This ppt involves NSGA II algorithm

1. A THESISA THESIS PRESENTATION ONPRESENTATION ON
Optimization of Process Parameters of Wire EDMOptimization of Process Parameters of Wire EDM
Process for Machining of Monel R-405 MaterialProcess for Machining of Monel R-405 Material
By
PINK RAJ
Registration No.: 321015024
2015-2017
Under The Guidance Of
Dr. Subhas Chandra Mondal
DEPARTMENT OF MECHANICAL ENGINEERING
INDIAN INSTITUTE OF ENGINEERING SCIENCE AND TECHNOLOGY
SHIBPUR, HOWRAH- 711103, WEST BENGAL
MAY-2017
1

2. 2
CONTENTSCONTENTS

3. 3
INTRODUCTION

4.  WEDM is an electro-thermal
machining process used for
machining of conductive and
difficult to cut materials.
 Dielectric used - deionized water.
 MRR mechanism - melting and
vaporization.
 Constant IEG maintain by
computer controlled positioning
system.
 Types of wires used- copper, brass,
stratified wires.
Fig. 1. The Wire EDM Process
troduction

5. 5
troduction
 The present applications of WEDM process includes automotive,
aerospace, mould, tool and die making industries. WEDM
applications can also be found in the medical, optical, dental,
jewellery industries.
Cont..

6. 6
LITERATURE REVIEWLITERATURE REVIEW

7. 7
References Materials Results
Huang et al.[1], 2003 high-speed steel
(ASP 23)
Recast layer observed between
the steel and wire electrode.
Hascalyk et al.[2], 2004 AISI D5 tool steel. Intensity of the process energy
does affect the amount of
recast and surface roughness
as well as micro-cracking.
Hewidy et al.[3], 2005 Inconel 601 Volumetric metal removal rate
generally increases with the
increase of the peak current
value and water pressure,
within a certain limit.
Choi et al.[4], 2008 Die steel STD11 Heat treatments after WEDM
improve the quality in terms of
microstructures and surface
roughness
Literature ReviewLiterature Review

8. 8
References Materials Results
Sharma et al. [5], 2013 High strength low alloy
Steel (HSLA)
MRR and S.R increases with
increasing Ton and I.P.
MRR and S.R decreases with
Increasing Toff and S.V.
Wire tension has no
significant role on MRR and
S.R.
Raju et al.[6], 2014 316 L Stainless Steel Pulse on time is most
important parameter for
surface roughness and kerf
width.
Shivade et al. [7], 2014 D3 tool steel Current and pulse on time
have significant effect on
MRR.
Literature ReviewLiterature Review Cont..

9. 9
References Materials Results
Kumar et al. [8], 2015 Al–SiC–B4C
Aluminum based
composite
Pulse –on time play 96.19% role
on the surface roughness and
kerf width.
Singh et al.[9], 2015 EN8 Steel Increasing the wire feed rate the
dimensional deviation
decreases. Increasing the pulse
off time initially dimensional
deviation increases and further it
decreases. Increasing servo
voltage dimensional deviation
decreases.
Mandal et al.[10], 2006 C40 Steel A pareto-optimal set of 100
solutions is obtained.
Literature ReviewLiterature Review Cont..

10. 10
Monel R-405 Material
Chemical Composition:
Chemical Composition Percentage
Nickel (Ni) 63%
Copper (Cu) 32%
Manganese (Mn) 2%
Iron (Fe) 2.2%
Silicon (Si) 0.5%
Sulfur (S) 0.05%
Carbon (C) 0.3%
Table 1. Chemical composition of Monel R-405 material [11]
Monel R-405 is the free machining version of Monel 400. It is a nickel-copper
alloy with a controlled amount of sulfur added to provide sulfide inclusions that
act as chip breakers during machining.

11. 11
Characteristics:
Resistant to seawater and steam at high temperature
High resistance to alkalis
Good machinability
Particularly resistant to hydrochloric and hydrofluoric acid when they are
de-aerated
Applications:
Feed water and steam generator tubing
Transfer piping from oil refinery crude columns
Cladding for upper areas of oil refinery crude columns
Meter and valve parts
Monel R-405 Material Cont..

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Objectives
 Study the effects of various input process parameters on the output
responses (MRR, Surface roughness).
 Development of models for the Surface roughness and MRR using
Response surface methodology.
 Identify optimal parameter settings of the WEDM process for
machining Monel R-405 material using Non-dominated sorting
genetic algorithm-II.

13. 13
Research Methodology
Fig. 2. Flow chart of proposed research work

14. 14
EXPERIMENT

15. 15
Table 2.The input process parameters and their levels in WEDM
Experimentation
Fig. 3. Monel R-405 Sample of dimensions (400mmX10mmX10mm)

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Fig. 4. Monel R-405 workpiece loaded in WEDM
Cont..Experimentation

17. 17
Fig. 5. Initial length of cut 7mm by WEDM
Fig. 6. Optical Microscope for measurement of Kerf width
Cont..Experimentation

18. 18
Fig. 7. Kerf Width Measurement
Fig. 8. Surface Roughness measurement
Cont..Experimentation

19. 19
Calculation of material removal rate (MRR)
The material removal rate is calculated in mm3
/min by using given formula
MRR = Volume of material removal / Time
= (Length of cut * kerf *thickness of cut)/ time take to cut
Or we can say that MRR = thickness of workpiece material* kerf * cutting velocity
MRR =K*t*l/T
Where k = kerf width in mm, t = thickness of workpiece m/t in mm,
l = length of cut in mm, T = time taken to cut in min

20. 20
S.no.S.no. TonTon ToffToff II SVSV WFWF Kerf(mm)Kerf(mm) MRR(mm3/min)MRR(mm3/min) SR (µm)SR (µm)
1 105 50 130 60 5 0.3453 1.6222 1.50
2 105 50 130 60 6 0.3314 1.6689 1.63
3 105 50 130 60 7 0.3263 1.4099 1.49
4 105 55 140 70 5 0.3461 0.9428 1.76
5 105 55 140 70 6 0.3344 0.9359 1.67
6 105 55 140 70 7 0.3273 0.8613 1.43
7 105 60 150 80 5 0.3398 0.5676 1.33
8 105 60 150 80 6 0.3385 0.5575 1.31
9 105 60 150 80 7 0.3376 0.547 1.28
10 110 50 140 80 5 0.3318 1.7268 2.07
11 110 50 140 80 6 0.3322 1.7603 2.19
12 110 50 140 80 7 0.3298 1.7126 2.06
13 110 55 150 60 5 0.3460 2.2784 2.21
14 110 55 150 60 6 0.3425 2.2364 2.31
15 110 55 150 60 7 0.3322 2.1491 2.38
16 110 60 130 70 5 0.3339 1.3954 2.01
17 110 60 130 70 6 0.3354 1.361 2.07
18 110 60 130 70 7 0.3422 1.383 2.10
19 115 50 150 70 5 0.3390 4.1413 2.51
20 115 50 150 70 6 0.3549 4.2466 2.74
21 115 50 150 70 7 0.3584 3.8596 2.50
22 115 55 130 80 5 0.3480 2.2597 2.41
23 115 55 130 80 6 0.3456 2.2194 2.36
24 115 55 130 80 7 0.3316 2.1078 2.53
25 115 60 140 60 5 0.3293 3.0132 2.45
26 115 60 140 60 6 0.3432 2.9985 2.61
27 115 60 140 60 7 0.3414 2.9309 2.88
Design matrix and output responses
Table 3. Design matrix and output responses

21. 21
RESULTS AND DISCUSSION

22. 22
Results and Discussion
Development of Model: Response surface methodology used for developing
the model. Minitab 17 used for RSM analysis.
Fig. 9. Normal probability plot for material removal rate (MRR)

23. 23
Results and Discussion Cont..
Fig. 10. Normal probability plot for surface roughness (SR)

24. 24
Analysis for MRR:
The discharge energy increases with the pulse on time and peak current, so more
material removes through the workpiece. As the pulse off time increases, the
number of discharges decreases so MRR decrease. With the increase in servo
voltage the average discharge gap gets widened resulting MRR decreasing.
Results and Discussion
Fig. 11. Effect of process parameters on the material removal rate (MRR)
Cont..

25. 25
Results and Discussion
Fig. 12. Surface plot of MRR vs Ton, Toff Fig. 13. Surface plot of MRR vs Ton, I
Cont..

26. 26
Results and Discussion
Fig. 14. Surface plot of MRR vs Ton, SV Fig. 15. Surface plot of MRR vs Ton, WF
Cont..

27. 27
Analysis for SR:
Larger discharge energy produces a larger crater, causing a larger surface
roughness. With the increase in servo voltage the average discharge gap gets
widened resulting into better surface accuracy due to stable machining.
Results and Discussion
Fig. 16. Effect of the input process parameters on the surface roughness (SR)
Cont..

28. 28
Results and Discussion
Fig. 17. Surface plot of SR vs Ton, Toff Fig. 18. Surface plot of SR vs Ton, I
Cont..

29. 29
Results and Discussion
Fig. 19. Surface plot of SR vs Ton, SV Fig. 20. Surface plot of SR vs Ton, WF
Cont..

30. 30
ANOVA for MRR:
Source DF SS Adj SS Adj MS F P Percentage
Contribution
Ton (μs) 2 19.7940 19.7940 9.89702 2129.30 0.000 70.74
Toff (μs) 2 3.4858 3.4858 1.74288 374.97 0.000 12.46
I (Amp) 2 1.5705 1.5705 0.78523 168.94 0.000 5.61
S.V (Volt) 2 2.9788 2.9788 1.48941 320.44 0.000 10.64
WF (m/min) 2 0.0749 0.0749 0.03743 8.05 0.004 0.27
Error 16 0.0744 0.0744 0.00465 0.28
Total 26 27.9783 100
Results and Discussion
Table 4. ANOVA for material removal rate
Cont..

31. 31
Results and Discussion
Fig. 21. Percentage contribution of input process parameter for MRR
Cont..

32. 32
ANOVA for Surface roughness:
Source DF Seq SS Adj SS Adj MS F P Percentage
Contribution
Ton (μs) 2 5.21690 5.2169 2.60845 186.07 0.000 90.06
Toff (μs) 2 0.05925 0.05925 0.02963 2.11 0.153 1.02
I (Amp) 2 0.05792 0.05792 0.02896 2.07 0.159 0.99
SV (Volt) 2 0.21103 0.21103 0.10551 7.53 0.005 3.7
WF (m/min) 2 0.02323 0.02323 0.01161 0.83 0.455 0.4
Error 16 0.2243 0.2243 0.01402 3.29
Total 26 5.79263 100
Results and Discussion
Table 5. ANOVA for Surface roughness
Cont..

33. 33
Results and Discussion
Fig. 22. Percentage contribution of input process parameter for surface roughness
Cont..

34. 34
Multi-Objective Optimization:
To convert the first objective function (MRR) for minimization, it is suitably
modified. The objective functions are given below.
Objective 1 = - (MRR)
Objective 2 = Surface Roughness
Most of the multi objective algorithms gives a set of solutions. This set of
solution known as pareto – optimal solution.
For this research work we use multi objective optimization technique NSGA II.
Results and Discussion Cont..

35. 35
The optimization running in Mat Lab -2016b. version and an initial size of 200
populations are chosen, for achieving better convergence, a generation of 1000 is used
in the study and other features are default.
Results and Discussion Cont..

36. 36
In the pareto- optimal solution sets any of the solution is not better than other, means
all solutions is better, the choice of one solution over other depends on the
requirements of the process engineer.
Results and Discussion
Fig. 24. Pareto optimal front for objective MRR and SR
Cont..

37. 37
Results and Discussion
Table 6. Optimal combination of parameters
S.no. Ton Toff I SV WF MRR SR
1 105.049 50.02028 149.869 75.06183 6.996914 1.62869 1.311969
2 114.9966 50.00775 149.9821 65.24295 5.169513 4.26211 2.547308
3 105.007 50.0119 149.938 78.4865 6.996395 1.39078 1.236744
4 105.0407 50.00733 149.9236 66.94045 6.436175 2.05972 1.527179
5 110.9705 50.04503 149.9906 65.99495 5.064142 3.13708 2.289557
6 112.2173 50.03396 149.9696 66.02673 5.041638 3.44234 2.379433
7 105.0272 50.0286 149.9408 67.55946 6.840431 1.97797 1.455632
8 108.9478 50.02665 149.9548 65.71607 5.144942 2.72187 2.110427
9 105.1265 50.01639 149.9266 66.93402 6.343631 2.07861 1.553405
10 107.8066 50.00861 149.9478 65.89425 5.107972 2.5222 1.982439
11 114.6555 50.08858 149.9578 66.13228 5.056303 4.12276 2.514248
12 114.8016 50.02524 149.9782 65.46647 5.105052 4.19419 2.529092
13 109.7181 50.03646 149.9631 66.40347 5.358234 2.85483 2.196596
14 105.5188 50.0242 149.9471 66.01122 5.651375 2.19595 1.680108
15 105.0303 50.04612 149.9241 67.75014 6.902108 1.95817 1.443454
16 110.1435 50.06091 149.9623 66.19062 5.171841 2.94523 2.227338
17 105.1705 50.01226 149.9245 65.97524 6.277102 2.11058 1.577637
18 114.4583 50.02357 149.9673 65.7456 5.106532 4.08237 2.513826
19 105.0963 50.02247 149.934 69.3305 6.986386 1.9078 1.416027
20 105.0445 50.02011 149.8718 76.02483 6.997634 1.56696 1.292994
21 108.3954 50.00837 149.9585 65.4024 5.116991 2.63109 2.050863
22 111.7838 50.04546 149.9652 66.07843 5.104972 3.32912 2.356236
23 109.1749 50.02753 149.9688 65.67048 5.176769 2.76634 2.135696
24 105.0487 50.02015 149.9337 73.84059 6.990908 1.70438 1.334172
25 105.0393 50.02632 149.9279 71.35048 6.994401 1.82343 1.373813
26 105.247 50.02175 149.9475 65.97519 5.686743 2.16622 1.637767
27 105.0156 50.04936 149.9315 77.02526 6.989992 1.4947 1.270412
28 113.6954 50.04141 149.975 65.94447 5.05908 3.84811 2.469838
29 105.0629 50.018 149.9218 67.27785 6.75478 2.00397 1.479842
30 112.9494 50.05138 149.995 65.96057 5.079169 3.63645 2.430732
31 105.089 50.02257 149.8968 70.97662 6.987114 1.8443 1.390237
32 114.9788 50.04002 149.9916 65.2618 5.128376 4.25107 2.540585
33 111.8709 50.02953 149.9445 66.17664 5.280854 3.34895 2.379377
34 106.2369 50.03617 149.9504 65.9808 5.17231 2.28818 1.789081
35 109.9391 50.04733 149.9638 66.00766 5.047543 2.908 2.199437
36 106.3384 50.0367 149.9577 66.05013 5.285854 2.30057 1.803205
37 105.0555 50.02324 149.9156 70.7014 6.934629 1.86285 1.398971
Cont..

38. 38
Results and Discussion
Table 6. (Continued)
S.no. Ton Toff I V WF MRR SR
38 111.0903 50.03433 149.9553 65.9826 5.137264 3.16554 2.306556
39 111.7835 50.01646 149.9679 66.68849 5.05641 3.32144 2.347901
40 111.4172 50.03876 149.9527 66.08547 5.116951 3.23998 2.330062
41 113.3942 50.03818 149.9734 65.97427 5.059152 3.76166 2.453675
42 108.1275 50.01279 149.94 66.22689 5.644396 2.55675 2.029314
43 105.0542 50.04694 149.9454 78.07247 6.990837 1.42223 1.255761
44 105.0056 50.04788 149.9122 77.35843 6.996751 1.46783 1.260775
45 112.7075 50.06326 149.9692 66.28503 5.016151 3.5602 2.407281
46 114.0382 50.04997 149.9919 66.08914 5.050984 3.94519 2.485034
47 108.0479 50.01032 149.9416 67.3554 5.400328 2.52685 2.011394
48 105.007 50.0119 149.938 78.4865 6.996395 1.39078 1.236744
49 106.5258 50.04004 149.9456 66.16609 5.307359 2.32152 1.827877
50 113.908 50.06911 149.9679 66.40729 5.030184 3.8948 2.475076
51 109.3292 50.03609 149.9669 66.36706 5.343346 2.77894 2.156847
52 114.2359 50.04246 149.9939 65.94454 5.057875 4.00893 2.495725
53 107.3912 50.03832 149.9701 67.4625 5.205334 2.41666 1.929042
54 114.8485 50.02177 149.983 65.42464 5.118184 4.21042 2.533048
55 109.7994 50.06974 149.9569 67.15386 5.141845 2.84897 2.188788
56 106.9104 50.02248 149.9485 66.04539 5.30842 2.38058 1.878246
57 106.6623 50.02003 149.9578 65.63007 5.140355 2.35442 1.845224
58 108.656 50.01512 149.9518 65.96374 5.176157 2.66578 2.080106
59 105.0221 50.03731 149.9067 73.93249 6.989838 1.69233 1.329115
60 112.4251 50.03253 149.9715 65.58383 5.056805 3.5053 2.396642
61 110.7804 50.05639 149.9661 66.34066 5.051664 3.0827 2.271935
62 110.0105 50.04377 149.947 66.1638 5.08078 2.91991 2.208238
63 113.1556 50.02099 149.9872 65.34626 5.092882 3.7101 2.446648
64 107.4353 50.02893 149.9458 65.98918 5.129027 2.45775 1.939577
65 105.0448 50.03589 149.9248 67.0797 6.543092 2.03686 1.51231
66 107.695 50.02276 149.9502 67.04802 5.35147 2.47607 1.970764
67 107.1821 50.01211 149.9608 65.92241 5.807059 2.40577 1.908218
68 109.4983 50.0117 149.9695 65.70025 5.107194 2.83193 2.162939
69 108.9622 50.01102 149.9662 65.7168 5.158125 2.72828 2.112266
70 106.0744 50.02317 149.9868 65.8373 5.189868 2.27512 1.767347
Cont..

39. 39
From the experimental results presented in Table 3 the parameters listed in the
experiment number 19 leads to MRR value of 4.1413 mm3
/min and SR value of
2.51µm. By optimizing using multi-objective genetic algorithm tool, the values
obtained for MRR and SR in solution set number 17 are 4.12276 mm3
/min,
2.514248 µm which is approximately same the settings of input parameters are
nearly same, again we take experiment number 19 and compare it optimal
solution no 2 in optimal solution set Table 6, the MRR and SR is 4.2611
mm3
/min and 2.5473µm which is nearly equal to experiment no 19 value, thus
we can say that the algorithm which we applied is perfect.
Results and Discussion Cont..

40. 40
CONCLUSIONS AND SCOPE
FOR FUTURE WORK

41. 41
 Material removal rate increases with the increase of pulse on time,
peak current and decreases with the increase of pulse off time, servo
voltage and wire feed.
 Surface roughness increases with the increase of pulse on time, and
peak current and decreases with increase in pulse off time, servo
voltage, and wire feed.
 It is seen from the ANOVA analysis that the percentage contribution of
pulse on time is 70.74%, pulse off time is 12.46%, peak current is
5.61%, servo voltage is 10.64%, wire feed rate is 0.27% for material
removal rate.
Conclusions

42. 42
 The percentage contribution of pulse on time is 90.06%, pulse off
time is 1.02%, peak current is 0.99%, servo voltage is 3.7%, wire
feed rate is 0.4% for Surface roughness
 In order to simultaneously optimize both MRR and SR, NSGA II is
adopted to obtain pareto-optimal front. Since none of the solutions
in the pareto-optimal front is said to be absolutely better than any
other. Any one of them is an acceptable solution. This provides
flexibility to the process engineer to choose one solution over the
other depending on the requirement
Conclusions Cont..

43. 43
For the future work we should take and measure different input
parameters such as wire tension, water pressure, and change of
the dielectric fluid on material removal rate and surface roughness.
The results could analyze using other optimization techniques such
as particle swarm optimization technique, strength pareto
evolutionary algorithm and simulated annealing and their results
may be compared.
Scope For Future Work

44. 44
[1] Huang, C.A., Hsu, C.C. and Kuo, H.H., 2003. The surface characteristics of P/M high-
speed steel (ASP 23) multi-cut with wire electrical discharge machine (WEDM). Journal of
Materials Processing Technology, 140(1), pp.298-302.
[2] Hasçalyk, A. and Caydas, U., 2004. Experimental study of wire electrical discharge
machining of AISI D5 tool steel. Journal of Materials Processing Technology, 148(3), pp.362-
367.
[3] Hewidy, M.S., El-Taweel, T.A. and El-Safty, M.F., 2005. Modelling the machining
parameters of wire electrical discharge machining of Inconel 601 using RSM. Journal of
Materials Processing Technology, 169(2), pp.328-336.
[4] Choi, K.K., Nam, W.J. and Lee, Y.S., 2008. Effects of heat treatment on the surface of a
die steel STD11 machined by W-EDM. journal of materials processing technology, 201(1),
pp.580-584.
[5] Sharma, N., Khanna, R., Gupta, R.D. and Sharma, R., 2013. Modeling and multi response
optimization on WEDM for HSLA by RSM. The International Journal of Advanced
Manufacturing Technology, 67(9-12), pp.2269-2281.
[6] Raju, P., Sarcar, M.M.M. and Satyanarayana, B., 2014. Optimization of Wire Electric
Discharge Machining Parameters for Surface Roughness on 316 L Stainless Steel Using Full
Factorial Experimental Design. Procedia Materials Science, 5, pp.1670-1676
References

45. 45
[7] Shivade, A.S. and Shinde, V.D., 2014. Multi-objective optimization in WEDM of D3 tool
steel using integrated approach of Taguchi method & Grey relational analysis. Journal
of Industrial Engineering International, 10(4), pp.149-162.
[8] Kumar, S.S., Uthayakumar, M., Kumaran, S.T., Parameswaran, P., Mohandas, E.,
Kempulraj, G., Babu, B.R. and Natarajan, S.A., 2015. Parametric optimization of wire
electrical discharge machining on aluminum based composites through grey relational
analysis. Journal of Manufacturing Processes, 20, pp.33-39.
[9] Singh, P., Chaudhary, A.K., Singh, T. and Rana, A.K., 2015. Experimental Investigation of
Wire EDM to Optimize Dimensional Deviation of EN8 Steel through Taguchi’s
Technique.
[10] Mandal, D., Pal, S.K. and Saha, P., 2007. Modeling of electrical discharge machining
process using back propagation neural network and multi-objective optimization using
non-dominating sorting genetic algorithm-II. Journal of Materials Processing
Technology, 186(1), pp.154-162.
[11] http://www.hpalloy.com/Alloys/descriptions/MONELR_405.aspx
References Cont..

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