The document discusses the implementation of the Taguchi method to optimize process parameters for maximizing the material removal rate (MRR) in electrical discharge machining (EDM). Key process parameters analyzed include peak current, pulse on time, pulse off time, tool material, and workpiece material. Experiments were conducted using a Taguchi L27 orthogonal array with three levels for each parameter. Analysis of variance of the signal-to-noise ratios revealed that current has the most significant effect on MRR, followed by pulse on time, pulse off time, and tool material. The optimal parameters determined for maximum MRR were a peak current of 20A, pulse on time of 8μs, pulse off time of 5μs, using

1. 1
The Implementation of Taguchi Method for optimization of
Material Removal Rate in Electrical Discharge Machining
Process
Pavan Patel 1
Darshit Patel 2
Tarun Modi3
Kaushik Parmar4
Sheetal Pandya5
1,2,3,4 B.E Mechanical Student 5 Assistant Professor
Vishwakarma. Govt. Engg. College, Department of Mechanical Engineering
Chandkheda, 382424 V.G.E.C, Chandkheda,382424
Ahmedabad. Ahmedabad
Email : 1. Pavan00747@yahoo.com Email: s_pandya7@yahoo.co.in
2. Darshil.5454@gmail.com
3. Kaushikparmar13@gmail.com
4. tarun_modi1989@yahoo.com
Abstract: In this paper, the application of the Taguchi method for optimization of process
parameters, to maximize MRR of EDM process has been reported. The Taguchi method is
used to formulate the experimental layout, analyses the effect of process parameter & to
determine the optimum choice for EDM parameters. Process parameters taken in to
consideration are Peak current, Pulse on time, Pulse off time, Workpiece material & Tool
material. The Analyses based on Signal to Noise ratio (S/N ratio) & Analysis of Variance
(ANOVA) of S/N ratio reveals the order in which the process parameters have effect on
MRR of EDM process. The optimum choice of process parameter to maximize MRR is
determined from main effect plots of means & main effect plots of S/N ratio.
Key words: EDM, MRR, ANOVA.
1. INTRODUCTION
With increasing demand for new hard, high strength & temperature resistance material in
engineering, the development of EDM has become increasingly important. In EDM Material is
removed by mean of rapid & repetitive spark discharge across the gap between the tool & work
piece. In EDM it is important to select machining parameters for achieving optimal machining
performance (McGeough, 1998). Since the EDM process does not involve mechanical energy,
the removal rate is not affected by hardness, strength or toughness of workpiece material (Mohd

2. 2
Amri Lajis et al.2009). Therefore it is of great significance & necessary to investigate effects of
EDM process parameters (Current, Pulse duration, interval time, work piece, tool) on material
removal rate of EDM process. The Taguchi method, an experimental design method adopts a set
of orthogonal arrays to investigate the effects of parameters on specific quality characteristics to
decide optimum parameter combination. Therefore, here in this study Taguchi method (Roy Ranjit
K, 1966) as a powerful tool in the design of experiment methods is used to determine the
optimum values of process parameters to maximize MRR.
2. EXPERIMENTAL WORK
In this study the experiment has been conducted on the Electrical Discharge Machine (S25
Sparkonix ) .The schematic diagram of the experimental set-up is shown in fig. 1. The electrode
& workpiece are separated by dielectric. An Experiment, for determining the optimal machining
parameters is carried out by setting three levels of the machining parameters. Ranges of factors
are selected based on the results obtained from pilot experiment.
Table 1 Machining parameters & their levels
Factors
Levels
Level 1 Level 2 Level 3
Current (amp) 3 12 20
Pulse On Time (µs) 3 5 8
Pulse Off Time (µs) 9 5 3
Tool Material Copper Brass Graphite
WP Material M.S. EN31 D2
Difference in weight of workpiece material is recorded after each machining test. The machining
time considered is 20 min for each test. The material removal rate is calculated by considering
amount of material removed, density of workpiece material & the required cutting time.
Fig. 1 Schematic diagram of the EDM process (Lin J.L. and Lin C.L., 2002)
3. DESIGN OF EXPERIMENT
In this study the design of experiment is carried out using Taguchi’s method. In Taguchi method to
design an experiment is to select the most suitable Orthogonal Array (OA) & to assign the
parameters and interactions to most appropriate columns.

3. 3
In the present investigation, the effects of the selected EDM process parameters on the selected
quality characteristics have been investigated through S/N data analysis. The optimum condition for
MRR has been established through main effect plot for S/N ratio and main effect plot for means.
Furthermore, statistical analysis of variance of S/N ratio is performed to identify the process
parameters those are of statistical significant.
3.1 Selection of Process Parameters ,Their levels & Identification of Response
The process parameters of EDM, their level & response selected for this experiment is given below
in table 2.
Table 2 Process Parameters & their levels
Factors
Levels Response
Level 1 Level 2 Level 3
Material removal rate
Current (amp) 3 12 20
Pulse On Time (µs) 3 5 8
Pulse Off Time (µs) 9 5 3
Tool Material Copper Brass Graphite
WP Material M.S. EN31 D2
3.2 Selection of OA & Assignment of Process Parameters to OA.
After selecting process parameters & their levels, L27 OA is selected based on the degree of
freedom. Assignment of the factors to OA is carried out by MINITAB software.
3.2.1 Degree of Freedom (DOF)
 No of DOF for a factor = numbers of level -1
 Total No of DOF for an experiment = no of test – 1. = 27-1 = 26
 No of DOF for error = DOF of an experiment – Total no of DOF of all factor
= 26-10 =16
Table 3 Degree of Freedom
Factor
Current Ton Toff Tool material
Workpiece
material
Total
Degree of
freedom 2 2 2 2 2 10
Table 4 Orthogonal Array
SR
No
Current
A
Ton
µs
Toff
µs
Tool Mtr. WP Mtr.
MRR mm3
/min Mean
MRR
S/N
RatioI II
1 3 3 9 Copper MS 0.0682 0.0695 0.068878 -23.238446
2 3 3 9 Copper D2 0.0649 0.0675 0.066234 -23.578411
3 3 3 9 Copper EN31 0.0164 0.0079 0.012171 -38.293437
4 3 5 5 Brass MS 0.3444 0.3176 0.330995 -9.603574
5 3 5 5 Brass D2 0.2006 0.1273 0.163961 -15.705187
6 3 5 5 Brass EN31 0.1862 0.1816 0.183882 -14.709236
7 3 8 3 Gr. MS 0.4994 0.8584 0.67889 -3.3640079
8 3 8 3 Gr. D2 0.4643 0.4571 0.460714 -6.7313664

4. 4
9 3 8 3 Gr. EN31 0.3737 0.2493 0.311513 -10.130472
10 12 3 5 Gr. MS 1.0925 0.9152 1.003827 0.03317339
11 12 3 5 Gr. D2 1.9422 2.0539 1.998052 6.01213551
12 12 3 5 Gr. EN31 2.2658 1.8072 2.036513 6.17774441
13 12 5 3 Copper MS 6.8265 6.6282 6.72736 16.556893
14 12 5 3 Copper D2 4.8097 4.2760 4.542857 13.1465816
15 12 5 3 Copper EN31 6.9243 8.5908 7.757566 17.7945093
16 12 8 9 Brass MS 1.7978 2.7596 2.278699 7.15373916
17 12 8 9 Brass D2 2.5558 2.1864 2.371104 7.49901168
18 12 8 9 Brass EN31 2.3461 3.0414 2.69375 8.60714575
19 20 3 3 Brass MS 0.8846 0.2526 0.568559 -4.9044942
20 20 3 3 Brass D2 0.5110 0.8169 0.663961 -3.5571481
21 20 3 3 Brass EN31 0.5559 0.7993 0.677632 -3.3801273
22 20 5 9 Gr. MS 2.5593 1.9707 2.264987 7.10131521
23 20 5 9 Gr. D2 5.7961 5.1506 5.473377 14.7651067
24 20 5 9 Gr. EN31 3.7441 2.2882 3.016118 9.58896778
25 20 8 5 Copper MS 20.4069 21.7806 21.09375 26.4830759
26 20 8 5 Copper D2 15.9851 14.4773 15.23117 23.6546646
27 20 8 5 Copper EN31 12.3783 11.2211 11.79967 21.437398
3.3 ANALYSIS OF RESULTS
Analysis of result is carried out by calculating S/N ratio of MRR values and ANOVA of S/N ratio.
Calculation of S/N ratio & ANOVA is carried out using MINITAB software.
3.3.1 Signal-to-Noise Ratio
From the analysis point of view, there are three possible categories of the response
characteristics 1. “Smaller is better” (LB), 2.”Larger is better” (HB) and 3. “Nominal is best” (NB)
types. Here MRR is considered as response characteristics there for “larger is better “is considered.
(S/N)HB= -10 log (MSDHB)
Where, MSDHB=
n
1
(
yyyy n
22
3
2
2
2
1
1
....
111
 ) ----------------------------------------------------------(1)
n = no. of repetitions,
y= observed value.
MSD = Mean square deviation
3.3.2 Analysis of Variance
The knowledge of the contribution of individual factors is critically important for the control of the
final response. The analysis of variance (ANOVA) is a common statistical technique to determine
the percent contribution of each factor for results of the experiment. It calculates parameters
known as sum of squares (SS), pure SS, degree of freedom (DOF), variance, F-ratio and
percentage of each factor.
4. RESULTS AND DISCUSSION
The effects of machining parameters i.e. work-piece, electrode, pulse on time, pulse off time,
current, are evaluated through S/N ratio and General linear Model ANOVA of S/N ratio. A

5. 5
confidence interval of 95% has been used for the analysis. Two runs for each of 27 trails are
executed to measure the S/N Ratio. The results for MRR for each of the 27 treatment conditions
with repetition are given in Table 4 MRR of each sample is calculated from weight difference of
work-piece before and after the performance trial, which is given by
--------------------------------------- (2)
Where, ρ = density of workpiece material in gm/cm3
Table 5 Response table for SN ratio
Level Current Ton Toff Tool WP
1 -16.1505 -9.4143 1.7145 3.7736 1.8020
2 9.2201 -4.3262 4.8645 3.1778 1.7228
3 10.1321 8.2899 -3.3772 2.6058 -0.3231
Rank 1 2 3 4 5
Table 6 Analysis of Variance for SN ratios
Source DF Seq SS Adj SS Adj MS F P
Current 2 4005.81 4005.81 2002.90 129.23 0.000
Ton 2 1556.86 1553.86 776.93 50.13 0.000
Toff 2 311.32 311.32 155.66 10.04 0.001
Tool Material 2 249.41 249.41 124.70 8.05 0.004
WP 2 26.12 26.12 13.06 0.84 0.449
Error 16 247.97 247.97 15.50
Total 26 6394.49
20123
0
-6
-12
-18
-24
853 953
GraphiteBrassCopper
0
-6
-12
-18
-24
EN31D2MS
Current
MeanofSNratios
Ton Toff
Tool Material WP
MainEffects Plot for SN ratios
Data Means
Signal-to-noise: Larger is better
20123
8
6
4
2
0
853 953
GraphiteBrassCopper
8
6
4
2
0
EN31D2MS
Current
MeanofMeans
Ton Toff
Tool Material WP
Main Effects Plot for Means
Data Means
Fig. 2 Main Effects Plot for SN ratios Fig. 3 Main Effects Plot for Means

6. 6
From Table 5 (Response table for S/N ratio of MRR at 95% confidence interval) and Table 6
(Analysis of Variance for SN ratios at α = 0.5 ); the order in which each machining parameters
affects MRR of EDM Process is determined. Figure 2 (Main Effects for Means for MRR) and
Figure 3 (Main Effects Plot for SN ratios), both reveals the same results for optimum condition of
machining parameters .Value of S/N ratio in Table 4 also gives optimum choice of machining
parameter for maximum MRR.
5. CONCLUSION
In this experimental analysis,
 Highest MRR was observed when work-piece material MS is machined with
copper tool at pulse on time 8μs, pulse off time 5μs & current 20Amp.
 Current at 20Amp has optimal value for higher MRR.
 Current was observed to be the most significant factor affecting the MRR,
followed
by pulse on time, pulse off time, tool material and work piece material.
 The results of ANOVA for S/N ratios of MRR (Table 5) indicates that current, Ton,
Toff & tool material are significant machining parameters while work piece
material is insignificant parameter affecting MRR.
6. REFERENCES
1. Garg R K, Singh K K, Sachdeva Anish, Sharma Vishal S, Ojha Kuldeep, and Singh Sharnjit, 2010 ,“Review of
Research Work in Sinking EDM and WEDM on Metal Matrix Composite Materials”, International Journal of
Advance Manufacturing Technology, pp. 611-624.
2. Lin J L , Wang K S , Yan B H, and Tarng Y S, 2000, “Optimization of the electrical discharge machining process
based on Taguchi method with fuzzy logics”, Journal of Materials Processing Technology, pp. 48-55.
3. Lin J Land Lin C L, 2002, “The use of the orthogonal array with grey relational analysis to optimize the electrical
discharge machining process with multiple performance characteristics”, International Journal of Machine Tools &
Manufacture, pp. 237–244.
4. McGeough J A, 1998, “Advanced Methods of Machining”, Chapman & Hall, New York.
5. Mohd Amri Lajis , Mohd Radzi H C D, and Narul Amin A K M, 2009, “The Implementation of Taguchi Method on
EDM Process of Tungsten Carbide”, European Journal of Scientific Research, pp. 609-617.
6. Ross P J, 1988, Taguchi Techniques for Quality Engineering, McGraw-Hill, New York.
7. Roy Ranjit K, 1966 “A premier on the Taguchi Method”, Van Nostrand Reinhold, New York.
8. Roy Ranjit K, 2001 “Design of Experiments using the Taguchi Approach”, Job Wiley & Sons, New York.
9. Thillaivanan A, Asokan P, Srinivasan K N, and Saravanan R, 2010, “Optimization of operating parameters for
EDM Process based on Taguchi Method and Artificial Neural Network”, International Journal of Engineering
Science and Technology, pp 6880-6888.