A Review - Status of Recent Developments & Effect of Machining Parameters on Performance Parameters in EDM

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Volume 2, Issue 1, 2015

32 Available online at www.ijiere.com

International Journal of Innovative and Emerging

Research in Engineering

e-ISSN: 2394 - 3343 p-ISSN: 2394 - 5394

A Review - Status of Recent Developments & Effect of

Machining Parameters on Performance Parameters in EDM.

Priyesh N. Santoki, Prof. Ashwin P. Bhabhor

P.G. Scholar, M.E.[Production], L.D.R.P.-ITR, Gandhinagar, Gujarat Asst. Prof. Mechanical Engineering Department, LDRP-ITR, Gandhinagar, Gujarat

ABSTRACT:

Electrical discharge machining (EDM) is non-traditional machining process, based on thermoelectric energy between the workpiece and tool. The selection of manufacturing conditions is the important thing to take into consideration in the majority of manufacturing processes and particularly, in processes related Electrical Discharge Machining (EDM). Electrical Discharge Machining performance is mainly find out on the basis of Overcut (OC), Material Removal Rate (MRR), Tool Wear Rate (TWR) and Surface Roughness (SR). The important EDM machining parameters affecting on the performance parameters are current, pulse on time, pulse off time, arc gap, flushing pressure, voltage and duty cycle. The performance of the process is also depends on the workpiece material, manufacturing and design method of the electrodes. Several optimization methods are used to optimization of performance parameters. In the view of above, this paper also presents a review of development done in the EDM & the effects of machining parameters on performance parameters with various DOE & Optimization Techniques.

Keywords: ANN, Electrical Discharge Machining, GA, GRA, MRR, OC, RSM, SR, TWR

I. INTRODUCTION

Electrical Discharge Machining (EDM) is an important manufacturing process for machining hard metals and alloys. This process is widely used for producing dies, molds, and finishing parts for aerospace, automotive, and surgical components. The process is capable of getting required dimensional accuracy and surface finish by controlling the process parameters. EDM performance is generally evaluated on the basis of Overcut (OC), Material Removal Rate (MRR), Tool Wear Rate (TWR) and Surface Roughness (SR).

The important EDM parameters affecting to the performance measures of the process are discharge current, pulse on time, pulse off time, arc gap, flushing pressure and duty cycle. In EDM, for optimum machining performance measures, it is an important task to select proper combination of machining parameters. Generally, the machining parameters are selected on the basis of operator’s experience or data provided by the EDM manufactures. When such information is used during Electrical Discharge Machining, the machining performance is not consistent. For these materials, experimental optimization of performance measures is essential. Optimization of EDM process parameters becomes difficult due to more number of machining variables and slight changes in a single parameter significantly affect the process. Optimization of EDM process parameters becomes difficult due to more number of machining variables and slight changes in a single parameter significantly affect the process. Thus, it is essential to understand the influence of various factors on EDM process. Analytical and statistical methods are used to select best combination of process parameters for an optimum machining performance. Different author use different combination of process parameters. They analyze the experimental data by plotting Interaction graphs, Residual plots for accuracy and Response curves. Some other methods used by different author for analysis of data related to Electrical Discharge Machining (EDM) are Regression analysis, Response Surface Methodology (RSM), Central Composite Design (CCD), Grey Relational Analysis (GRA), Genetic Algorithm (GA), Fuzzy clustering, Artificial Neural Network (ANN) etc. Most of the author used L9 & L27 Orthogonal Array. Generally the effect of Pulse ON time, Pulse OFF time, Spark gap set Voltage, Peak current on the Material Removal Rate, Surface Roughness, Overcut, Tool Wear Rate is investigated.

II. PRINCIPLE OF EDM

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33 major Components: (1) Computerized Numerical Control (CNC), (2) Power Supply, (3) Mechanical Section : Worktable, work stand, taper unit etc., (4) Dielectric System

Fig 1. Setup of EDM [1]

III. VARIOUS MACHINING PARAMETERS OF EDM

A. Polarity: The Polarity normally used is normal polarity in which the tool is negative and work piece is positive. Sometimes positive polarity can be used depending upon the requirement, where tool is positive and work piece is negative. The negative polarity of the work piece has an inferior surface roughness than that under positive polarity in EDM.

B. Pulse on time: Pulse on-time is the time period during which machining takes place. MRR is directly proportional to amount of energy applied during pulse on-time. The longer the on-time pulse is sustained, the more work piece material will be eroded. The resulting crater will be broader and deeper than a crater produced by a shorter on-time. These large craters will create a rougher surface finish.

C. Pulse off time: Pulse off-time is the time during which re-ionization of dielectric takes place. The discharge between the electrodes leads to ionization of the spark gap. Before another spark can take place, the medium must de-ionize and regain its dielectric strength.

D. Peak current or Discharge Current: This is the amount of power used in discharge machining, measured in units of amperage, and is the most important machining parameter in EDM. In each on-time pulse, the current increases until it reaches a preset level, which is expressed as the peak current. Higher value of peak current leads to rough surface finish operations and wider craters on work materials. Its higher value improves MRR, but at the cost of surface finish and tool wear. Hence it is more important in EDM because the machined cavity is a replica of tool electrode and excessive wear will hamper the accuracy of machining.

E. Arc gap: The Arc gap is distance between the electrode and work piece during the process of EDM. It may be called as spark gap. Spark gap can be maintained by servo system.

F. Duty cycle: It is a percentage of the on-time relative to the total cycle time. This parameter is calculated by dividing the on time by the total cycle time.

G. Voltage: It is a potential that can be measure by volt it is also effect to the material removal rate and allowed to per cycle.

IV. VARIOUS PERFORMANCE PARAMETERS OF EDM

A. Over-Cut (OC): It is the measure of cut produced exceeding the diameter of the tool. The impression created while EDM process is generally slightly larger than the original diameter of the tool electrode. This is because the spark is generated from along the side of the tool and hence erosion takes place in that direction also. OC is calculated as half the difference of the diameter of the hole produced to the tool diameter.

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34 C. Surface Roughness (SR): Surface Roughness is the measure of the texture of the surface. It is measured in μm. If the value is high then the surface is rough and if low then the surface is smooth. It is denoted by Ra. The values are measured using Portable style type profilometer.

D. Tool Wear Rate (TWR): TWR is the rate at which the material is removed from the tool. The TWR is defined as the ratio of the difference in weight of the tool before and after machining to the machining time.

V. WORKPIECE & TOOL MATERIAL USE IN EDM

A. Workpiece Material

There are different types of workpiece material are using the EDM method. It is capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc.

List of Workpiece which are used in EDM : EN31 Tool Steel, Tungstan-Carbied, V Composite, Al 7075 B4C MMC, AISI 202 SS, AISI D3 Tool Steel, H-11 Steel, H-13 Tool Steel, Hastelloy Steel, Mild Steel, AISI 1040 Medium Carbon Steel, EN19, EN9, AISI 316-L SS, NiTi60-SMA, AISI D2 Tool Steel, Ai-SiCP MMC, AISI P20 Tool Steel, Silver Steel, W300 Die Steel, AISI 4340 Steel, Titanium Super Alloy.

B. Tool Material

Tool material should be such that it would not undergo much tool wear when it is impinged by positive ions. Thus the localized temperature rise has to be less by tailoring or properly choosing its properties or even when temperature increases, there would be less melting. List of Tool Material which are used in EDM & why they are used?

1. Copper: In Europe and Japan still prefer to use Copper as the primary electrode material, due to their tool making culture that is averse to the “untidiness” of working with graphite. Due to its structural integrity, Copper can produce very fine surface finishes, even without special polishing circuits. This same structural integrity also makes Copper electrodes highly resistant to DC arcing in poor flushing situations. Copper is frequently used in reverse burning punches and cores in the Sinker EDM. Most of researchers used this material for the machining of workpiece. [1] 2. Graphite: Graphite is the preferred electrode material for 95% of all sinker EDM applications. Thus, it is important that we expend considerable effort to understand its properties and application to EDM. [29]

3. Brass: Brass is one of the first EDM electrode materials. It is economical and easy to machine. Today, brass is less used as an electrode material in modern sinker EDMs, due to its high wear rate. [24]

4. Silver: Silver is occasionally used as an electrode material, due to its superior electrical conductivity, purity, and structural integrity. The use of Silver electrodes and fine finish can produce extraordinary fine finishes in coining dies, where the use of orbiting to improve the finish would distort the cavity detail. [2]

5. Tungsten: Due to the combination of its high density, tensile strength, and melting point, Tungsten had been the electrode material of choice for certain limited EDM applications. It is important to note that Tungsten, due to its relatively poor electrical conductivity, cuts much slower than Brass or Copper. Also, due to its high cost and very low machinability, Tungsten is seldom used. [2]

6. Copper Tungsten: Copper Tungsten (CuW) is a powder metal product designed to combine the best EDM properties of Copper and Tungsten. Copper Tungsten combines the high electrical conductivity of copper with the high melting point of tungsten. The combination of these two metals creates an electrode material with very good wear properties. Copper Tungsten is unmatched for its wear resistance, holds up very well in sharp corners, and is readily machined and ground without the burr issues associated with Copper. [6] [17]

7. Silver Tungsten: Silver Tungsten is a powder metal product which combines the wear resistance of Tungsten with the high conductivity of Silver, to give an unmatched combination of low wear and fine surface finish for EDM applications with fine detail. Silver Tungsten is made by the same process as Copper Tungsen. Due to its high cost and limited availability, Silver Tungsten has a very limited range of applications. [2]

8. Tungsten Carbide: Due to its extraordinary stiffness and low wear properties, Tungsten Carbide is often the preferred electrode material. [2]

9. Aluminum: Easily Available & Economical. [22]

VI. DESIGN OF EXPERIMENT [DOE] TECHNIQUES

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35 stage, Analyzing stage. The Various DOE Techniques are Full Factorial Methods, Taguchi Method, Response Surface Method & Mixed Method. These all methods are design by MINITAB software.

VII. VARIOUS TECHNIQUES FOR OPTIMIZATION OF EDM MACHINING PARAMETERS To apply the mathematical results and numerical techniques of optimization theory to concentrate engineering problems, it is necessary to clearly delineate the boundaries of the engineering system to be optimized, to define the quantitative criterion on the basis of which candidates will be ranked to determine the” best,” to select the system variables that will be used characterize or identify candidates, and to define a model that will express a manner in which the variables are related. The studies use several optimization techniques they may be classical or numerical based and have lead to evolved techniques used in modern technical scenario. After going through the literature the major optimization techniques and tools utilized by the researchers are as follows :

A. Taguchi Method: Taguchi Method is a new engineering design optimization methodology that improves the quality of existing products and processes and simultaneously reduces their costs very rapidly, with minimum engineering resources and development man-hours. The Taguchi Method achieves this by making the product or process performance "insensitive" to variations in factors such as materials, manufacturing equipment, workmanship and operating conditions. Taguchi’s philosophy is founded on the following three very simple and fundamental concepts (Ross, 1988; Roy, 1990): Quality should be designed into the product and not inspected into it; Quality is best achieved by minimizing the deviations from the target. The product or process should be so designed that it is immune to uncontrollable environmental variables, the cost of quality should be measured as a function of deviation from the standard and the losses should be measured system-wide. [29]

B. Genetic Algorithm Technique (GA): Genetic algorithms are inspired by Darwin's theory about evolution. Solution to a problem solved by genetic algorithms is evolved. Algorithm is started with a set of solutions (represented by chromosomes) called population. Solutions from one population are taken and used to form a new population. This is motivated by a hope, that the new population will be better than the old one. Solutions which are selected to form new solutions (offspring) are selected according to their fitness - the more suitable they are the more chances they have to reproduce. Genetic algorithm possesses advantages that do not require any inherent parallelism and gradient information in searching the design space. Now it is a robust adaptive optimization technique. Some researchers investigated GA application in EDM. Long back used a multi objective optimization method, non-dominating sorting genetic algorithm-II to maximize the result of the process. This provides an optimization model based on genetic algorithms for EDM parameters to imitate a decision. Genetic algorithms find application in bioinformatics, phylogenetic, economics, computational science, engineering, chemistry, manufacturing, pharmacometrics, mathematics, physics and other fields. [9][30]

C. Response Surface Method (RSM): In statistics, Response surface methodology (RSM) investigates the interaction between several illustrative variables and one or more response variables. Box and Draper [1987] were introducing RSM in 1951.The most important proposal of RSM is to use a series of designed experiments to attain an optimal response. A second-degree polynomial model is use in RSM. These models are only an approximation, but use it because such a model is easy to estimate and apply, even when little is known about the process. The response surface methodology (RSM) is a collection of mathematical and statistical technique useful for the modeling and Analysis of problems in which a response of interest is influenced by a several variables and the objective is to optimize the response ( Montgomery, 2005). It used in the development of an adequate functional relationship between responses of interest. [10][28][30][36]

D. Grey Relational Analysis (GRA): GRA theory developed for the new methods for solving the complicated the inter relationship among the multiple performing characteristics. In grey system theory includes three types of systems first black which shows no information in this system, second white which shows all information in this system & third grey system which shows imperfect information. The grey system theory is a efficient technique, which requires a limited information to estimate the behavior of an uncertainty system & discrete data problem. If the sequences range is large, in GRA, the factors are effaceable. Although, if the measured factors are discrete, then wrong results may be produce by GRA. So, for evade this influence, must perform data preprocessing of original experimental data. The range of data processing is zero to one (0-1). Normalizing involves transforming the original sequence to comparable sequence. This is known as grey relational generating. [32]

E. Artificial Neural Network (ANN): An artificial neural network is an information-processing system that has certain performance characteristics in common with biological neural networks. Artificial neural networks have been developed as generalizations of mathematical models of human cognition or neural biology. Elements of Artificial Neural Networks: Processing Units, Topology, Learning Algorithm. [24][31]

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Feasible-Volume 2, Issue 1, 2015

36 Direction Algorithm, SA algorithm, Pareto, Artificial Bee Colony (ABC), Tabu-Search Algorithm, Principle component method, Trust region method.

VIII. EXISTING RESEARCH EFFORTS Name of

Researchers

Year Contribution Workpiece

Material Tool Material Machining Parameters Performance Parameters Shankar Singh et. al.[1]

2003 Some investigations into the electric discharge machining of Hardened Tool Steel using different electrode materials.

EN 31 Tool Steel

Copper, Copper- Tungsten, Brass

Current MRR, OC,

TWR, SR

S.H.Tomadi et. al.[2]

2009 Analysis of the influence of EDM parameters on Surface Quality, Material Removal Rate and Electrode Wear of Tungsten Carbide. Tungsten Carbide Copper Tungsten Current, Voltage, Pulse on time, Pulse off time

MRR, SR, EWR

S. Assarzadeh et. al.[3]

2013 Statistical modeling and optimization of process

parameters in Electro Discharge Machining of Cobalt-Bonded Tungsten Carbide Composite (WC/6%Co)

Tungsten Carbide Composite

Copper Current, Pulse on time, Duty Cycle, Gap Voltage MRR, TWR, SR S. Gopalakannan et. al.[4]

2012 Modeling & Optimization of EDM process parameter on machining of AL 7075-B4C MMC using RSM.

AL 7075-B4C

Copper Current, Gap Voltage, Pulse on time, Pulse off time

MRR, EWR, SR

T.M.Chenthil Jegan et. al.[5]

2012 Determination of EDM

parameters in AISI 202 SS using Gray Relation Analysis.

AISI 202 SS

Copper Current, Pulse on time, Pulse off time

MRR, SR

Nibu Mathew et. al.[6]

2014 Study of material removal rate of different Tool Materials during EDM of H11 Steel at Reverse Polarity.

H 11 Steel Copper, Copper- Tungsten Current, Duty Cycle, Gap Voltage MRR Pardeep Singh et. al.[7]

2012 Determination of best parameter setting for Overcut during Electrical Discharge Machining of H-13 Tool Steel using Taguchi Method.

H 13 Tool Steel

Copper Current, Duty Cycle, Gap Voltage

Overcut

Dinesh Kumar et. al.[8]

2011 Study of overcut during Electric Discharge Machining of Hastelloy Steel with different electrodes using the Taguchi Method. Hastelloy steel Copper, Copper- Tungsten Current, Gap Voltage, Pulse on time, Duty Cycle

Overcut

Gaurav Raghav et. al.[9]

2013 Optimization of Material Removal Rate in Electric Discharge Machining using Mild Steel.

Mild Steel Copper Current, Time SR, MRR

Singaram Lakshmanan et. al.[10]

2013 Optimization of Surface Roughness using Response Surface Methodology for EN31 Tool Steel EDM Machining.

EN 31 Tool Steel

Copper Current, Pulse on time, Pulse off time, Voltage

MRR, SR

Amit Kohli et. al.[11]

2012 Optimization of Material Removal Rate in Electrical Discharge Machining using Fuzzy Logic.

AISI 1040 Medium

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Volume 2, Issue 1, 2015 37 Carbon Steel Shashikant et. al.[12]

2014 Optimization of machine process parameters on Overcut in EDM for EN19 material using RSM.

EN 19 Copper Current, Pulse on time, Pulse off time, Gap Voltage

Overcut

Shivendra Tiwari[13]

2013 Effect of different process parameters on Over Cut in optimizing of EDM process.

Medium Carbon Steel

Copper Current, Pulse on time, Gap Voltage

Overcut

Vishal J Nadpara et. al.[14]

2014 Optimization of EDM Process Parameters Using Taguchi Method with Graphite Electrode.

AISI D3 Tool Steel

Graphite Current, Pulse on time, Duty Cycle

MRR, TWR

Hitesh b. Prajapati et. al.[15]

2013 Parametric Analysis of Material Removal Rate and Surface Roughness of Electro Discharge Machining on EN 9.

EN 9 Graphite, Copper, Brass

Current, Pulse on time, Pulse off time

MRR, SR

Navdeep Malhotra et. al.[16]

2009 Experimental study of Material Removal Rate in EDM.

EN 31 Copper Current, Pulse on time, Pulse off time, Voltage, Spark Gap

MRR

P. Janmanee

et. al.[17]

2010 Performance of difference Electrode materials in Electrical Discharge Machining of Tungsten Carbide. Tungsten Carbide Graphite, Copper-Graphite, Copper- Tungsten

Duty Cycle, Pulse on time, Pulse off time

MRR, TWR

Mr. V.D.Patel et. al.[18]

- Analysis of different Tool material on MRR and Surface Roughness of Mild Steel in EDM.

Mild Steel Copper, Brass, Aluminum

Current, Pulse on time

MRR, SR

Santanu Dey et. al.[19]

2013 Experimental study using different Tools/Electrodes E.G. Copper, Graphite on M.R.R of E.D.M process and selecting the best one for maximum M.R.R in optimum condition.

Mild Steel Copper, Graphite Current, Pulse Duration, Spark Gap MRR S.H.Tomadi et. al.[20]

2009 Analysis of the influence of EDM parameters on Surface Quality, Material Removal Rate and Electrode Wear of Tungsten Carbide.

Tungsten Carbide

Copper- Tungsten

Current, Pulse on time, Pulse off time, Voltage, MRR, Surface Quality, TWR Harpreet Singh et. al.[21]

2012 Effect of Pulse On/Pulse Off Time on machining of AISI D3 Die Steel using Copper And Brass Electrode In EDM.

AISI D3 Tool Steel

Copeer, Brass

Pulse on time, Pulse off time

MRR

Shankar Singh et. al.[22]

2004 Some investigations into the Electric Discharge Machining of Hardened Tool Steel using different Electrode materials.

EN 31 Tool Steel Copper, Brass, Aluminum, Copper- Tungsten

Current MRR,

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38 N.Arunkumar

et. al.[23]

2012 Investigation on the effect of process parameters for machining of EN31 (Air Hardened Steel) By EDM.

EN 31 Copper, Aluminum

Current, Pulse on time, Flushing Pressure

SR, MRR, TWR

Bhavesh A. Patel et. al.[24]

2013 Influence of Electrode material and process parameters on Surface Quality and MRR in EDM of AISI H13 using ANN.

AISI H13 Steel

Aluminum, Copper, Brass

Current, Pulse on time, Pulse off time, Gap Voltage

MRR, SR, TWR

Subramanian Gopalakannan et. al.[25]

2012 Effect of Electrode Materials on Electric Discharge Machining of 316 L and 17-4 PH Stainless Steels.

316 L and 17-4 PH Stainless Steels Copper, Graphite Copper- Tungsten

Current MRR, EWR,

SR

Harpuneet Singh[26]

2012 Investigating the effect of Copper Chromium and

Aluminum Electrodes on EN-31 Die Steel on Electric Discharge Machine using Positive Polarity.

EN 31 Copper Chromium, Aluminum

Current Overcut, MRR, SR

Saeed Daneshmand et. al.[27]

2012 Investigation of EDM parameters on Surface Roughness and Material Removal Rate of NiTi60 Shape Memory Alloys.

NiTi60 Shape Memory Alloys

Brass Current, Pulse on time, Pulse off time, Gap Voltage

MRR, TWR

Mohan Kumar Pradhan et. al.[28]

2008 Modeling of machining parameters for MRR in EDM using Response Surface Methodology.

AISI D2 Tool Steel

MRR

Suraj

Choudhary et. al.[29]

2013 Analysis of MRR and SR with different Electrode for SS 316 on Die-Sinking EDM using Taguchi Technique.

SS 316 Copper, Brass, Graphite

MRR, SR

Periyakgounder Suresh et. al.[30]

2014 Optimization of intervening variables in Micro EDM of SS 316L using a Genetic Algorithm and Response-Surface

Methodology.

SS 316L Brass Current, Pulse on time, Pulse off time

MRR, TWR

V.Balasubrama niam et. al.[31]

2014 Optimization of Electrical Discharge Machining parameters using Artificial Neural Network with different Electrodes.

Ai-SiCP MMC

Copper, Brass, Tungsten

Current, Pulse on time, Pulse off time

MRR, EWR

S. Dewangan et. al.[32]

2014 Optimization of the quality and productivity characteristics of AISI P20 tool steel in EDM process using PCA-based Grey Relation Analysis.

AISI P20 Tool Steel

Brass Current, Pulse on time, Duty Cycle

MRR, SR

Saeed

Daneshmand et. al.[33]

2013 Influence of machining

parameters on Electro Discharge Machining of NiTi Shape Memory Alloys.

NiTi Shape Memory Alloys

Copper Current, Pulse on time, Voltage

MRR, TWR

Nikhil Kumar et. al.[34]

2012 Comparative study for MRR on die-sinking EDM using

Electrode of Copper & Graphite.

AISI P20 Tool Steel

Copper, Graphite

MRR, TWR

Harpreet Singh et. al.[35]

2012 Examination of Surface Roughness using different machining parameter in EDM.

AISI D3 Tool Steel

Copper, Brass

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39 S.B.Chikalthan

kar et. al.[36]

2013 Experimental Investigations of EDM Parameters.

AISI D2 Tool Steel

Copper Current, Pulse on time, Pulse off time, Gap Voltage

MRR, SR

Dilshad Ahmad Khan et. al.[37]

2011 Effect of tool polarity on the machining characteristics in Electric Discharge Machining of Silver Steel and Statistical Modeling of the process.

Silver Steel Copper Pulse Current, Pulse Duration

MRR, TWR, SR

Shivendra Tiwari et. al.[38]

2013 Optimization of Electrical Discharge Machining (EDM) with respect to Tool Wear Rate.

Mild Steel Copper Current, Pulse on time, Gap Voltage

TWR

Alpesh Nogas et. al.[39]

2013 Experimental investigation of MRR of Cold Work Tool Steel material on EDM for different electrode materials.

Cold Work Tool Steel D3

Copper, Aluminum, Brass

MRR

Praveen Kumar Singh et. al.[40]

2014 Parametric studies for MRR and TWR using die sinking EDM with electrode of Copper and Brass.

AISI D2 Tool Steel

Copper, Brass

Current, Voltage, Gap Voltage

MRR, TWR

IX. CONCLUSION

The analysis in this area of EDM performance is generally find out on the basis of MRR, OC, TWR and SR. The performance is affected by discharge current, pulse on time, pulse off time, voltage, arc gap, duty cycle, and flushing pressure. When current increases, the MRR also increases. The higher the current, intensity of spark is increased and results in high MRR. When the current is increased, surface roughness is also increased. When pulse-on-time increases, the MRR is increased or decreased, It is based on tool & workpiece materials. Same, when pulse-off-time increases, the MRR is increased or decreased, It is based on tool & workpiece materials. But, its effect is less as compare to the pulse-on-time. When Pulse-on-time is increased, surface roughness is decreased. Surface Roughness improves with increase in pulse-off time. The review paper evaluates the areas and subareas where optimization techniques are used. It works on identifying parameters for optimization and also suitable techniques for EDM mechanism. The researchers used latest optimization techniques such as Taguchi, GA, ANN, GRA, RSM, SA, fuzzy logic, desirability, and utility are mostly focused on multi response optimization. The application of latest optimization techniques in optimizing performance parameters of EDM process positively gives good results compared to conventional techniques as proven from the existing work mentioned in this paper. So, the latest optimization technique used in the electric discharge machining (EDM) processes for maximize the Material Removal Rate (MRR), reduced the Tool Wear Rate (TWR), reduced the Over cut (OC), improve the Surface Roughness (SR).

REFERENCES Journal Papers:

[1] Shankar Singh et. al.,“Some investigations into the electric discharge machining of hardened tool steel using different electrode materials”,Journal of Materials Processing Technology 149 (2004) 272–277.

[2] S.H.Tomadi et. al.,“ Analysis of the Influence of EDM Parameters on Surface Quality, Material Removal Rate and Electrode Wear of Tungsten Carbide”, Proceedings of the International MultiConference of Engineers and Computer Scientists 2009 Vol IIIMECS 2009, March 18 - 20, 2009, Hong Kong.

[3] S. Assarzadeh et. al.,“ Statistical modeling and optimization of process parameters in electro-discharge machining of cobalt-bonded tungsten carbide composite (WC/6%Co)”,Procedia CIRP 6 ( 2013 ) 463 – 4682212-8271 © 2013 The Authors. Published by Elsevier B.V.Selection and/or peer-review under responsibility of Professor Bert Lauwers doi: 10.1016/j.procir.2013.03.099.

[4] S. Gopalakannan et. al.,“Modeling & Optimization of EDM process parameter on machining of AL 7075-B4C MMC using RSM”, Procedia Engineering 38 ( 2012 ) 685 – 690 1877-7058.

[5] T.M.Chenthil Jegan et. al., “Determination of EDM parameters in AISI 202 SS using Gray Relation Analysis”, Procedia Engineering 38 ( 2012 ) 4005 – 4012 1877-7058.

[6] Nibu Mathew et. al.,“Study of Material Removal Rate of Different Tool Materials During EDM of H11 Steel at Reverse Polarity”, International Journal of Advanced Engineering Technology E-ISSN 0976-3945.

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40 [8] Dinesh Kumar et. al.,“ Study of Overcut During Electric Discharge Machining of Hastelloy Steel With Different Electrodes Using the Taguchi Method”,International Journal of Advanced Engineering Technology E-ISSN 0976-3945.

[9] Gaurav Raghav et. al.,“Optimization of Material Removal Rate in Electric Discharge Machining Using Mild Steel”, International Journal of Emerging Science and Engineering (IJESE) ISSN: 2319–6378, Volume-1, Issue-7, May 2013. [10] Singaram Lakshmanan et. al.,“ Optimization of Surface Roughness using Response Surface Methodology for EN31 Tool Steel EDM Machining”,ISSN 2347-6435(Online), Volume 1, Issue 3, December 2013.

[11] Amit Kohli et. al.,“ Optimization of Material Removal Rate in Electrical Discharge Machining Using Fuzzy Logic”, World Academy of Science, Engineering and Technology Vol:6 2012-12-20.

[12] Shashikant et. al.,“Optimization of Machine Process Parameters on Overcut in EDM for EN19 Material using RSM”, International Journal of Current Engineering and Technology ISSN 2277 – 4106.

[13] Shivendra Tiwari,“ Effect of Different Process Parameters on Over Cut in Optimizing of Electrical Discharge Machining (EDM) Process”, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 7, July – 2013, ISSN: 2278-018.

[14] Vishal J Nadpara et. al.,“ Optimization of EDM Process Parameters Using Taguchi Method with Graphite Electrode”, International Journal of Engineering Trends and Technology (IJETT) – Volume 7 Number 2- Jan 2014. [15] Hitesh B. Prajapati et. al.,“ Parametric Analysis of Material removal rate and surface roughness of Electro Discharge Machining on EN 9”,Vol. 1, Issue:1, February 2013 (IJRMEET) ISSN:2320-6586.

[16] Navdeep Malhotra et. al.,“ Experimental Study of Material Removal Rate in EDM”, IJAEA, Volume 2, Issue 1, pp. 6-10, 2009.

[17] P. Janmanee et. al.,“ Performance of Difference Electrode Materials in Electrical Discharge Machining of Tungsten Carbide”,Energy Research Journal 1 (2): 87-90, 2010ISSN 1949-0151, 2010 Science Publications. [18] Mr. V.D.Patel et. al.,“ Analysis of Different Tool Material On MRR and Surface Roughness of Mild Steel In EDM”,International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622, Vol. 1, Issue 3, pp. 394-397.

[19] Santanu Dey et. al.,“Experimental Study Using Different Tools/Electrodes E.G. Copper, Graphite on M.R.R of E.D.M Process and Selecting The Best One for Maximum M.R.R in Optimum Condition”, IJMER, Vol.3, Issue.3, May-June. 2013 pp-1263-1267 ISSN: 2249-6645.

[20] S.H.Tomadi et al.,“ Analysis of the Influence of EDM Parameters on Surface Quality, Material Removal Rate and Electrode Wear of Tungsten Carbide”, Proceedings of the International MultiConference of Engineers and Computer Scientists 2009 Vol II IMECS 2009, March 18 - 20, 2009, Hong Kong.

[21] Harpreet Singh et. al.,“Effect of Pulse On/Pulse Off Time On Machining Of AISI D3 Die Steel Using Copper And Brass Electrode In EDM”, International Journal of Engineering and Science ISSN: 2278-4721, Vol. 1, Issue 9 (November 2012), PP 19-22.

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[24] Bhavesh A. Patel et. al.,“Influence of Electrode Material and Process Parameters on Surface Quality and MRR in EDM of AISI H13 using ANN”, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 1, Issue: 12, 858 – 869.

[25] Subramanian Gopalakannan et. al.,“Effect of Electrode Materials on Electric Discharge Machining of 316 L and 17-4 PH Stainless Steels”, Journal of Minerals and Materials Characterization and Engineering, 2012, 11, 685-690 Published Online July 2012.

[26] Harpuneet Singh,“ Investigating the Effect of Copper Chromium and Aluminum Electrodes on EN-31 Die Steel on Electric Discharge Machine Using Positive Polarity”, Proceedings of the World Congress on Engineering 2012 Vol III WCE 2012, July 4 - 6, 2012, London, U.K.

[27] Saeed Daneshmand et. al.,“ Investigation of EDM Parameters on Surface Roughness and Material Removal Rate of NiTi60 Shape Memory Alloys”, Australian Journal of Basic and Applied Sciences, 6(12): 218-225, 2012ISSN 1991-8178.

[28] Mohan Kumar Pradhan et. al.,“ Modelling of machining parameters for MRR in EDM using response surface methodology”, Proceedings of NCMSTA’08 ConferenceNational Conference on Mechanism Science and Technology: from Theory to ApplicationNovember 13-14, 2008 National Institute of Technology, Hamirpur.

[29] Suraj Choudhary et. al.,“ Analysis of MRR and SR with Different Electrode for SS 316 on Die-Sinking EDM using Taguchi Technique”, Global Journal of Researches in EngineeringMechanical and Mechanics Engineering Volume 13 Issue 3 Version 1.0 Year 2013.

[30] Periyakgounder Suresh et. al.,“ Optimization of Intervening Variables in MicroEDM of SS 316L using a Genetic Algorithm and Response-Surface Methodology”, Journal of Mechanical Engineering 60(2014)10, 656-664.

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[33] Saeed Daneshmand et. al.,“Influence of Machining Parameters on Electro Discharge Machining of NiTi Shape Memory Alloys”, Int. J. Electrochem. Sci., 8 (2013) 3095 – 3104.

[34] Nikhil Kumar et. al.,“Comparative Study For Mrr on Die-Sinking EDM Using Electrode of Copper & Graphite”, International Journal of Advanced Technology & Engineering Research (IJATER), VOLUME 2, ISSUE 2, MAY 2012. [35] Harpreet Singh et. al.,“Examination of Surface Roughness Using Different Machining Parameter in EDM”,IJMER, Vol.2, Issue.6, Nov-Dec. 2012 pp-4478-4479.

[36] S.B.Chikalthankar et. al.,“ Experimental Investigations of EDM Parameters”, International Journal of Engineering Research and Development e-ISSN: 2278-067X, p-ISSN: 2278-800X, Volume 7, Issue 5 (June 2013), PP. 31-34.

[37] Dilshad Ahmad Khan et. al.,“ Effect of Tool Polarity on The Machining Characteristics in Electric Discharge Machining of Silver Steel And Statistical Modelling of The Process”, IJEST, Vol. 3 No. 6 June 2011.

[38] Shivendra Tiwari,“Optimization of Electrical Discharge Machining (EDM) with Respect to Tool Wear Rate”, International Journal of Science, Engineering and Technology Research (IJSETR) Volume 2, Issue 4, April 2013. [39] Mr. Alpesh Nogas et. al.,“Experimental Investigation of MRR of Cold Work Tool Steel Material on EDM for Different Electrode Materials”, IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 3, 2013 | ISSN (online): 2321-0613.

[40] Praveen Kumar Singh et. al.,“ Parametric studies for MRR and TWR using die sinking EDM with electrode of Copper and Brass”, Proc. of the Intl. Conf. on Advances In Engineering And Technology - ICAET-2014, ISBN: 978-1-63248-028-6 doi: 10.15224/ 978-1-63248-028-6-03-145.

Brochures:

[1] Instruction and Maintenance Manual For Spark Erosion Machine by Sparkonix Pvt. Ltd. [2] Techtips by Roger Kern.

Books:

[1] Text book of Machine tool engineering by G.R. Nagpal in 2004 , Khana publication. [2] Text book of production engineering by P.C. Sharma in 1982, S.Chand & Company ltd.

[3] Text book of Manufacturing science by Amitabha Ghose & Asok mallik in 2005 West press private Ltd. [4] Text book of Production Engineering Technology by R.K Jain.