NITW.ppt

Analysis of Process parameters in
WEDM

STATEMENT OF PURPOSE
 Inconel 718 is a high strength, temperature resistant (HSTR) nickel-
based super alloy. It is extensively used in aerospace applications,
such as gas turbines, rocket motors and spacecraft as well as in
nuclear reactors, pumps and tooling. Inconel 718 is difficult to
machine, because of its poor thermal properties, high toughness,
high hardness, high work hardening rate, presence of highly
abrasive carbide particles and strong tendency to weld to the tool to
form build up edge
 In today’s era of advanced manufacturing technology, the world
requires a unique and exact methodology for machining nickel-
based super alloys. There can be many solutions to this, each
having their own pros and cons. One of them is spark erosion
technology

 Inconel is a high strength, temperature resistant (HSTR) nickel-
based super alloy. It is extensively used in aerospace applications,
such as gas turbines, rocket motors and spacecraft as well as in
nuclear reactors, pumps and tooling. Inconel 718 is difficult to
machine, high toughness, high hardness, high work hardening rate,
presence of highly abrasive carbide particles and strong tendency to
weld to the tool to form build up edge However, a comprehensive
characterization of machining and material mechanical properties for
a wide range of speeds is lacking in the literature. Various other
input and output parameters need to be considered. Even though,
number of techniques to study this process is proposed in the
literature, multi-view approaches using soft computing techniques
have become popular based on their accuracy. This is a very
important topic and has industrial application if the optimum
parameters are developed.

Research Objectives
The extensive objective of this work is Wire-Electric Discharge
Machining of Inconel and study of various parameters. The
objectives of the study are:-
 To investigate the significant WEDM parameters that effects on the
process performance noted as Material Removal Rate (MRR),
Electrode wear ratio, (EWR) also the effect on hardness of the
material to be focused.
 To study the effects of process parameters on dimensional output of
the holes like circularity, cylindricity, Overcut and taper.
 To establish the optimum Wire-EDM parameters for Inconel
material.
 To develop the empirical model for the WEDM process using
Taguchi’s method of Design of Experiment (DOE).
 A cost effective solution using unconventional machining methods
such as the Wire Electrical Discharge Machining (WEDM) process
for exotic materials which can be machined with the ability to
produce complicated shapes.

Contents
 Introduction
 About EDM
 Influencing Parameters
 Equation
 Research Methodology(Proposed)
 Literature Review
 Common observations
 References

Introduction about EDM:
Electric discharge machining (EDM) is a non-traditional
manufacturing process that uses electric spark
discharge for machining of electrically conductive
materials. Material is removed from the specimen by a
series of recurring current discharge between two
electrodes i.e. one is work piece and the other is tool.
The important process parameters in the techniques
are discharge pulse on time, discharge pulse off time
and supplied heat flux in terms of current,voltage, wire
feed and wire Tension. EDM is a wide spread technique
used to get high metal removal rate, low tool wear rate
with high quality surface finish.

IMPORTANCE OF WEDM PROCESS IN
PRESENT DAY MANUFACTURING :
Wire electrical discharge machining (WEDM) technology has
grown tremendously since it was first applied more than 40
years ago. In 1974, D.H. Dulebohn applied the opticalline
follower system to automatically control the shape of the
components to be machined by the WEDM process. By 1975,
its popularity rapidly increased, as the process and its
capabilities were better understood by the industry. It was
only towards the end of the 1970s, when computer numerical
control (CNC) system was initiated into WEDM, which brought
about a major evolution of the machining process (Ho et. al.,
2004).

Its broad capabilities have allowed it to encompass the
production, aerospace and automotive industries and virtually
all areas of conductive material machining. This is because
WEDM provides the best alternative or sometimes the only
alternative for machining conductive, exotic, high strength
and temperature resistive materials, conductive engineering
ceramics with the scope of generating intricate shapes and
profiles (Kozak et.al., 2004 and Lok and Lee, 1997).
WEDM has tremendous potential in its applicability in the
present day metal cutting industry for achieving a
considerable dimensional accuracy, surface finish and
contour generation features of products or parts. Moreover,
the cost of wire contributes only 10% of operating cost of
WEDM process. The difficulties encountered in the die sinking
EDM are avoided by WEDM, because complex design tool is
replaced by moving conductive wire and relative movement of
wire guides.

MECHANISM OF MATERIAL REMOVAL IN
WEDM PROCESS:
The mechanism of metal removal in wire electrical
discharge machining mainly involves the removal of
material due to melting and vaporization caused by the
electric spark discharge generated by a pulsating direct
current power supply between the electrodes.
In WEDM, negative electrode is a continuously moving
wire and the positive electrode is the work piece. The
sparks will generate between two closely spaced
electrodes under the influence of dielectric liquid. Water
is used as dielectric in WEDM, because of its low
viscosity and rapid cooling rate (Lok and Lee, 1997).

No conclusive theory has been established for the complex
machining process.However, empirical evidence suggests
that the applied voltage creates an ionized channel between
the nearest points of the work piece and the wire electrodes in
the initial stage. In the next stage the actual discharge takes
place with heavy flow of current and the resistance of the
ionized channel gradually decreases. The high intensity of
current continues to further ionize the channel and a powerful
magnetic filed is generated. This magnetic field compresses
the ionized channel and results in localized heating. Even with
sparks of very short duration, the temperature of electrodes
can locally rise to very high value which is more than the
melting point of the work material due to transformation of the
kinetic energy of electrons into heat. The high energy density
erodes a part of material from both the wire and work piece by
locally melting and vaporizing and thus it is the dominant
thermal erosion process.

Application :
 The present application of WEDM process includes
Automotive,
Aerospace,
Mould,
Tool and Die making industries.
Medical, dental
optical
jewellery industries etc
Without WEDM, the fabrication process requires many hours of electrodes
fabrication for the conventional EDM technique, as well as many hours of
manual grinding and polishing.
With WEDM the overall fabrication time is reduced by 37%, however, the
processing time is reduced by 66%.

LITERATURE REVIEW
 Scott et. al. (1991) developed
mathematical models to predict material
removal rate and surface finish while
machining D-2 tool steel at different
machining conditions. It was found that
there is no single combination of levels of
the different factors that can be optimal
under all circumstances.

LITERATURE REVIEW (Contd.)
 Tarng et. al. (1995) formulated a neural
network model and simulated annealing
algorithm in order to predict and optimize
the surface roughness and cutting velocity
of the WEDM process in machining of
SUS-304 stainless steel materials.

 Spedding and Wang (1997) attempted to
model the cutting speed and surface
roughness of EDM process through the
response-surface methodology and
artificial neural networks (ANNs). The
authors attempted further to optimize the
surface roughness, surface waviness and
used the artificial neural networks to
predict the process performance.

 Liao et. al. (1997) performed an experimental
study using SKD11 alloy steel as the workpiece
material and established mathematical models
relating the machine performance like MRR,
SR and gap width with various machining
parameters and then determined the optimal
parametric settings for WEDM process
applying feasible-direction method of non-
linear programming.

 Shajan Kuriakose et al. (2004) studied the Characteristics of
wire-electro discharge machined Ti6Al4V surface. They studied
the nature of surface produced. It is observed that more
uniform surface characteristics are obtained with coated wire
electrode.
 Ozdemir and Ozek (2006) investigated the machinability of
standard GGG40 nodular cast iron by A300 Fine Sodick Mark XI
WEDM using different parameters. The increase in surface
roughness and cutting rate clearly followed the trend indicated
with increasing discharge energy as a result of an increase in
removal rate, surface roughness, and wire wear ratio for the
WEDM process could be improved by setting the various process
parameters at their optimal levels.

 Sarkar et. al. (2005) performed experiments using
γ-titanium aluminide alloy as work material and then
formulated mathematical models to predict the
cutting speed, surface finish and dimensional
deviation as the function of different control
parameters. They determined the optimal process
parameters by applying constrained optimization
technique in which one performance characteristic
was optimized considering others as constraints.

 Kuriakose and Shunmugam (2005) used titanium alloy
(Ti-6Al-4V) as the work material and conducted
experiments based on Taguchi‟s L-18 orthogonal
array. Then they employed the nondominated sorting
genetic algorithm to determine the optimal process
parameters that would optimize the cutting velocity
and SR of WEDM process.
 Chiang and Chang (2006) presented an approach for
the optimization of the WEDM process of Al2O3
particle reinforced material with two performance
characteristics, e.g. SR and MRR, based on the grey
relational analysis.

 Ramakrishnan and Karunamoorthy (2006) considered
three response characteristics, e.g. MRR, SR and wire
wear ratio (WWR) for a WEDM process and
determined the optimal process settings by
optimization of multiple response signal-to-noise
(MRSN) ratio, which is the logarithmic
transformation of the sum of the weighted
normalized quality loss of individual response variable.
 Manna and Bhattacharyya (2006) established
mathematical models relating to the machining
performance criteria like MRR, SR, spark gap and gap
current using the Gauss elimination method for
effective machining of Al/SiC-MMC.

 Mahapatra and Patnaik (2006) conducted
experiments on ROBOFIL 100 high precision 5 axis
CNC WEDM to find the relationship between control
factors and responses like MRR, SF and kerf by
means of nonlinear regression analysis. Genetic
algorithm was employed to optimize the wire
electrical discharge machining process with multiple
objectives. The error associated with MRR, SF, and
kerf were 3.14%, 1.95%, and 3.72%, respectively. The
optimum search of machining parameter values for
maximizing MRR and SF and minimizing kerf was
formulated as a multi-objective, multivariable, non-
linear optimization problem.

 Hargrove and Ding (2006) applied finite element method (FEM)
to determine work piece temperature for different cutting
parameters. They investigated the effect of WEDM parameters
such as discharge voltage and pulse on-time on the damaged
layer thickness of a machined work piece using low carbon steel
(AISI 4340) as the cutting material. The thickness of the
temperature affected layers for different cutting parameters
was computed based on a critical temperature value. Through
minimizing the thickness of the temperature affected layers
and satisfying a certain cutting speed, a set of the cutting
process parameters was determined for work piece
manufacture. A set of optimum parameters for this machining
process was selected such that the condition of machine cutting
speed was 1.2 mm/min, on time pulse was 8 μs and no load voltage
was 4 volt. The analyzed results had a good agreement with
testing results.

 Han et al (2007) conducted experiments on WEDM
EU64 to machine alloy steel (Cr12) having thickness
of 40 mm. It was reported that the surface finish
improved by decreasing pulse duration and discharge
current. Mahapatra and Patnaik (2007) developed
relationships between various process parameters and
responses like MRR, SR and kerf by means of non-
linear regression analysis and then employed genetic
algorithm to optimize the WEDM process with
multiple objectives.

 Saha et. al. (2007) developed a second order multi-
variable regression model and a feed-forward back-
propagation neural network (BPNN) model to
correlate the input process parameters, such as pulse
on-time, pulse off time, peak current and capacitance
with the performance measures namely, cutting speed
and surface roughness in wire electro- discharge
machining (WEDM) of tungsten carbide-cobalt (WC-
Co) composite material. 4-11-2 neural network
architecture provides the best prediction capability
with 3.29% overall mean prediction error, while 6.02%
error was revealed by regression model.

 Li et. al. (2007) developed a model of WEDM with
higher forecast precision and generalization ability
which combined modeling function of fuzzy inference
with the learning ability of artificial neural network
and a set of rules were generated directly from the
experimental data. The process relation expressed by
the neural-fuzzy inference model was used directly as
the fitness function and was embedded in GA to be
optimised, and then the automatic optimization of the
wire electrical discharge machining was realized.

 Sarkar et. al. (2007) performed experimental investigation on
trim cutting of wire electrical discharge machining of γ-TiAl
alloy. The process was successfully modelled using RSM and
model adequacy checking was also carried out. WEDM process
was optimized using Minitab (statistical software package) which
generally makes use of the desirability function approach. But it
was observed that lot of trial and error and manual tuning was
required to obtain the true optimal solution. By using developed
computer program based upon pareto optimization algorithm, the
33 pareto-optimal solutions were searched out from the set of
all 6561 outputs. It was observed that the developed pareto
optimization strategy eliminates the guess work. It was also
seen that the surface quality decreases as the cutting speed
increases and varies almost linearly up to surface roughness
value of 1.22 µm and cutting speed of 13.88 mm/min. Beyond this
value of cutting speed, surface roughness deteriorates
drastically.

 Kanlayasiri and Boonmung (2007) investigated
influences of wire-EDM machining variables on
surface roughness of newly developed DC 53 die steel
of width, length, and thickness 27, 65 and 13 mm,
respectively. The machining variables included pulse-
on time, pulse-off time, pulse-peak current, and wire
tension. The variables affecting the surface
roughness were identified using ANOVA technique.
Results showed that pulseon time and pulse-peak
current were significant variables to the surface
roughness of wireEDMed DC53 die steel. The
maximum prediction error of the model was less than
7% and the average percentage error of prediction
was less than 3%.

 Ramakrishnan and Karunamoorthy (2008) developed artificial
neural network (ANN) models and multi response optimization
technique to predict and select the best cutting parameters of
wire electro-discharge machining (WEDM) process. Inconel 718
was selected as work material to conduct experiments and brass
wire of 0.25mm diameter was used as tool electrode.
Experiments were planned as per Taguchi‟s L-9 orthogonal
array. Experiments were performed under different cutting
conditions of pulse on time, delay time, wire feed speed and
ignition current. It was found that the pulse on time, delay time
and ignition current had more influence than wire feed speed on
the performance characteristics considered in the study. An
MRR was improved with increase in pulse on time and ignition
current. But the surface quality of the work specimen was
affected adversely with increased value of pulse on time and
ignition current.

 Gauri and Chakraborty (2008) suggested a modified
approach of the principal component analysis (PCA)
based procedure for multi-response optimization.
Analysis was done data on experimental data on
WEDM processes obtained by the past researchers
i.e. on γ-titanium aluminized alloy with the settings of
six controllable factors. Quality characteristics were
material removal rate (MRR) (larger the better type),
surface roughness (SR) (smaller the better type) and
wire wear ratio (WWR) (smaller the better type).
.

 Rao and Sarcar (2009) analyzed the effects of process
parameters on machining characteristics for CNC WEDM for
brass work pieces of varying thickness. Mathematical relations
were obtained for cutting speed, spark gap and MRR.
 Pradhan et. al. (2009) optimized micro-EDM process
parameters for machining Ti-6Al-4V super alloy. The influence
of machining process parameters such as peak current, pulse-on-
time, dielectric flushing pressure and duty ratio on performance
criteria like MRR, TWR, over cut and taper have been examined.
 Manna and Kumar (2009) investigated the effects of various
cutting parameters of WEDM on wire crater depth, electrode
wear rate and surface roughness using Taguchi methods based
on L-18 mixed orthogonal array.

 Shabgard et al.(2011) SVJME investigated the
Influence of Input Parameters on the Characteristics
of theEDM. They concluded that The increase in
pulse on-time leads to an increase in the material
removal rate, surface roughness, as well the white
layer thickness and depth of heat affected zone.
 Imtiaz and Rajput (2012) presented an effective
approach for Multi- objective optimization of the
process parametric combinations by modelling WEDM
process by use of artificial neural networks
(ANN).applied ANN model to predict the process
performance.

 Hayakawa, S. et al (2013) In this paper,
the flying debris particles, as well as
bubble expansion and contraction,
generated by a pulse discharge in a parallel
flat gap space are observed, and the time
when the debris particles are removed
from the discharge point is estimated in
order to discuss the material removal
mechanism in the EDM process.

 Pradhan (2013) Estimated the effect of
process parameters on surface integrity of
EDMed AISI D2 tool steel by response
surface methodology coupled with grey
relational analysis. He found that the pulse
duration was the most dominant factor for
surface integrity followed by duty factor,
pulse current, and discharge voltage.

 Pradhan et al. (2013) successfully
applied hybrid optimization in EDM.
Response Surface Methodology (RSM),
Gray Relational Analysis (GRA) and
Principal Component Analysis (PCA) used
for optimizing the Electrical Discharge
machining responses, such as material
removal rate, surface roughness, and
Radial overcut (ROC)

Danial Ghodsiyeh et al.
Review on Current Research Trends in Wi
re Electrical Discharge Machining (WEDM)
Danial Ghodsiyeh, Abolfazl Golshan,Jamal
Azimi Shirvanehdeh

 Yanzhen ZhangIn et al. (2014) investigated the
material removal characteristics in different
dielectrics. The whole geometry of craters, including
the recast material in the crater, was precisely
determined by metallographic methods. It was found
that there was a huge difference of the geometry
shape of the craters formed in different dielectrics
even with the same experiments conditions.
 Suresh et al (2014) optimized the machining
parameter for Aluminium alloy matrix. Grey relational
analysis was successfully used to predict the optimum
input parameters for achieving lower electrode wear
ratio, surface roughness, and power consumption.

Identified Gaps in Literature
 After a comprehensive study of the existing literature, a
number of gaps have been observed in machining of
WEDM.
 Literature review reveals that the researchers have
carried out most of the work on WEDM developments,
monitoring and control but very limited work has been
reported on optimization of process variables.
 The effect of machining parameters on Stainless Steel
grades AISI 304, AISI 410, hot working tool steel H21
and EN31 has not been fully explored using WEDM.
 Most of the research work on Wire EDM has been
performed by using Brass Wire alone and very less
work has been done by using other wire electrodes such
as Molybdenum.

Identified Gaps in Literature (Contd.)
 Multi - response optimization of WEDM process is
another thrust area which has been given less attention
in past studies.
 Lots of research tried to minimize the surface roughness
by different approaches. Based on the theory surface
roughness significantly affected by the pulse on time and
peak current and the cutting speed and surface
roughness have an inverse relationship. surface
roughness de- crease as the cutting speed increase the
surface roughness increases because of “double
sparking”. In the other words double sparking and
localized sparking become more frequent as the pulse
on time increases. Double sparking produces a poor
surface finish.

Research Objectives
 Achieve the maximum material removal rate
with minimum surface roughness

Research Methodology(Proposed)
Step-1
 Development of experimental set up providing
varying range of input parameters in WEDM and
measuring the various responses on-line and
off-line
 Investigation of the working ranges and the
levels of the WEDM process parameters (pilot
experiments) affecting the selected quality
characteristics, by using one factor at a time
approach

 Step-2 Investigation of the effects of WEDM process parameters on
quality characteristics viz. cutting rate, surface roughness, gap
current and dimensional deviation while machining Inconel
 Optimization of quality characteristics of machined parts:
1. Prediction of optimal sets of WEDM process parameters
2. Prediction of optimal values of quality characteristics
3. Prediction of confidence interval (95%CI)
 Experimental verification of optimized individual quality
characteristics
The Taguchi‟s parameter design approach has been used to
obtain the above objectives.

Step-3
 Development of mathematical models and response
surfaces of cutting rate, surface roughness, gap current
and dimensional deviation using response surface
methodology

Step-4
 Development of single response optimization
model using desirability function
 Development of multi objective optimization
models using desirability function
 Determination of optimal sets of WEDM process
parameters for desired combinations of quality
characteristics
 Experimental verification of quality
characteristics optimized in different
combinations

Step-5
 Development of multi objective optimization
models using Taguchi technique and utility
concept
 Determination of optimal sets of WEDM
process parameters for desired combined
quality characteristics
 Experimental verification of quality
characteristics optimized in different
combinations

MODELING AND OPTIMISATION
(i) Mathematical Models
(ii) Simulation
(iii) Fuzzy Logic
(iv) Artificial Neural Networks (ANN)
(v) Response Surfaceness Methodology (RSM)
(vi) Taguchi Technique
(vii) ANOVA

References
 Scott, D., Boyina, S., Rajurkar, K.P. (1991),“Analysis
and optimization of Parameter Combination in Wire
Electrical Discharge Machining”, International
Journal of Production Research, 29, 2189-2207.
 Tarng, Y. S., Ma, S. C., Chung, L. K. (1995),
“Determination Of optimal cutting parameters in wire
electrical discharge machining”, International Journal
of Machine Tools and Manufacture , 35, 1693-1701.
 Spedding, T. A., Wang, Z.Q. (1997), “Parametric
optimization and surface characterization of wire
electrical discharge machining process”, Precision
Engineering, 20(1), 5-15.

References
 Liao, Y.S., Huang, J.T., Su, H.C. (1997), “A Study of
machining-parameters Optimization of wire electrical
discharge machining”, Journal of Material Processing
Technology, 71 , 487-493
 Kuriakose. S, Shunmugam, M.S. (2004),
“Characteristics of wire-electro discharge machined
Ti6Al4V surface” , Materials Letters, 58, 2231–
2237.
 Ozdemir, N. and Ozek, C. (2006), “An investigation on
machinability of nodular cast iron by WEDM”,
International Journal of Advanced Manufacturing
Technology, 28, 869–872

References
 Sarkar, S., Mitra, S., Bhattacharyya, B. (2005), “Parametric
analysis and optimization of wire electrical discharge machining
of γ-titanium aluminide alloy”, Journal of Materials Processing
Technology, 159, 286–294.
 Kuriakose, S., Shunmugam, M.S. (2005), “Multi-objective
optimization of wire-electro discharge machining process by
non-dominated sorting genetic algorithm”, Journal of Materials
Processing Technology, 170,133-141.
 Chiang, K.T., Chang, F.P. (2006), “Optimization of the WEDM
process of particle-reinforced material with multiple
performance characteristics using grey relational analysis”,
Journal of Materials Processing Technology, 180, 96-101.

References
 Ramakrishnan, R. and Karunamoorthy, L. (2006), “Multi response
optimization of wire EDM operations using robust design of
experiments”, International Journal of Advanced Manufacturing
Technology, 29, 105–112.
 Manna, A. and Bhattacharyya, B. (2006), “Taguchi and Gauss
elimination method: A dual response approach for parametric
optimization of CNC wire cut EDM of PRAlSiCMMC”,
International Journal of Advanced Manufacturing Technology,
28, 67–75.
 Mahapatra, S. and Patnaik, A. (2006), “Parametric optimization
of wire electrical discharge machining (wedm) process using
Taguchi method”, J. of the Braz. Soc. of Mech. Sci. & Eng., 28,
422-429.

References
 Hargrove, S. K., Ding, D. (2007), “Determining cutting
parameters in wire EDM based on workpiece surface
temperature distribution”, International Journal of Advanced
Manufacturing Technology, 34, 295-299.
 Han, F., Jiang, J., Dingwen, Yu. (2007), “Influence of machining
parameters on surface roughness in finish cut of WEDM”,
International Journal of Advanced Manufacturing Technology,
34, 538–546.
 Saha, P., Singha, A., Pal, S.K., Saha, P. (2008), “Soft computing
models based prediction of cutting speed and surface roughness
in wire electro-discharge machining of tungsten carbide cobalt
composite”, International Journal of Advanced Manufacturing
Technology, 39.,74-84.

References
 Sarkar, S. , Sekh, M., Mitra, S., Bhattacharyya, B. (2007),
“Modeling and optimization of wire electrical discharge
machining of γ-TiAl in trim cutting operation”, Journal of
Material Processing Technology, 205, 376-387.
 Kanlayasiri, K., Boonmung, S. (2007), “An investigation on effects
of wire-EDM machining parameters on surface roughness of
newly developed DC53 die steel”, Journal of Materials
Processing Technology, 187–188, 26–29.
 Ramakrishnan,R. and Karunamoorthy,L. (2008), “Modeling and
multi-response optimization of Inconel 718 on machining of CNC
WEDM process”, Journal of materials processing technology, 2 0
7, 343–349.

References
 Gauri, S.K. and Chakraborty, S. (2009), “Multi-response
optimization of wedm process using principal component
analysis”, International Journal of Advanced Manufacturing
Technology, 41, 741-748.
 Rao, CVS Parameswara, and M. M. M. Sarcar. "Evaluation of
optimal parameters for machining brass with wire cut EDM."
Journal of Scientific and Industrial Research 68.1 (2009): 32.
 Pradhan, B. B., et al. "Investigation of electro-discharge micro-
machining of titanium super alloy." The International Journal of
Advanced Manufacturing Technology 41.11-12 (2009): 1094-1106.

References
 Mohammadreza Shabgard, Mirsadegh Seyedzavvar, Samad
Nadimi Bavil Oliaei, (2011) “Influence of Input Parameters on
the Characteristics of the EDM Process” SV-Journal of
Mechanical Engineering 579, 689-696
 Imtiaz Ali Khan and Tikam Singh Rajput, (2012) “Modeling of
Wire Electrical Discharge Machining of Alloy Steel (HCHCr)”,
International Journal of Precision Engineering and
Manufacturing, Vol. 13, No. 11, pp. 1989-1995

References
 Hayakawa, S., et al. "Relationship between occurrence of
material removal and bubble expansion in electrical discharge
machining." Procedia CIRP 6 (2013): 174-179.
 Pradhan, Mohan K. "Estimating the effect of process parameters
on surface integrity of EDMed AISI D2 tool steel by response
surface methodology coupled with grey relational analysis." The
International Journal of Advanced Manufacturing Technology
67.9-12 (2013): 2051-2062.
 Soni, Hargovind, T. K. Mishra, and M. K. Pradhan. "Multi-
Response Optimization of EDM Parameters by Grey-PCA
Method." International Journal of Current Engineering and
Technology 3.5 (2013): 1941-1945.

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