1. WELCOMEWELCOME
Research Title -
Investigation On Process Parameters Of Electric Discharge
Machining Of Titanium Grade 5 Alloy
By
Mr. Saravanan Paramasivam / Head of Engineering Department / NCT / Oman
Research Scholar under the Guidance of
Dr. L. Antony Michael Raj / VP / SRM University – Ramapuram
DC Members
1. Dr.G.Prabhakaran - Professor & VP / Velammal Engineering College
2. Dr.S.Satish Kumar - Professor / Department of Production / Velammal Engineering College
3. Dr.R.Varahamoorthi - Professor / Department of Manufacturing / Annamalai University
4. Dr.B.K.Vinayagam - Professor & Head / Department of Mechatronics / SRM University

2. ACKNOWLEDGEMENT
Supervisor
DC Members
SRM Administration
NCT Administration
MoMP
Friends & Well Wishers
Family

3. Need ( Industrial) for Research
Problem related to a popular aero industry material ASD-STAN PREN 2339 sheet,
which is a cold rolled titanium grade 5 alloy sheet of thickness between 2mm and
3mm.
Nearly 40% [12] of entire machining work done on titanium grade 5 alloy is to
make holes on the sheets. So doing research in sink EDM is very useful to the
industries at the same time thick sheets of titanium grade 5 alloys are cut by wire
cut EDM before making holes. So researching both sink EDM & WEDM in the
identified application specific area will be useful for the industries.
Multi objective optimization of EDM parameters which results in the
improvements of responses will be very useful for the industry to increase their
overall productivity, so it always is a priority.

4. Titanium Alloys
There are 38 grades of titanium alloys from pure to most alloyed, providing
wide range of properties
Out of all those alloys, titanium grade 5 alloy is most significant and is called
“Aero Industry Work Horse”.
Machining Titanium Grades 1, 2, 3, 4, 7 and 11 presents no particular
difficulties while machining conventionally as long as chemical and physical
characteristics are understood correctly but the other grades, particularly
titanium grade 5 alloy is difficult to machine through conventional machining.
The two machining procedures that are good for titanium grade 5 alloy are
EDM & water jet machining [11][103, 104]

5. Titanium Grade 5 Alloy
Chemical Composition ( Ti - 6Al - 4V by weight )
Properties
• High tensile strength, high toughness, light weight, extraordinary corrosion resistance,
having high hardness at high temperatures and great weld ability
• It has a density of roughly 4420 kg/m3
, Young's modulus of 110 GPa, and tensile strength of
1000 MPa.[8]
By comparison, annealed type 316 stainless steel has a density of 8000 kg/m3
,
modulus of 193 GPa, and tensile strength of only 570 MPa.[9]
And tempered
6061 aluminium alloy has 2700 kg/m3
, 69 GPa, and 310 MPa, respectively. Hardness 349
Vickers.
• Highest strength-to-weight ratio metal. The titanium 6AL-4V alloy accounts for almost 50%
of all alloys used in aircraft applications[31]
Applications
Military applications, aircraft, spacecraft, medical devices, connecting rods on expensive
sports cars , premium sports equipment and medical implants.

6. About EDM

7. This process is much faster than electrode EDM.
Wire EDM

8. Literature Survey – Critical
# Year Author Title Work done Publisher
1 2010 Kuldeep
Ojha, Garg,
R. K., and
Singh, K. K.
MRR improvement in sinking electrical
discharge machining : a review.
A review paper analyzed over 500
published papers in EDM field
and proposed probable research
areas.
J. Minerals & Mater.
Characterization &
Engineering, 2010, 9(8),
709-739.
2 2010 Anand P &
Shankar
Singh
Current research trends in variants of EDM:
A review
International Journal of
Engineering Science and
Technology; 2(6) : 2172-
2191
3 2007 Ahmet
Hascalik and
Ulas Caydas.
Electrical discharge machining of titanium
alloy (Ti-6Al-4V)
Metallurgical machinability
study was done with SEM images
of resultant surfaces.
Applied Surface Science,
2007, 253, 9007-9016
4 2009 Pradhan,B.B
., and
Bhattachary
ya, B.
Modeling of Micro-EDM during machining
of Titanium Alloy , Ti-6Al-4V using RSM
and ANN Algorithm
Experiment modeling was done
using RSM and ANN.
Proc. IMechE Part B: J.
Engineering
Manufacture, 2009, 223,
No. 6, 683-693.
5 2012 Belgassim,O.,
and
Abusaada,A
Investigation of the influence of EDM
parameters on the machined surface
hardness of AISI D3 tool steel.
Influence of EDM process
parameters were studied.
Journal of Engineering
Research, March 2012,
16, 31-38.

9. Problem Statement
Aim of this research was to investigate the effect of pulse current, pulse on
time, pulse off time and tool geometry on important EDM output responses such as
metal removal rate (MRR), tool wear rate (TWR), surface roughness (SR) and entry
exit deviation (EED) for three different tool electrode materials such as graphite,
copper and brass while machining titanium grade 5 alloy using EDM.
And also to investigate the effect of six critical wire cut EDM experimental
parameters such as current, pulse on time, pulse off time, servo voltage , wire tension
and wire speed on important WEDM output responses metal removal rate(MRR),
surface roughness (SR) and wire offset while machining titanium grade 5 alloy
using brass wire in wire cut EDM.

10. Research Objectives in EDM
Experimental determination of the effects of the various process parameters on the
performance measures ( Responses) in EDM process of titanium grade 5 alloy.
Influence ranking of process parameters
Modeling the performance measures using regression analysis and experimental
validation.
Finding electrical parameters interaction effect
Multi-objective optimization of the process parameters of EDM process
using statistical methods.

11. Research Objectives in WEDM
Experimental determination of the effects of the various process parameters on the
performance measures ( Responses) in WEDM process of titanium grade 5 alloy.
Influence ranking of process parameters
Modeling the performance measures using regression analysis and validation.
Single response optimization of parameters using contour plots in WEDM.
Multi-objective optimization of the process parameters of EDM process using SA,
BFOA and CUCKOO evolutionary heuristic algorithms.

12. Stages of Research in EDM

13. Experiment Design - EDM
Taguchi Design
• L27 orthogonal array with interaction between factors
• Four factors at three levels
– Current
– Pulse on time
– Pulse off time
– Tool cross sectional area and geometry
• Held Constant Factors
– Dielectric – Kerosene, Side Flushing, Pressure – 0.6MPa
– Gap Voltage 60V
Responses
• MRR
• TWR
• Surface Roughness
• Entry and exit deviation angle

14. Process Parameters & Levels for EDM
PARAMETERS LEVELS
L1 L2 L3
Current (I) amps 15 20 25
Pulse on-time (Ton)µs 50 100 200
Pulse off-time (Toff) µs 50 100 200
Tool electrode
geometry (mm2
)
Rectangular (40) /
Triangle (27.7)
Circle (50.265) Square (64)

15. Experiment Design
(Taguchi design using Minitab with the Consideration for Interaction Effects)
Exp No Current ( amps) Pulse on time(µs) Pulse off time(µs) Geometry (sq mm)
1 1 1 1 3
2 1 1 2 1
3 1 1 3 2
4 1 2 1 1
5 1 2 2 2
6 1 3 1 2
7 1 2 3 3
8 1 3 2 3
9 1 3 3 1
10 2 1 1 1
11 2 1 2 2
12 2 1 3 3
13 2 2 1 2
14 2 2 2 3
15 2 2 3 1
16 2 3 1 3
17 2 3 2 1
18 2 3 3 2
19 3 1 1 2
20 3 1 2 3
21 3 1 3 1
22 3 2 1 3
23 3 2 2 1
24 3 2 3 2
25 3 3 1 1
26 3 3 2 2
27 3 3 3 3

16. Experimental Work ( Work Piece & Tool)
Titanium Grade 5
Work piece
Graphite Tools
Used
Copper Tools
Used
Brass Tools
Used

17. Experimental Work
Work piece & Electrode
Setting
EDM Machining in
progress
Machined Work Piece with
Graphite
Machined Work piece with
Copper

18. Measurements
CMM – Entry exit deviation measurement.
MRR = MRW / (ρ x T)
Where MRW is the work piece Metal
Removal Weight in grams, ρ is the density of the work piece in
gram per cubic millimeter and T is the machining time in minutes.
TWR = TMLW / (ρ x T)
Where TMLW is the Tool Material Lost
Weight in grams, ρ is the density of the tool in gram per cubic
millimeter and T is the machining time in minutes.
Surface Roughness Measurement

19. Results Data - Graphite Tool
Exp.
No
MRR
(mm3
/min)
TWR
(mm3
/min)
SR
(µm)
EED
(º)
1 0.6642 0.0585 2.870 0.5548
2 0.7966 0.0625 2.601 0.5154
3 0.3914 0.0411 1.407 0.2146
4 0.8342 0.0638 2.729 0.4604
5 0.9646 0.0665 2.351 0.4085
6 0.5666 0.0537 1.819 0.2161
7 1.0646 0.0703 2.979 0.4510
8 1.1968 0.0819 2.346 0.2473
9 0.7964 0.0613 1.554 0.0725
10 0.7294 0.1285 2.794 0.7308
11 0.8558 0.1387 2.686 0.5121
12 0.4578 0.1197 1.639 0.3372
13 0.8954 0.1409 2.946 0.6054
14 1.0332 0.1444 2.417 0.5236
15 0.6356 0.1235 1.889 0.2129
16 1.1292 0.1492 3.205 0.4502
17 1.2636 0.1519 2.577 0.3919
18 0.8624 0.1384 1.634 0.1795
19 0.7886 0.2100 3.021 0.6766
20 0.9252 0.2086 2.648 0.6567
21 0.5472 0.1889 1.974 0.4643
22 0.9658 0.2109 3.276 0.7201
23 1.1000 0.2225 2.502 0.5023
24 0.6980 0.2019 1.868 0.3571
25 1.1964 0.2264 3.025 0.5883
26 1.3280 0.2308 2.913 0.5323
27 0.9330 0.2084 1.704 0.1822

20. ANOVA table for MRR
(Graphite tool)
Source DF Seq SS MS F P
Current 2 9.708 4.8539 13.85 0.016
On Time 2 89.409 44.7043 127.58 0.003
Off Time 2 8.008 3.9522 10.62 0.028
Geometry 2 79.728 39.8642 113.76 0.025
Current* on time 4 0.731 0.1827 0.52 0.725
Current* off time 4 1.891 0.4728 1.35 0.353
On*Off 4 0.253 0.0633 0.18 0.94
Error 6 2.102 0.3504
Total 26 183.884

21. ANOVA table for TWR
(Graphite tool)
Source DF Seq SS MS F P
Current 2 537.85 268.92 1036 0.001
On Time 2 11.71 5.855 22.55 0.002
Off Time 2 8.212 4.6 16.52 0.004
Geometry 2 9.615 4.808 18.52 0.003
Current* on ti 4 2.993 0.748 2.88 0.119
Current* off ti 4 0.179 0.045 0.17 0.945
On*Off 4 2.271 0.568 2.19 0.187
Error 6 1.558 0.26
Total 26 566.2

22. ANOVA table for SR
(Graphite tool)
Source DF Seq SS MS F P
Current 2 4.22 2.11 18.06 0.003
On Time 2 110.92 55.458 474.6 0.001
Off Time 2 1.005 0.5025 4.3 0.049
Geometry 2 1.003 0.564 4.8 0.043
Current* on ti 4 0.301 0.0752 0.64 0.651
Current* off ti 4 5.429 1.3571 11.62 0.005
On*Off 4 0.334 0.0836 0.72 0.611
Error 6 0.701 0.1168
Total 26 123.02

23. ANOVA table for EED
( Graphite)
DF Seq SS MS F P
Current 2 79.361 39.681 13.2 0.006
On Time 2 334.94 167.47 55.71 0.001
Off Time 2 30.283 15.232 6.14 0.052
Geometry 2 96.321 48.161 16.02 0.004
Current* on ti 4 11.583 2.896 0.96 0.491
Current* off ti 4 15.698 3.924 1.31 0.366
On*Off 4 2.637 0.659 0.22 0.918
Error 6 18.038 3.006
Total 26

24. Residual Analysis in MRR
(Graphite tool)
Residual
Percent
0.500.250.00-0.25-0.50
99
90
50
10
1
Fitt ed Value
Residual
0-4-8
0.50
0.25
0.00
-0.25
-0.50
Residual
Frequency
0.60.40.20.0-0.2-0.4
4.8
3.6
2.4
1.2
0.0
Obser vation Or der
Residual
2624222018161412108642
0.50
0.25
0.00
-0.25
-0.50
Normal Probabilit y Plot of t he Residuals Residuals Versus t he Fit t ed Values
Hist ogram of t he Residuals Residuals Versus t he Order of t he Dat a
Residual Plot s f or S/ N r at ios - MRR
Residual analysis plot for MRR – to check model adequacy

25. Residual Analysis in TWR
(Graphite tool)
Residual analysis plot for TWR – to check model adequacy
Residual
Percent
0.500.250.00-0.25-0.50
99
90
50
10
1
Fit t ed Value
Residual
-12-16-20-24-28
0.50
0.25
0.00
-0.25
-0.50
Residual
Frequency
0.40.20.0-0.2-0.4
6.0
4.5
3.0
1.5
0.0
Obser vat ion Or der
Residual
2624222018161412108642
0.50
0.25
0.00
-0.25
-0.50
Normal Probabilit y Plot of t he Residuals Residuals Versus t he Fit t ed Values
Hist ogram of t he Residuals Residuals Versus t he Order of t he Dat a
Residual Plots f or S/ N r at ios - TWR

26. Residual Analysis in SR
(Graphite tool)
Residual analysis plot for SR – to check model adequacy
Residual
Percent
0.40.20.0-0.2-0.4
99
90
50
10
1
Fitt ed Value
Residual
10864
0.30
0.15
0.00
-0.15
-0.30
Residual
Frequency
0.240.120.00-0.12-0.24
4.8
3.6
2.4
1.2
0.0
Obser vat ion Or der
Residual
2624222018161412108642
0.30
0.15
0.00
-0.15
-0.30
Normal Probabilit y Plot of t he Residuals Residuals Versus t he Fit t ed Values
Hist ogram of t he Residuals Residuals Versus t he Order of t he Dat a
Residual Plot s f or S/ N r at ios - SR

27. Residual Analysis in EED
(Graphite tool)
Residual analysis plot for EED – to check model adequacy
Residual
Percent
210-1-2
99
90
50
10
1
Fitt ed Value
Residual
0-5-10-15-20
1
0
-1
-2
Residual
Frequency
1.51.00.50.0-0.5-1.0-1.5-2.0
6.0
4.5
3.0
1.5
0.0
Obser vat ion Or der
Residual
2624222018161412108642
1
0
-1
-2
Normal Probabilit y Plot of t he Residuals Residuals Versus t he Fit t ed Values
Hist ogram of t he Residuals Residuals Versus t he Order of t he Dat a
Residual Plot s f or S/ N r at ios - EED

28. Findings - Factor effect on MRR
(Graphite tool)
MeanofS/Nratios
252015
0
-1
-2
-3
-4
20010050
20010050
0
-1
-2
-3
-4
64.0050.2640.00
Current Pulse on t ime
Pulse off t ime Geomet ry
Main Effects Plot (data means) for S/ N ratios - MRR
Signal-to-noise: Larger is better
Main effect plot for MRR
Cur r ent
0
-2
-4
Pulse on t im e
Pulse of f t im e
20010050
20010050
0
-2
-4
252015
0
-2
-4
Current
25
15
20
Pulse
200
on
time
50
100
Pulse
200
off
time
50
100
I nt er action Plot ( data means) f or S/ N r at ios - MRR
Signal-to-noise: Larger is better
Interaction effect plot for MRR

29. Findings - Factor effect on TWR
(Graphite tool)
Main effect plot for TWRInteraction effect plot for TWR
MeanofS/Nratios
252015
-15.0
-17.5
-20.0
-22.5
-25.0
20010050
20010050
-15.0
-17.5
-20.0
-22.5
-25.0
64.0050.2640.00
Current Pulse on time
Pulse off time Geometry
Main Effects Plot (data means) for S/ N ratios - TWR
Signal-to-noise: Larger is better
Cur r ent
-15
-20
-25
Pul se on t im e
Pulse of f t im e
20010050
20010050
-15
-20
-25
252015
-15
-20
-25
Current
25
15
20
Pulse
200
on
time
50
100
Pulse
200
off
time
50
100
I nt er act ion Plot ( dat a means) f or S/ N r at ios - TWR
Signal-to-noise: Larger is better

30. Findings - Factor effect on SR
(Graphite tool)
Main effect plot for SRInteraction effect plot for SR
MeanofS/Nratios
252015
9
8
7
6
5
20010050
20010050
9
8
7
6
5
64.0050.2640.00
Current Pulse on time
Pulse off time Geometry
Main Effects Plot (data means) for S/ N ratios - SR
Signal-to-noise: Larger is better
Cur r ent
10.0
7.5
5.0
Pul se on t im e
Pu lse of f t i m e
20010050
20010050
10.0
7.5
5.0
252015
10.0
7.5
5.0
Current
25
15
20
Pulse
200
on
time
50
100
Pulse
200
off
time
50
100
I nt er act ion Plot ( dat a means) f or S/ N r at ios - SR
Signal-to-noise: Larger is better

31. Findings - Factor effect on EED
(Graphite tool)
Main effect plot for EEDInteraction effect plot for EED
MeanofS/Nratios
252015
-4
-6
-8
-10
-12
20010050
20010050
-4
-6
-8
-10
-12
64.0050.2640.00
Current Pulse on time
Pulse off time Geometry
Main Effects Plot (data means) for S/ N ratios -EED
Signal-to-noise: Larger is better
Cur r e nt
-5
-10
-15
Pu lse on t im e
Pul se of f t im e
20010050
20010050
-5
-10
-15
252015
-5
-10
-15
Current
25
15
20
Pulse
200
on
time
50
100
Pulse
200
off
time
50
100
I nt er act ion Plot ( dat a means) f or S/ N r at ios - EED
Signal-to-noise: Larger is better

32. Delta Values and Influence Ranking
(Graphite Tool)
Current Pulse on
time
Pulse off
time
Tool cross
sectional area
or geometry
On
MRR
Delta 1.4687 4.3203 0.1165 4.2088
Rank 3 1 4 2
On
TWR
Delta 10.78 1.56 0.08 1.46
Rank 1 2 4 3
On SR Delta 0.968 4.808 0.425 0.158
Rank 2 1 3 4
On EED Delta 4.172 8.225 2.039 4.531
Rank 3 1 4 2

33. Regression Equations (Graphite Tool)
In these equations W represents current in Amps, X represents pulse on time in µs, Y represents pulse off time in µs and Z
represents tool cross sectional area in mm2
.
MRR = - 0.009 + 0.0134 W - 0.00208 X - 0.000018 Y + 0.0167 Z
R-Sq = 81.7% R-Sq(adj) = 78.4%
TWR = - 0.190 + 0.0150 W - 0.000104 X + 0.000007 Y + 0.000749 Z
R-Sq = 99.3% R-Sq(adj) = 99.2%
SR = 2.89 + 0.0253 W - 0.00841 X- 0.000581 Y+ 0.00136 Z
R-Sq = 94.5% R-Sq(adj) = 93.5%
EED = 0.802 + 0.0171 W - 0.00223 X - 0.000637 Y - 0.00726 Z
R-Sq = 99.5% R-Sq(adj) = 99.4%
Their R2
values and adjusted R2
values are confirming the validity of the model as their values are above 95% for all the
responses except MRR where it is close to 80% and is acceptable too .

34. Model Validation
(Graphite Tool)
Factors MRR TWR SR EED
(mm3
/min) (mm3
/min) (µm) (º)
25A, 100µs,
100µs,
circular
Predicted 0.9555 0.2129 2.66 0.577
Exp. Result 0.9823 0.221 2.623 0.551
Variation 2.8% 3.8% 1.3% 4.5%
25A, 100µs,
100µs,
square
Predicted 1.185 0.2232 2.683 0.478
Exp. Result 1.152 0.2333 2.663 0.497
Variation 2.8% 4.5% 0.75% 4%

35. Results Data - Copper Tool
Exp.
No
MRR
(mm3
/min)
TWR
(mm3
/min)
SR
(µm)
EED
(º)
1 0.4982 0.0761 2.296 0.8322
2 0.5975 0.0813 2.0808 0.7731
3 0.2936 0.0534 1.1256 0.3219
4 0.6257 0.0829 2.1832 0.6906
5 0.7235 0.0865 1.8808 0.6128
6 0.425 0.0698 1.4552 0.3242
7 0.7985 0.0914 2.3832 0.6765
8 0.8976 0.1065 1.8768 0.371
9 0.5973 0.0797 1.2432 0.1088
10 0.5471 0.1671 2.2352 1.0962
11 0.6419 0.1803 2.1488 0.7682
12 0.3434 0.1556 1.3112 0.5058
13 0.6716 0.1832 2.3568 0.9081
14 0.7749 0.1877 1.9336 0.7854
15 0.4767 0.1606 1.5112 0.3194
16 0.8469 0.194 2.564 0.6753
17 0.9477 0.1975 2.0616 0.5879
18 0.6468 0.1799 1.3072 0.2693
19 0.5915 0.273 2.4168 1.0149
20 0.6939 0.2712 2.1184 0.9851
21 0.4104 0.2456 1.5792 0.6965
22 0.7244 0.2742 2.6208 1.0802
23 0.825 0.2893 2.0016 0.7535
24 0.5235 0.2625 1.4944 0.5357
25 0.8973 0.2943 2.42 0.8825
26 0.996 0.3 2.3304 0.7985
27 0.6998 0.2709 1.3632 0.2733

36. ANOVA table for MRR
(Copper tool)
Source DF Seq SS MS F P
Current 2 13.680 6.840 14.04 0.005
On Time 2 87.559 43.77 89.89 0.00
Off Time 2 0.432 0.2158 0.44 0.06
Geometry 2 21.359 10.67 21.93 .002
Current* on time 4 2.815 0.7.39 1.45 0.327
Current* off time 4 0.667 0.1669 0.34 0.84
Current * Geometry 4 13.51 3.379 6.94 0.019
Error 6 2.922 0.487
Total 26 142.952

37. ANOVA table for TWR
(Copper tool)
Source DF Seq SS MS F P
Current 2 376.05 188.02 660.61 0.000
On Time 2 13.991 6.995 24.58 0.001
Off Time 2 0.191 0.096 0.34 0.727
Geometry 2 150.744 75.372 264.81 0.00
Current* on time 4 2.427 0.607 2.13 0.194
Current* off time 4 0.555 0.139 0.49 0.746
Current * Geometry 4 51.878 12.969 45.57 0.00
Error 6 1.708 1.708 0.285
Total 26 597.544

38. ANOVA table for SR
(Copper tool)
Source DF Seq SS MS F P
Current 2 2.639 1.31 3.94 0.08
On Time 2 125.699 62.84 187.73 0.00
Off Time 2 0.794 0.3970 1.19 0.038
Geometry 2 0.148 0.0739 0.22 0.08
Current* on time 4 0.940 0.235 0.70 0.618
Current* off time 4 3.44 0.860 2.57 0.145
Current * Geometry 4 1.910 0.4775 1.43 0.332
Error 6 2.009 0.3348
Total 26 137.579

39. ANOVA table for EED
( Copper tool )
Source DF Seq SS MS F P
Current 2 98.507 49.254 12.02 0.008
On Time 2 447.152 223.576 54.58 0.00
Off Time 2 4.814 2.407 0.59 0.085
Geometry 2 0.913 0.456 0.11 0.06
Current* on time 4 18.898 4.725 1.15 0.416
Current* off time 4 20.235 5.059 1.23 0.388
Current * Geometry 4 114.062 28.515 6.96 0.019
Error 6 24.578 4.096
Total 26 729.159

40. Residual Analysis in MRR
(Copper tool)
Residual analysis plot for MRR – to check model adequacy

41. Residual Analysis in TWR
(Copper tool)
Residual analysis plot for TWR – to check model adequacy

42. Residual Analysis in SR
(Copper tool)
Residual analysis plot for SR – to check model adequacy

43. Residual Analysis in EED
(Copper tool)
Residual analysis plot for EED – to check model adequacy

44. Findings - Factor effect on MRR
(Copper tool)
Main effect plot for MRR
Interaction effect plot for MRR

45. Findings - Factor effect on TWR
(Copper tool)
Main effect plot for TWRInteraction effect plot for TWR

46. Findings - Factor effect on SR
(Copper tool)
Main effect plot for SRInteraction effect plot for SR

47. Findings - Factor effect on EED
(Copper tool)
Main effect plot for EEDInteraction effect plot for EED

48. Delta Values and Influence Ranking
(Copper Tool)
Current
Pulse on
time
Pulse off time
Tool cross
sectional area
or geometry
On
MRR
Delta 1.743 4.206 0.310 2.174
Rank 3 1 4 2
On Delta 8.41 1.75 0.20 5.79
TWR Rank 1 3 4 2
On
SR
Delta 0.675 5.154 0.415 0.170
Rank 2 1 3 4
On
EED
Delta 4.171 9.563 1.025 0.440
Rank 2 1 3 4

49. Regression Equations (Copper Tool)
In these equations W represents current in Amps, X represents pulse on time in µs, Y represents pulse off
time in µs and Z represents tool cross sectional area in mm2
.
MRR = 0.283 + 0.0112 W - 0.00166 X+ 0.000023 Y + 0.00699 Z
R-Sq = 87.0% R-Sq(adj) = 82.5%
TWR = - 0.220 + 0.0196 W - 0.000154 X+ 0.000014 Y + 0.000447 Z
R-Sq = 98.5% R-Sq(adj) = 98.2%
SR = 2.51 + 0.0207 W- 0.00714 X- 0.000430 Y - 0.00232 Z
R-Sq = 86.4% R-Sq(adj) = 83.9%
EED = 1.04 + 0.0253 W - 0.00348 X - 0.000956 Y - 0.00787 Z
R-Sq = 96.9% R-Sq(adj) = 96.3%

50. Model Validation
(Copper Tool)
Factors
MRR TWR SR EED
(mm3
/min) (mm3
/min) (µm) (º)
25A, 100µs,
Predicted 0.6574 0.0992 1.8441 0.6433
100µs,
circular Exp. Result 0.6891 0.1001 1.8512 0.5981
Variation 4.60% 0.90% 0.38% -7.56%
25A, 100µs,
Predicted 0.7099 0.1036 1.8518 0.63552
100µs,
square Exp. Result 0.7423 0.11 1.86 0.6456
Variation 4.36% 5.02% 0.44% 1.56%

51. Results Data – Brass Tool
Exp.
No
MRR TWR SR EED
(mm3
/min) (mm3
/min) (µm) (º)
1 0.79704 0.22815 4.1328 1.16508
2 0.95592 0.24375 3.74544 1.08234
3 0.46968 0.16029 2.02608 0.45066
4 1.00104 0.24882 3.92976 0.96684
5 1.15752 0.25935 3.38544 0.85785
6 0.67992 0.20943 2.61936 0.45381
7 1.27752 0.27417 4.28976 0.9471
8 1.43616 0.31941 3.37824 0.51933
9 0.95568 0.23907 2.23776 0.15225
10 0.87528 0.50115 4.02336 1.53468
11 1.02696 0.54093 3.86784 1.07541
12 0.54936 0.46683 2.36016 0.70812
13 1.07448 0.54951 4.24224 1.27134
14 0.7749 0.41298 2.32032 1.49226
15 0.9534 0.54587 3.32464 0.60677
16 1.6938 0.65946 5.6408 1.28307
17 1.8954 0.6714 4.53552 1.11692
18 1.2936 0.61173 2.87584 0.51158
19 1.1829 0.9282 5.31696 1.92831
20 1.3878 0.92201 4.66048 1.8716
21 0.8208 0.83494 3.47424 1.32326
22 1.4487 0.93218 5.76576 2.05229
23 1.65 0.98345 4.40352 1.43156
24 1.047 0.8924 3.28768 1.01774
25 1.7946 1.00069 5.324 1.67666
26 1.992 1.02014 5.12688 1.51706
27 1.3995 0.92113 2.99904 0.51927

52. ANOVA table for MRR
(Brass tool)
Source DF Seq SS MS F P
Current 2 54.069 27.0346 15.51 0.004
On Time 2 66.855 33.427 19.18 0.002
Off Time 2 1.445 0.7224 0.41 0.078
Geometry 2 108.29 54.149 31.07 0.001
Current* on time 4 4.919 1.229 0.71 0.616
Current* off time 4 4.998 1.249 0.72 0.610
Current* Geometry 4 7.974 1.993 1.14 0.420
Error 6 10.458 1.743
Total 26 259.017

53. ANOVA table for TWR
(Brass tool)
Source DF Seq SS MS F P
Current 2 642.48 321.24 553.17 0.000
On Time 2 7.024 3.512 6.05 0.036
Off Time 2 0.789 0.395 0.68 0.042
Geometry 2 15.92 7.96 13.71 0.006
Current* on time 4 6.109 1.527 2.63 0.139
Current* off time 4 1.894 0.474 0.82 0.559
Current * Geometry 4 4.183 1.046 1.80 0.247
Error 6 3.484 0.581
Total 26 681.885

54. ANOVA table for SR
(Brass tool)
Source DF Seq SS MS F P
Current 2 34.422 17.220 15.60 0.004
On Time 2 97.858 48.928 44.35 0.00
Off Time 2 0.155 0.0773 0.07 0.033
Geometry 2 2.732 1.366 1.24 0.095
Current* on time 4 3.539 0.8848 0.80 0.566
Current* off time 4 16.732 4.1831 3.79 0.072
Current * Geometry 4 7.106 1.776 1.61 0.286
Error 6 6.620 1.103
Total 26 169.164

55. ANOVA table for EED
(Brass tool)
Source DF Seq SS MS F P
Current 2 211.48 105.74 35.73 0.00
On Time 2 317.63 158.817 53.66 0.00
Off Time 2 19.93 9.96 3.37 0.105
Geometry 2 65.35 32.676 11.04 0.010
Current* on time 4 13.16 3.29 1.11 0.431
Current* off time 4 12.9 3.225 1.09 0.440
Current* Geometry 4 17.17 4.293 1.45 0.03
Error 6 17.76 2.96
Total 26 675.39

56. Residual Analysis in MRR
(Brass tool)
Residual analysis plot for MRR – to check model adequacy

57. Residual Analysis in TWR
(Brass tool)
Residual analysis plot for TWR – to check model adequacy

58. Residual Analysis in SR
(Brass tool)
Residual analysis plot for SR – to check model adequacy

59. Residual Analysis in EED
(Brass tool)
Residual analysis plot for EED – to check model adequacy

60. Findings - Factor effect on MRR
(Brass tool)
Main effect plot for MRR
Interaction effect plot for MRR

61. Findings - Factor effect on TWR
(Brass tool)
Main effect plot for TWRInteraction effect plot for TWR

62. Findings - Factor effect on SR
(Brass tool)
Main effect plot for SRInteraction effect plot for SR

63. Findings - Factor effect on EED
(Brass tool)
Main effect plot for EEDInteraction effect plot for EED

64. Delta Values and Influence Ranking
(Brass Tool)
Current
Pulse on
time
Pulse off time
Tool cross
sectional area or
geometry
On MRR
Delta 3.4069 3.6513 0.5525 4.8549
Rank 3 2 4 1
On Delta 11.8630 1.1413 0.380 1.8234
TWR Rank 1 3 4 2
On SR
Delta 2.711 4.614 0.161 0.694
Rank 2 1 4 3
On EED
Delta 6.82432 7.930 2.038 3.646
Rank 2 1 4 3

65. Regression Equations (Brass Tool)
In these equations W represents current in Amps, X represents pulse on time in µs, Y represents pulse
off time in µs and Z represents tool cross sectional area in mm2
.
MRR = 0.313 + 0.0210 W - 0.00245 X- 0.000063 Y+ 0.0131 Z
R-Sq = 81.0% R-Sq(adj) = 77.5%
TWR = - 0.764 + 0.0633 W - 0.000417 X+ 0.000014 Y+ 0.00195 Z
R-Sq = 99.1% R-Sq(adj) = 99.0%
SR = 2.84 + 0.109 W - 0.0122 X- 0.000980 Y+ 0.00125 Z
R-Sq = 93.0% R-Sq(adj) = 91.7%
EED = 1.01 + 0.0485 W- 0.00447 X- 0.00128 Y- 0.00928 Z
R-Sq = 96.1% R-Sq(adj) = 95.4%

66. Model Validation
(Brass Tool)
Factors
MRR TWR SR EED
(mm3
/min) (mm3
/min) (µm) (º)
25A, 100µs, 100µs Predicted 1.4765 0.8991 4.5348 1.5669
circular Exp. Result 1.5251 0.9232 4.3223 1.605
Variation 3.19% 2.61% -4.92% 2.37%
25A, 100µs, 100µs Predicted 1.7036 0.9343 4.638 1.4504
square Exp. Result 1.824 0.9589 4.7772 1.4921
Variation 6.60% 2.57% 2.91% 2.79%

67. Statistical Multi Objective Optimization Flow Diagram

68. Optimum Parameters Found in EDM
Optimum parameter
code ( tool)
Pulse
Current
(A)
Pulse-on
time (B)
Pulse-off
time(C)
Geometry (D) MRR TWR SR EED
A1 B1 C3 D2 ( Graphite) 15 50 200 cylindrical 0.39128 0.04103 1.40721 0.2145
Average Responses For all 27 experiments 0.87481 0.13716 2.4226 0.4357
Increase / reduction in response due to optimization - Graphite Tool -55% -70% -42% -51%
A1 B3 C1 D2 (Copper) 15 200 50 cylindrical 0.42495 0.06981 1.4552 0.3242
Average Responses For all 27 experiments 0.6561 0.1783 1.937 0.6535
Increase / reduction in response due to optimization - Copper Tool -35% -61% -25% -50%
A2 B3 C3 D2 (Brass) 20 200 200 cylindrical 1.03488 0.23976 2.35296 0.377
Average Responses For all 27 experiments 1.0609 0.5482 3.533 0.8674
Increase / reduction in response due to optimization - Brass Tool -2% -56% -33% -57%

69. Stages of Research in WEDM

70. Experiment - WEDM
Taguchi Design
• L18 orthogonal array
• Six factors at three levels
– Current
– Pulse on time
– Pulse off time
– Wire Tension
– Servo Voltage
– Wire Speed
Responses
• MRR
• Wire Offset
• Surface Roughness

71. Process Parameters & Levels for WEDM
(Decided through literature study and machine capability)
PARAMETERS
LEVELS
L1 L2 L3
Pulse on-time (Ton)
(µs)
3 8 13
Pulse off-time (Toff)
(µs)
15 30 45
Current (I)(A) 8 10 12
Wire tension (WT)
(N)
10 15 20
Servo voltage (SV)
(V)
17 34 51
Wire speed (WS)
(m/min)
5 10 15

72. Experiment Design
(Taguchi design using Minitab with the Consideration for Interaction Effects – L18 OA)
EXPERIMENT
NUMBER
PARAMETERS
1 2 3 4 5 6
1 1 1 1 1 1 1
2 1 2 2 2 2 2
3 1 3 3 3 3 3
4 2 1 1 2 2 3
5 2 2 2 3 3 1
6 2 3 3 1 1 2
7 3 1 2 1 3 2
8 3 2 3 2 1 3
9 3 3 1 3 2 1
10 1 1 3 3 2 2
11 1 2 1 1 3 3
12 1 3 2 2 1 1
13 2 1 2 3 1 3
14 2 2 3 1 2 1
15 2 3 1 2 3 2
16 3 1 3 2 3 1
17 3 2 1 3 1 2
18 3 3 2 1 2 3

73. Experimental Work
Titanium Grade 5
sheets
Work Piece Before Machining
(SODICK AQ300LWEDM) WEDM Cutting Operation

74. Experimental Work
Cut Pieces Titanium Grade 5 alloy Cut Pieces

75. Responses Measurement
MRR:
MRR =k.ρ.t.v
t = thickness of the workpiece = 3 mm
k = kerf width,mm
ρ = density of the work piece = 4.42g/cm³
v = cutting speed, mm/min
= Length of Cut / machining time
WIRE OFFSET:
Wire offset = (0.5 x diameter)+ overcut, mm
Overcut =(kerf width – wire diameter)/2,mm
Wire diameter= 0.25 mm
Kerf Width = Total width – ( Remaining width – cut
width), mm
SR :
Using surface roughness gauge.
Schematic Diagram for Wire Offset

76. Kerf Width Calculation
Exp.
No.
Machining
time (mm)
Total width
of material,
L(mm)
Uncut width
U1 (mm)
Cut width,
U(mm)
Kerf width=
L- U1-U, mm
1 10.47 89.88 84.6072 5.00 0.2728
2 5.28 84.6072 79.3186 5.00 0.2886
3 16.34 79.3186 74.0221 5.00 0.2965
4 4.55 74.0221 68.7344 5.00 0.2877
5 11.40 68.7322 63.4344 5.00 0.301
6 7.43 63.4344 58.1518 5.00 0.2816
7 6.39 58.1518 52.8476 5.00 0.3042
8 7.40 52.8476 47.5707 5.00 0.2769
9 6.54 47.5707 42.2707 5.00 0.3
10 7.24 42.52707 36.9747 5.00 0.2960
11 24.12 36.9747 31.6971 5.00 0.2776
12 9.07 31.6971 26.4128 5.00 0.2843
13 6.42 26.4128 21.1373 5.00 0.2755
14 5.05 21.1373 15.8499 5.00 0.2874
15 9.01 15.8499 10.5481 5.00 0.3018
16 6.07 10.5481 5.2514 5.00 0.2967
17 5.05 25.4107 20.1117 5.00 0.299
18 3.58 20.1117 148201 5.00 0.2919

77. WEDM Results Data
Exp.
No.
Pulse
On-
time
(µs)
Pulse
off-time
(µs)
Current
(A)
Wire
Tension
(N)
Servo
Voltage
(V)
Wire Speed
(mm/min)
MRR
*10e-3
(g/min)
Wire
Offset
(mm)
Surface
Roughness
(µm)
1 3 15 8 10 17 5 8.6374 0.13640 1.699
2 3 30 10 15 34 10 18.119 0.14430 2.072
3 3 45 12 20 51 15 6.0100 0.14825 2.059
4 8 15 8 15 34 15 20.961 0.14385 2.229
5 8 30 10 20 51 5 8.7500 0.15050 1.957
6 8 45 12 10 17 10 12.660 0.14080 2.029
7 13 15 10 10 51 10 15.780 0.15210 2.527
8 13 30 12 15 17 15 12.400 0.13845 2.172
9 13 45 8 20 34 5 15.206 0.15000 2.346
10 3 15 12 20 34 10 13.550 0.14800 1.683
11 3 30 8 10 51 15 3.8100 0.13880 1.885
12 3 45 10 15 17 5 10.391 0.14215 1.687
13 8 15 10 20 17 15 14.225 0.13775 2.026
14 8 30 12 10 34 5 18.866 0.14370 2.065
15 8 45 8 15 51 10 11.103 0.15090 2.307
16 13 15 12 15 51 5 16.204 0.14385 2.062
17 13 30 8 20 17 10 19.627 0.14950 2.418
18 13 45 10 10 34 15 27.029 0.15950 2.355

78. ANOVA table for MRR
Paramete
rs
DOF SS MS FCAL P
TON 2 100.4951 50.24755 10.1945 0.023
TOFF 2 9.450045 4.725023 0.95864 0.09
I 2 14.02578 7.012891 1.42281 0.06
WT 2 8.734862 4.367431 0.88609 0.10
SV 2 113.9028 56.95141 11.5546 0.021
WS 2 15.70865 7.854325 1.59353 0.07
ERROR 5 24.64447 4.928894
TOTAL 17 286.9617

79. ANOVA table for Wire Offset
Parameters DOF SS MS FCAL p
TON 2 0.3872 0.1936 2.697 0.006
TOFF 2 0.3064 0.1532 2.1341 0.01
I 2 0.1604 0.0802 1.1168 0.023
WT 2 0.1277 0.0639 0.8896 0.030
SV 2 0.7067 0.3534 4.9222 0.001
WS 2 0.1501 0.0751 1.0457 0.0291
ERROR 5 0.3589 0.0718
TOTAL 17 2.1975

80. ANOVA table for SR
Parameters DOF SS MS FCAL P
TON 2 11.734 5.867 12.579 0.001
TOFF 2 0.5595 0.2797 0.5998 0.049
I 2 0.8522 0.4261 0.9135 0.032
WT 2 0.0071 0.0036 0.0077 0.09
SV 2 1.1806 0.5903 1.2657 0.01
WS 2 2.3673 1.1836 2.5378 0.009
ERROR 5 2.332 0.4664
TOTAL 17 19.033

81. Residual Analysis in MRR - WEDM
(Brass wire)
Residual analysis plot for MRR – to check model adequacy

82. Residual Analysis in Wire Offset - WEDM
(Brass wire)
Residual analysis plot for EED – to check model adequacy

83. Residual Analysis in SR - WEDM
(Brass wire)
Residual analysis plot for SR – to check model adequacy

84. Findings - Factor effect on MRR - WEDM
(Means Graphs)
Factors effect plot for MRR ( Means)
Interaction effect plot for MRR
( Means)

85. Findings - Factor effect on Wire Offset - WEDM
(Means Graphs)
Factors effect plot for Wire Offset
( Means)
Interaction effect plot for Wire Offset
( Means)

86. Findings - Factor effect on SR - WEDM
(Means Graphs)
Factors effect plot for SR( Means)
Interaction effect plot for SR ( Means)

87. Delta Values and Influence Ranking - WEDM
(Brass Tool)
TON TOFF Current WT SV WS
On MRR
Delta 5.68 1.69 2.14 1.55 6.15 2.23
Rank 2 5 4 6 1 3
On Delta 0.35 0.29 0.22 0.20 0.44 0.20
Wire
Offset
Rank 2 3 4 5 1 6
On SR
Delta 1.969 0.418 0.525 0.047 0.563 0.835
Rank 1 5 4 6 3 2

88. Regression Model for MRR
Where A is pulse on time in micro seconds, B is pulse off time in micro seconds, C is current in Amperes, D is
wire tension in Newton, E is servo voltage in Volts and F is wire speed in mm/min.
MRR = - 83.2 + 2.54 A - 0.0354 A2
+ 0.193 B + 0.00161 B2
+ 10.4 C - 0.526 C2
- 0.76 D + 0.0226 D2
+ 2.21 E - 0.0340 E2
+ 1.74 F - 0.0782 F2
- 0.0381A*B
S. no Actual values Simulated Values Error %
1 16.2040 15.52715 4.18
2 19.6270 19.0175 3.11
3 27.0293 25.99515 3.83
Percentage of Error during Validation for 16, 17 & 18 Experiments

89. Regression Model for Wire Offset
Where A is pulse on time in micro seconds, B is pulse off time in micro seconds, C is current in Amperes, D is
wire tension in Newton, E is servo voltage in Volts and F is wire speed in mm/min.
WIRE OFFSET = 0.0032 + 0.00053 A + 0.000024 A2
- 0.000050 B + 0.000005 B2
+ 0.0200 C - 0.000999 C2
- 0.00108 D
+ 0.0000048 D2
+ 0.00145 E - 0.000018 E2
+ 0.00329 F - 0.000157 F2
- 0.000006 A*B
Percentage of Error during Validation
S.no Actual values Simulated values Error %
1 0.14385 0.143752 0.07
2 0.14950 0.145118 2.93
3 0.15950 0.155128 2.74

90. Regression Model for SR
Where A is pulse on time in micro seconds, B is pulse off time in micro seconds, C is current in Amperes, D is
wire tension in Newton, E is servo voltage in Volts and F is wire speed in mm/min.
SURFACE ROUGHNESS = - 0.843 + 0.188 A - 0.00264 A2
+ 0.0448 B - 0.000245 B2
+ 0.022 C - 0.00310 C2
- 0.0767 D
+ 0.00281 D2
+ 0.0664 E - 0.000932 E2
+ 0.161 F - 0.00687 F2
- 0.00301 A*B
Percentage of Error during Validation
S.no Actual values Simulated values Error %
1 2.062 2.0796 -0.85
2 2.418 2.454492 -1.51
3 2.355 2.3873 -1.37

91. Contour Plot based Parameter Optimization in MRR
(Maximization Objective)

92. Contour Plot based Parameter Optimization in MRR (Maximization
Objective)

93. Contour Plot based Parameter Optimization in MRR
(Maximization Objective)

94. Contour Plot based Parameter Optimization in MRR
(Maximization Objective)

95. Contour Plot Findings for Maximum MRR
Sl.No WEDM Parameters Range for maximum MRR
1 Pulse on time 11 to 13 µs
2 Pulse off time 25 to 35 µs
3 Current 9 to 11 A
4 Wire tension 10 to 14 N
5 Servo voltage 25 to 40 V
6 Wire speed 11 to 15 mm/min

96. Contour Plot based Parameter Optimization in Wire Offset
(Minimization Objective)

97. Contour Plot based Parameter Optimization in Wire Offset
(Minimization Objective)

98. Contour Plot based Parameter Optimization in Wire Offset
(Minimization Objective)

99. Contour Plot based Parameter Optimization in Wire Offset
(Minimization Objective)

100. Contour Plot Findings for Minimum Wire
Offset
Sl.No WEDM Parameters Range for Minimum Wire Offset
1 Pulse on time 3 to 6 µs
2 Pulse off time 15 to 35 µs
3 Current 9 to 12 A
4 Wire tension 10 to 14 N
5 Servo voltage 15 to 22 V
6 Wire speed 12.5 to 15 mm/min

101. Contour Plot based Parameter Optimization in SR (Minimization
Objective)

102. Contour Plot based Parameter Optimization in SR (Minimization
Objective)

103. Contour Plot based Parameter Optimization in SR (Minimization
Objective)

104. Contour Plot based Parameter Optimization in SR (Minimization
Objective)

105. Contour Plot Findings for Minimum Surface
Roughness
Sl.No WEDM Parameters Range for Minimum SR
1 Pulse on time 3 to 7 µs
2 Pulse off time 15 to 20 µs
3 Current 11 to 12 A
4 Wire tension 14 to 20 N
5 Servo voltage 15 to 30 V
6 Wire speed 5 to 7 mm/min

106. WEDM Optimization Results
Optimization
Technique
Pulse On-
time
Pulse off-
time
Current
Wire
Tension
Servo
Voltage
Wire
Speed
MRR
(g/min)
Wire Offset
Surface
Roughnes
s
(µs) (µs) (A) (N) (V) (mm/min) *10e-3 (mm) (µm)
SA
13 30 12 15 34 15 23.2994 0.138347 2.453248
Average Values for All 18 Experiments 14.0738 0.145489 2.087667
Performance Improvement/Reduction 65.55% -4.90% 17.51%
BFOA
11.2172 32.2172 11.2172 14.2172 35.2172 12.2172 24.44258 0.167533 2.493216
Average Values for All 18 Experiments 14.0738 0.145489 2.087667
Performance Improvement/Reduction 73.67% 15.15% 19.43%
Cuckoo
12.0259 34.0259 11.0259 14.0259 36.0259 12.0259 24.68218 0.168673 2.512946
Average Values for All 18 Experiments 14.0738 0.145489 2.087667

107. Conclusions – EDM - Slide1
By comparison brass tool is the best from MRR point of view. Its average MRR is 1.170
mm3
/min whereas graphite is at 0.8748 mm3
/min and copper is at 0.6561mm3
/min which is
the least performing in terms of MRR.
From TWR point of view, graphite is the best and brass is the worst. TWR average values
are for graphite 0.1371 mm3
/min, for copper 0.1783 mm3
/min and for brass 0.5769
mm3
/min.
From SR point of view, copper is the best with 1.937µm followed by graphite at 2.4226µm
and then brass at 3.8257µm.
From EED point of view, graphite is the best as it is not wearing because of very high
melting point , gives 0.4351º . Copper is at 0.6535º and brass results in 1.0938º.

108. Optimum values of factors were found out for each studied tool material
Regression models found out were validated and found to be very good in prediction and
are very useful for the industries who work with titanium grade 5 alloy for prediction and
to improve productivity with in the studied range of parameters.
In general, the interactive effects among the electrical parameters were not influencing
the responses significantly.
Based on the delta values, the current and pulse on time are the two most important and
critical process parameters that affects all the studied responses. Among these two factors
pulse on time is more critical.
Conclusions – EDM – Slide2

109. Except MRR, all other responses were negatively affected by increasing pulse current
value.
Pulse off time is not affecting any response except surface roughness in the studied
range.
Tool cross sectional area is influencing the entry exit deviation rather than the geometry.
Higher cross sectional area reduces the deviation.
Tool cross sectional area is not influencing in any way the surface roughness.
Conclusions – EDM – Slide3

110. To obtain maximum MRR, the percentage of contribution of each parameters were in the
order Servo Voltage(SV), Pulse On Time(TON), Wire Speed(WS), Current(I), Pulse Off
Time(TOFF), Wire Tension(WT). Among them SV and TON are the most influential
parameters and other parameters are less significant as their percentage of contribution
for maximizing the MRR is less than 10%.
To obtain minimum Wire offset value, the percentage of contribution of each parameters
were in the order SV, TON, TOFF, I, WT, WS. Among them SV, TON and TOFF are the
most influential parameters and other parameters are less significant as their percentage
of contribution for minimizing the Wire offset value is less than 10%.
Conclusions – WEDM - Slide1

111. To obtain minimum Surface Roughness value, the percentage of contribution of each
parameters were in the order TON, WS, SV, I, TOFF, WT. Among them TON and WS
are the most influential parameters and other parameters are less significant as their
percentage of contribution for minimizing the Surface Roughness value is less than 10%.
WEDM regression models found out were validated and found to be very good in
prediction and are very useful for the industries who work with titanium grade 5 alloy for
prediction and to improve productivity.
Conclusions – WEDM – Slide2

112. For brass wire, optimum parameters found through simulated annealing based multi
objective optimization were 12A pulse current, 13µs pulse on time, 30 µs pulse off time,
wire tension 15N, servo voltage 34V and wire speed 15 mm/min which resulted in 65.5%
increase in MRR and 4.9% reduction in wire offset compared to the averages of
corresponding responses in all 18 experiments. However SR is increased by 17.5% which
is a compromise made for getting high MRR.
Conclusions – WEDM – Slide3

113. Optimum parameters found through BFOA based multi objective optimization were
11.2172 A pulse current, 11.2172 µs pulse on time, 32.2172 µs pulse off time, wire tension
14.2172N, servo voltage 35.2172V and wire speed 12.2172 mm/min which resulted in
73.67% increase in MRR , 15.15% increase in wire offset and 19.43% increase in SR
compared to the averages of corresponding responses in all 18 experiments which is a
compromise made for getting very high MRR.
Conclusions – WEDM – Slide4

114. Optimum parameters found through COA based multi objective optimization were
11.0259 A pulse current, 12.0259 µs pulse on time, 34.0259 µs pulse off time, wire tension
14.0259 N, servo voltage 36.0259 V and wire speed 12.0259 mm/min which resulted in
75.38 % increase in MRR , 15.93 % increase in wire offset and 20.37 % increase in SR
compared to the averages of corresponding responses in all 18 experiments which is a
compromise made for getting very high MRR.
Conclusions – WEDM – Slide5

115. Suggestions for Future Work
1. Efforts should be made to investigate the effects of WEDM process parameters on
performance measures in a cryogenic cutting environment as this material is suitable to
be used in cryogenic environment.
2. The effect of process parameters such as flushing pressure, conductivity of dielectric, wire
diameter, work piece height etc. may also be investigated.
3. In this research for multi objective optimization, weighing method was used to combine
many objectives into one objective function. Instead multi-layer simultaneous search
based algorithms can be found to be used in such environments.

116. Publications – International Journals
1. Saravanan P Sivam, Antony L Michael Raj, Satish Kumar S, Varahamoorthy R and Dinakaran D.
“Effects of electrical parameters, its interaction and tool geometry in electric discharge machining of
titanium grade 5 alloy with graphite tool”. Proc. IMechE Part B: J. Engineering Manufacture, January
2013 vol. 227 no. 1 119-131.
2. Saravanan P Sivam, Antony L MichaelRaj, Satish Kumar S, Prbhakaran G, Dinakaran D and
Ilamkumaran V2014. “Statistical multi objective optimization of machining parameters in EDM of
titanium grade 5 alloy with graphite electrode”, Proceedings of Institution of Mechanical Engineers: Part B
– Journal of Engineering Manufacture, Vol. 228, Issue 7 July 2014 pp. 736 – 743. DOI:
10.1177/0954405413511073.
3. Saravanan P Sivam, Antony L MichaelRaj and Satish Kumar S, 2013.” Influence Ranking of Process
Parameters in Electric Discharge Machining of Titanium Grade 5 Alloy Using Brass Electrode”,
International Journal of Mechanical Engineering and Technology, 4(5):71-80.

117. Publications – International Conferences
1. Influence ranking of process parameters in electric discharge machining of titanium grade 5
alloy using graphite tool electrode. NCT 3rd
International Conference Proceedings, Oman 2012
, p17-18.
2. Influence Ranking of Process Parameters in Electric Discharge Machining of Titanium
Grade 5 Alloy Using Copper Electrode, Proceedings of ICRDPET, 2013, IEEE explore
transactions, India 2013, p72-78 ISBN 978 -1-4673-4948-2 2013IEEE.

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128. THANK YOU
One & All