0.92 5 0.18 0.63 0.6867 Aliased Residual 1.77 6 0.29 Total 3344.17 32 104.51 2 Lack of Fit Tests Source Sum of Squares DOF Mean Square F-Value Prob > F Linear 53.48 21 2.55 11.16 0.0070 2FI 39.87 11 3.62 15.89 0.0034 Quadratic 1.55 6 0.26 1.13 0.4555 Suggested Cubic 0.63 1 0.63 2.74 0.1587 Aliased Pure Error 1.14 5 0.23 3 Model summary Statistics Source Standard Deviation R-Squared Adjusted R- Squared Predicted R- Squared PRESS Linear 1.45 0.1021 -0.0705 -0.3993 85.13 2FI 1.60 0.3259 -0.3061 -0.5057 91.60 Quadratic 0.49 0.9558 0.8754 0.3309 40.70 Suggested Cubic 0.54 0.9710 0.8500 -10.1824 680.29 Aliased
  • 8. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 734 The Table 7 indicates that the quadratic model in all the response characteristics does not show significant lack of fit, hence the adequacy of quadratic model is confirmed. Another test “model summary statistics” given in the following Table further confirms that the quadratic model is the best to fix as it exhibits low standard deviation, high “R-Squared” values, and a low “PRESS” (Adeq Precision). fLN = N–1 …2 Where, fLN = Total degrees of freedom of an OA LN = OA designation N = Number of trials The regression coefficients of the second order equation (Equation 3) are obtained by using the experimental data in Table 3. The regression equations for the response characteristic as a function of five process parameters considered in this experiment are given below. The insignificant coefficients (identified from ANOVA) have been omitted from the equation. SR = +33.28–1.24*A+0.15*IP–0.06*Po–0.73*Mt– 30.15*Pf–8.21E–003*A*IP+2.66E–003*A*Ton– 0.02*A*M+1.08*A*Pf–4.03E–004*IP*Ton+5.91E– 003*P*Mt+0.22*IP*Pf–8.25E– 005*Ton*Mt+0.04*Ton*Pf+0.13*Mt*Pf+0.15*A2– 7.18E–003*IP2+1.65E– 004*Ton 2+0.01*Mt 2+8.66*Pf 2 3 Where A: Amplitude of vibration, IP: Peak Current, Ton: Pulse-on Time, Mt: Machining Time, Pf: Flushing Pressure. Using this equation, the response surfaces have been plotted to study the effect of process parameters on the performance characteristics. Figure 3 shows the combined effect of amplitude of vibration and peak current on SR. At the lower value of peak current against amplitude of vibration, the SR is less but it increases with increase in the peak current. SR decreases with increase in the amplitude of vibration with respect to peak current up to a certain level. This happens due to intensity of vibration, as amplitude of vibration at its lower level allows less debris to get removed and at higher level, the ideal time is more which result in increased surface roughness. Fig -3: Response Surface for the Combined Effect of Amplitude of Vibration and Peak Current on SR Figure 4 shows the combined effect of amplitude of vibration and pulse-on time on SR. The amplitude of vibration and pulse on-time have similar effect on SR..SR is minimum at the average value of both parameter. The response increases when the value of both factors increase, as with the increased amplitude of vibration, more craters are created at the work piece and with increased pulse on time as lesser time is available to eject the eroded material from working zone. Fig -4: Response Surface for the Combined Effect of Amplitude of Vibration and Pulse-on Time on SR Figure 5 shows the combined effect of amplitude of vibration and machining time on SR. Lower value of machining time and amplitude of vibration cause lesser SR while SR increases with their increasing values, as the machining time is increased, deeper craters are formed, which result in rougher surfaces. The maximum value of response occurs at the lower value of machining time and higher value of amplitude of vibration. 15.00 20.00 25.00 30.00 35.00 2.00 3.00 4.00 5.00 6.00 8 8.5 9 9.5 10 10.5 SurfaceRoughness A: Amplitude of Vibration B: Peak Current 100.00 125.00 150.00 175.00 200.00 2.00 3.00 4.00 5.00 6.00 8 9 10 11 12 SurfaceRoughness A: Amplitude of Vibration C: Pulse-on Time
  • 9. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 735 Fig -5: Response Surface for the Combined Effect of Amplitude of Vibration and Machining Time on SR Figure 6 shows the combined effect of amplitude of vibration and flushing pressure on SR. At higher values of flushing pressure and amplitude of vibration, SR is much more as compared to the any other values.. On the lower value of amplitude of vibration and increasing value of flushing pressure the SR decrease while at the higher value of amplitude of vibration with increasing value of flushing pressure the SR increase. The SR is higher at lower and higher value of amplitude of vibration with respect to flushing pressure, while SR decreases at its average value. Fig -6: Response Surface for the Combined Effect of Amplitude of Vibration and Flushing Pressure on SR Figure 7 shows the combined effect of peak current and pulse- on time on SR. With rise in peak current, SR increases up to a certain level and then decreases. At lower value of peak current and increasing value of pulse on time, SR is much lower then any other level. The response is maximum at higher value of pulse on time and average value of peak current. The pulse on-time causes the increased size of spark crater with aid of peak current which results in increased SR. Fig -7: Response Surface for the Combined Effect of Peak Current and Pulse-on Time on SR Figure 8 shows the combined effect of peak current and machining time on SR. The SR decreases when the machining time is maximum and peak current is minimum or vice versa. The SR is maximum at average value of peak current and higher value of machining time. The higher machining time and peak current gave less time for the removal of debris which results in increase in SR. Fig -8: Response Surface for the Combined Effect of Peak Current and Machining Time on SR Figure 9 shows the combined effect of peak current and flushing pressure on SR. The increase in peak current with respect to flushing pressure increases the SR. The SR is lesser when the flushing pressure is increasing and peak current is minimum. When the peak current increases, the size of crater increases which causes more erosion of the work piece. This eroded work piece can only be removed with higher flushing pressure otherwise the SR would increase. 25.00 27.00 29.00 31.00 33.00 35.00 2.00 3.00 4.00 5.00 6.00 8 9 10 11 12 13 SurfaceRoughness A: Amplitude of Vibration D: Machining Time 0.40 0.50 0.60 0.70 0.80 2.00 3.00 4.00 5.00 6.00 8 9 10 11 12 13 SurfaceRoughness A: Amplitude of Vibration E: Flushing Pressure 100.00 125.00 150.00 175.00 200.00 15.00 20.00 25.00 30.00 35.00 8 9 10 11 12 13 SurfaceRoughness B: Peak Current C: Pulse-on Time 25.00 27.00 29.00 31.00 33.00 35.00 15.00 20.00 25.00 30.00 35.00 8 9 10 11 12 13 SurfaceRoughness B: Peak CurrentD: Machining Time
  • 10. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 736 Fig -9: Response Surface for the Combined Effect of Peak Current and Flushing Pressure on SR Figure 10 shows the combined effect of pulse-on time and machining time on SR. The SR is maximum at higher values of pulse on time and machining time. The response is minimum at the average values of both parameters. The pulse on-time and machining time collectively effect the SR because machining time is totally dependant on pulse on-time. Fig -10: Response Surface for the Combined Effect of Pulse- on Time and Machining Time on SR Figure 11 shows the effect of pulse-on time and flushing pressure on SR. SR increases at lower and higher levels of both parameters collectively. SR decreases at minimum value of pulse on time and higher value of flushing pressure or vice versa. Pulse on time allows the current to erode the work piece by heating it. The higher flushing pressure quench the work piece while the lower did not remove debris properly, which result higher SR at their higher and lower level. Fig -11: Response Surface for the Combined Effect of Pulse- on Time and Flushing Pressure on SR Figure 12 shows the combined effect of machining time and flushing pressure on SR. The SR is maximum when the values of both the parameters are at higher level. With increase in flushing pressure with respect to machining time, the SR decreases. The increase in machining time with flushing pressure causes, firstly reduction and then increase in SR. The more flushing pressure with less machining time cause proper elimination of debris which result in lower SR. Fig -12: Response Surface for the Combined Effect of Machining Time and Flushing Pressure on SR CONCLUSIONS Using Al6063 as work piece, the effects on SR is found by five parameters i.e. amplitude of vibration, pulse-on time, peak current, machining time and flushing pressure by using RSM. It is concluded that all the five parameters have their different effect on SR when used with each other. The intensity of SR may be reduced by increasing flushing pressure as this leads to easy removal of debris. Also, SR increases with increase in amplitude of vibration, peak current and pulse on-time. 0.40 0.50 0.60 0.70 0.80 15.00 20.00 25.00 30.00 35.00 8 9 10 11 12 13 SurfaceRoughness B: Peak Current E: Flushing Pressure 25.00 27.00 29.00 31.00 33.00 35.00 100.00 125.00 150.00 175.00 200.00 8 9 10 11 12 13 SurfaceRoughness C: Pulse-on TimeD: Machining Time 0.40 0.50 0.60 0.70 0.80 100.00 125.00 150.00 175.00 200.008 9 10 11 12 13 SurfaceRoughness C: Pulse-on Time E: Flushing Pressure 0.40 0.50 0.60 0.70 0.80 25.00 27.00 29.00 31.00 33.00 35.00 8 9 10 11 12 13 SurfaceRoughness D: Machining Time E: Flushing Pressure
  • 11. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 737 So, it is concluded that at lower values of amplitude of vibration, peak current & pulse on-time and higher value of flushing pressure leads to reduction in SR. REFERENCES [1]. Russell V. Lenth, “Response-Surface Methods in R, Using RSM,” Journal of Statistical Software, Vol. 32 (7), 2009 [2]. M.R. Shabgard and R.M. Shotorbani, “Mathematical Modeling of Machining Parameters in Electrical Discharge Machining of FW4 Welded Steel,” World Academy of Science, Engineering and Technology, Vol. 52 (63), pp. 403-409, 2009 [3]. K. M. Iqbal Asif and Ahsan Ali Khan, “Modeling and analysis of MRR, EWR and Surface Roughness in EDM Milling through Response Surface Methodology,” American Journal of Engineering and Applied Sciences, Vol. 3 (4), pp. 611-619, 2010 [4]. P. Sahoo, Routara B.C. and A. Bandyopadhyay, “Roughness modeling and optimization in EDM using response surface method for different work piece materials,” International Journal of Machining and Machinability of Materials, Vol. 5, (2-3), pp. 321- 346, 2009. [5]. D. Kanagarajan, R. Karthikeyan, K. Palanikumar and P. Sivaraj, “Influence of process parameters on electric discharge machining of WC30%Co composites,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 222, pp. 807-815, 2008. [6]. M.R. Shabgard and R.M. Shotorbani, “Mathematical Modeling of Machining Parameters in Electrical Discharge Machining of FW4 Welded Steel” World Academy of Science, Engineering and Technology, Vol. 52 (63), pp. 403-409, 2009. [7]. M.K. Pradhan and C.K. Biswas, “Modeling & analysis of process parameters on surface roughness in EDM of AISI D2 tool steel by RSM approach,” International Journal of Mathematical, Physical and Engineering Sciences, Vol. 3 (1), pp. 66-71, 2009 [8]. P. Kuppan, A. Rajadurai, and S. Narayanan, “Influence of EDM process parameters in deep hole drilling of inconel 718,” International Journal of Advance Manufacturing Technology, Vol. 38, pp. 74–84, 2007. [9]. Jaharah, C. Liang, A. Wahid, S.Z., M. Rahman, and C. Che Hassan, “Performance of copper electrode in electical discharge machining of AISI H13 harden steel,” International Journal of Mechanical and Materials Engineering, Vol. 3 (1), pp. 25–29, 2008. [10]. K. Chiang, “Modeling and analysis of the effects of machining parameters on the performance characteristics in the edm process of Al2O3+tic mixed ceramic,” International Journal of Advanced Manufacturing Technology, Vol. 37 (5- 6), pp. 523–533, 2008. [11]. D.C.Montgomery, “Design and Analysis of Experiments,” Wiley-India, 2007. [12]. Myers, D.C. Montgomery and Anderson-Cook, “Response Surface Methodology,” John Wiley-New York, 2009. [13]. D. C. Montgomery, “Design and analysis of experiments,” John wiley and Sons Inc., 2001. BIOGRAPHIES Dr. Jujhar Singh has completed his Ph.D. in 2012 from PEC University of Technology, Chandigarh, he received his B.Tech. (Mechanical Engineering) in 2000 from Guru Nanak Dev Engineering College, Ludhiana, India and M.E. (Rotodynamic Machines) in 2004 from PEC University of Technology, Chandigarh, India. He has 12 years of experience in teaching and research. His area of research interest includes modeling and analysis and optimization of manufacturing processes. He has published more than 11 research papers in international Journal/Conferences. He is life member of ISTE. Currently he is working as Associate Professor and Vice-Principal with Rayat and Bahra Institute of Engineering and Bio-Technology, Kharar, Punjab, India. Sukhvir Singh is a student of M.Tech (Regular) in Rayat and Bahra Institute of Engineering and Bio-Technology, Kharar, Punjab, India. He received his B.Tech (Mechanical Engineeering) from Rayat and Bahra Institute of Engineering and Bio- Technology, Kharar, Punjab, India in the year 2012. His area of research interest includes optimization of machining processes. Gurpreet Singh is a student of M.Tech (Regular) in Rayat and Bahra Institute of Engineering and Bio-Technology, Kharar, Punjab, India.. He received his degree in Mechanical Engineering in 2012 from Rayat and Bahra Institute of Engineering and Bio-Technology, India, which is affiliated to the Punjab Technical University, India. He has been a dedicated, consistent and hardworking student throughout his studies. He has keen interest in the machining processes with a main focus on the optimization techniques involved in it.