parametric optimization of machining parameters …

www.tjprc.org SCOPUS Indexed Journal [email protected]

PARAMETRIC OPTIMIZATION OF MACHINING PARAMETERS BY USING

ANNEALED COPPER WIRE ELECTRODE ON WIRE ELECTRIC DISCHARGE

MACHINING

I. HARISH1*, SANTOSH PATRO2 & P SRINIVASA RAO3*

1Research Scholar, Centurion University of Technology and Management, Odisha, India

2Mechanical Engineering Department, Centurion University of Technology and Management, Odisha, India

3Mechanical Engineering Department, Centurion University of Technology and Management, Odisha, India

ABSTRACT

In this work Annealed Copper wire is used to find performance of Wire Electrical Discharge Machining (WEDM) on D2

Steel with input variables like T ON time, Input Current, T OFF time, spark gap set voltage, wire runoff time and

Tension of the wire. The experiments are conducted as per the standard Taguchi’s L27 orthogonal array. The multiple

performances like metal removal rate (MRR), Tool wear ratio (TWR), Surface roughness and kerf width (KW) are

optimized by employing a Multi criteria decision making method called Technique for order preference by similarity to

ideal solution (TOPSIS). By the results, the optimal arrangement of input variables are T ON-time 120 µs, T OFF- time

30 µs, Spark gap set Voltage 20 volts, Input Current 210 amps, Wire Tension 10 Kg-f and Wire runoff 2 m/min. Later,

Analysis of variance is implemented and it shows that the T ON-time is the most important parameter that affects the

output performances.

KEYWORDS: T ON time, T OFF time, Kerf Width (KW), Metal Removal Rate (MRR), Surface Roughness, AHP and

TOPSIS.

Received: Jun 03, 2020; Accepted: Jun 23, 2020; Published: Jun 29, 2020; Paper Id.: IJMPERDJUN2020134

1. INTRODUCTION

In the field of mechanical industry, the need for alloy materials having high rigidity, toughness and impact load

resistance are growing. However, such materials are hard to be machined by traditional machining methods.

Therefore, Non-Conventional machining methods like Electrical Discharging Machining (EDM) are replacing

conventional machining operation. There are two main types of EDM: Die sinker and Wire. Die sinker EDM uses a

tool which acts as cathode and runs along the work piece which acts as anode and the electrical current reacts to

melt or vaporize the metal. As a result of the dielectric fluid, whatever little debris is produced washes away from

the work piece. WEDM with a thin wire as an electrode transforms electrical energy to thermal energy for cutting

materials. With this process, alloy steel, conductive ceramics and aerospace materials can be machined irrespective

to their hardness and toughness. Furthermore, WEDM is capable of producing a fine, precise, corrosion and wear

resistant surface. Kumar et. al. [1] uses four input parameters namely Input current, Ton time, Toff time & servo

voltage and two performance characteristics namely MRR and surface roughness. They use response surface graphs

to optimize multi-performance characteristics and desirability function employed. They find that low discharge

energy and more value of Toff time results in low defects on machined faces. Patro et. al. [2] found the optimum

parameters for the performance of WEDM by using Fuzzy modeling on D2 Steel. They conclude that modeled

values are in accordance to the experimental values with more than 90 per cent accuracy. Ugrasen et. al. [3] uses

Orig

ina

l Article

International Journal of Mechanical and Production

Engineering Research and Development (IJMPERD)

ISSN (P): 2249–6890; ISSN (E): 2249–8001

Vol. 10, Issue 3, Jun 2020, 1513-1526

© TJPRC Pvt. Ltd.

1514 I. Harish, Santosh Patro & P Srinivasa Rao

Impact Factor (JCC): 8.8746 SCOPUS Indexed Journal NAAS Rating: 3.11

molybdenum wire as electrode to find the optimized results by using Artificial Neural Networks in wire EDM taking 3

output values namely surface roughness, accuracy, volumetric MRR. Oliver et. al [4] did the EDM parameters optimization

for HCHCr tool steel by TOPSIS. Later, ANOVA were implemented to find the validity of regression model and

concluded the important parameters affecting the surface roughness. Muniappan et. al. [5] found effects of three wire

electrodes in wire electrical discharge machining (WEDM) namely Brass, diffused coated & zinc coated wire to get right

wire electrode for the process performance in terms of cutting speed. Senthil et. al. [6] presented the work which focus on

optimization of process parameters in Wire EDM of metal matrix composites Al-CuTiB2 in which they take outputs

namely MRR, surface roughness (SR) &TWR, they used TOPSIS to solve multi criteriadecision making problem in EDM

process. Luca et. al. [7] used two types of electrodes(zinc-coated copper wire & brass wire) in WEDM on IN718 metal to

find the optimized input parameters from the levels of pulse on time, pulse off time and wire feed, by taking the output

responses and characteristics of recast layer. Sarat et. al. [8] investigated the consequence of process parameters namely

pulse duration, servo voltage and wire runoff on output variables like MRR, cutting speed, surface roughness and kerf

width on D2 steel in wire EDM using TOPSIS method to select the optimal level of machining parameters. Ugrasen et. al.

[9] performed the process parameters optimization in wire Electric discharge machining on D2 steel,where as Taguchi’s

L27 orthogonal array was used to get the optimized process parameters by taking output variables namely volumetric

MRR,surface roughness and accuracy. This work mainly focus on process parameters optimization of WEDM by taking

the output variables namely Kerf width, MRR, surface roughness & Tool wear ratio on D2 (High carbon high chromium)

steel. Experimental trials are made according to Taguchi’s L27 array by using the inputvariables (Ton, Toff, spark voltage,

wire runoff time, input current & wire tension) at three levels. MCDM (multi criteria decision making) technique namely

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to optimize the responses. Later,

Analysis of variance (ANOVA) applied to get the importance of input variables on output values.

2. EXPERIMENTAL SETUP

In Present work, experiments are conducted on D2 steel with 10 mm thick on Wire Electric discharge machining

(Electronica Sprint cut shown in Fig.1) with an Annealed copper wire having 0.25 mm diameter electrode. D2 steel is used

in manufacturing of Dies,shafts, gears, spindles, automotive components etc. Process parameters of Peak current (IP),

TON, TOFF, Servo Voltage (SV), Wire runoff time (WF) and Wire Tension (WT) in the ranges as shown in Table 1.

Taguchi’s L27 array was used in the experimentation is shown in Table 2. The surface quality was taken by Surtronic 3+ as

shown in Fig. 3. The surtronic 3+ is a surface-roughness measuring instrument, which finds the surface of different

machined surfaces and gives surface roughness values accurately which projects the values in μm. Cubes shown in Fig. 2

are tested for roughness at3 machined faces to get more accurate values we will take average and made it as final value.

Figure 1: Sprint cut electric discharge Machine

Parametric Optimization of Machining Parameters by using Annealed Copper Wire 1515

Electrode on Wire Electric Discharge Machining


Figure 2: Cubes of work pieces after machining

Figure 3: Surface roughness measuring machine



Tool Wear Ratio (TWR) is the ratio of change in diameter to original diameter of wire and the diameter was

measured by using screw gauge, Kerf width calculated by means of Profile projector. MRR can be defined as the rate of

material removed per minute or the ratio of change in volume of work piece during machining divided by duration of

machining. It was measured by the below formula in which cutting speed was taken as the ratio of total length cut to time

taken for the total cut.

MRR = Vc x B x H

Where, Vc = Cutting speed of machining (mm/min)

B = Kerf width (mm)




H = Depth of cut (mm)

3. METHODOLOGY

3.1. Multi Criteria Decision Making Technique (TOPSIS)

This technique (TOPSIS) was used to finds the optimum set of values which are nearest to positive ideal solution (PIS) and

farthest from negative ideal solution (NIS). Different stages involved in this technique are:

Stage 1: First we have to construct decision matrix by using the information available in the criteria in which

decision matrix gives the performance of ith alternative with respect to jth criterion.

Stage 2: Then get the normalized decision matrix, with equation given below:

(1)

Stage 3: Evaluate weights for the each criterion which is done by using Analytical Hierarchy Process (AHP) with below

Stages:

(2)

Construct pair wise comparison matrix

Derive the Eigen value and Eigen vector

Find out the consistency index (CI) by formula (λmax –N) divided by (N-1).

Later, get the consistency ratio as it is the ratio of CI to Random index and it should be less than or equal to 0.1

which is an acceptable one.

Stage 4: In this stage, we have to find weighted normalized matrix:

(3)

Stage 5: Find out PIS and NIS values by the following equations:



(4)

(5)

Stage 6: Acquire the separation values namely Si+ and Si

-.

Stage 7: Find relative closeness values for each alternative and it is calculated as follows:

(6)

Stage 8: Now arrange these Ci+ values for each alternative in descending order and prepare rankings from highest to lowest

and the highest value is the one which is closer to ideal solution.

4. RESULTS AND DISCUSSIONS

In this work 27 experimental trails are conducted to find the influence of process parameters in wire electric discharge

machining on multiple responses namely kerf width, MRR, surface roughness and Tool wear ratio and they are shown in

Table 3.

Table 3: Output Results

S.No Kerf

Width

(mm)

MRR

(mm3/s)

TWR

(mm3/s)

SR (μm)

1 0.237 0.100 0.02 1.74

2 0.273 0.116 0.028 2.32

3 0.313 0.135 0.036 1.88

4 0.317 0.126 0.02 3.38

5 0.34 0.157 0.02 2.7

6 0.35 0.173 0.02 2.97

7 0.345 0.092 0.012 1.74

8 0.358 0.093 0.016 1.7

9 0.301 0.079 0.008 1.75

10 0.247 0.140 0.02 2.98

11 0.238 0.137 0.02 3.03

12 0.242 0.136 0.032 2.81

13 0.245 0.122 0.02 4.01

14 0.234 0.142 0.16 3.81

15 0.234 0.149 0.2 3.43

16 0.236 0.080 0.18 2.78

17 0.25 0.082 0.008 2.49

18 0.255 0.081 0.016 2.11

19 0.401 0.218 0.02 4.12

20 0.375 0.232 0.016 4.1

21 0.355 0.222 0.06 3.76

22 0.342 0.210 0.012 3.54

23 0.315 0.212 0.016 4.36

24 0.31 0.223 0.036 3.6

25 0.31 0.159 0.04 3.37

26 0.31 0.165 0.04 3.06




27 0.321 0.168 0.02 2.15

Now, these output responses are normalized by using equation (1) and it was shown in Table 4.

Table 4: Normalized Values

Exp.no Kerf Width Metal

removal rate

Surface

roughness

Tool Wear

Ratio

1 0.154079 0.125512 0.109409 0.059173

2 0.177484 0.145655 0.145879 0.082842

3 0.203489 0.168615 0.118212 0.106511

4 0.206089 0.157074 0.21253 0.059173

5 0.221042 0.196032 0.169773 0.059173

6 0.227543 0.21598 0.18675 0.059173

7 0.224293 0.114495 0.109409 0.035504

8 0.232744 0.116948 0.106894 0.047338

9 0.195687 0.098973 0.110038 0.023669

10 0.160581 0.175338 0.187379 0.059173

11 0.15473 0.171133 0.190523 0.059173

12 0.15733 0.170099 0.176689 0.094676

13 0.15928 0.152883 0.252144 0.059173

14 0.152129 0.177974 0.239568 0.473381

15 0.152129 0.186476 0.215674 0.591726

16 0.153429 0.100091 0.174803 0.532554

17 0.162531 0.103145 0.156568 0.023669

18 0.165782 0.100963 0.132674 0.047338

19 0.2607 0.272298 0.259061 0.059173

20 0.243797 0.290513 0.257803 0.047338

21 0.230794 0.277597 0.236424 0.177518

22 0.222342 0.26251 0.222591 0.035504

23 0.204789 0.265394 0.274151 0.047338

24 0.201538 0.278531 0.226364 0.106511

25 0.201538 0.1989 0.211901 0.118345

26 0.201538 0.206306 0.192409 0.118345

27 0.20869 0.209722 0.135189 0.059173

Later, Analytical Hierarchy Process was implemented to find the weights for each criterion. Table 5 shows us the

pair wise comparison matrix. By using equation (2) calculated the weights as:

WMRR = 0.509

WKF= 0.173

WTWR = 0.224

WSR = 0.0912

Table 5: Comparison of Output responses with their importance

Metal Removal

Rate

(MRR)

Kerf Width

(K W)

Tool Wear Ratio

(TWR)

Surface Roughness

(SR)

MRR 1 5 3 3

KW 0.25 1 0.5 4



TWR 0.33 2 1 3

SR 0.33 0.25 0.33 1

λmax = 4.0455, Consistency Ratio = 0.015

Now, take the above weights for each response and calculated the weighted normalized values by using the

equation (3) which are tabulated in below Table 6.

Table 6: Values of Weighted Normalized equation

S.no. Kerf Width Metal

Removal

Rate

Tool Wear

Ratio

Surface

Roughness

1 0.0266557 0.0638854 0.0132547 0.0099781

2 0.0307047 0.0741386 0.0185565 0.0133041

3 0.0352036 0.0858249 0.0238584 0.0107809

4 0.0356535 0.0799508 0.0132547 0.0193828

5 0.0382403 0.0997801 0.0132547 0.0154833

6 0.039365 0.109934 0.0132547 0.0170316

7 0.0388027 0.0582777 0.0079528 0.0099781

8 0.0402648 0.0595263 0.0106037 0.0097487

9 0.0338539 0.0503775 0.0053019 0.0100355

10 0.0277804 0.0892468 0.0132547 0.0170889

11 0.0267682 0.0871069 0.0132547 0.0173757

12 0.0272181 0.0865805 0.0212075 0.0161141

13 0.0275555 0.0778174 0.0132547 0.0229955

14 0.0263183 0.0905888 0.1060374 0.0218486

15 0.0263183 0.0949163 0.1325467 0.0196695

16 0.0265433 0.0509465 0.119292 0.015942

17 0.0281179 0.0525006 0.0053019 0.014279

18 0.0286802 0.0513899 0.0106037 0.0120999

19 0.0451011 0.1385995 0.0132547 0.0236263

20 0.0421768 0.1478712 0.0106037 0.0235116

21 0.0399274 0.1412971 0.039764 0.0215619

22 0.0384652 0.1336175 0.0079528 0.0203003

23 0.0354285 0.1350855 0.0106037 0.0250026

24 0.0348662 0.1417723 0.0238584 0.0206444

25 0.0348662 0.1012399 0.0265093 0.0193254

26 0.0348662 0.1050095 0.0265093 0.0175477

27 0.0361033 0.1067483 0.0132547 0.0123293

After getting the weighted normalized values by using equation (4) and (5) get the PIS and NIS values for

response which is shown in Table 7.

Table 7: Values of Positive and Negative ideal solutions

S.no. Kerf Width Metal Removal

Rate

Tool Wear

Ratio

Surface

Roughness

Positive

ideal

solution 0.0263183 0.1478712 0.0053019 0.0097487

Negative

Ideal 0.0451011 0.0503775 0.1325467 0.0250026




Solution

Later stages find the Si+ and Si

- (separation measures) values of output responses from PIS and NIS which are

tabulated in Table 8.

Table 8: Si+ and Si- values

S.No Si+ value Si- value

1 0.084292042 0.1223709

2 0.075060801 0.1178981

3 0.06531284 0.1156265

4 0.069627849 0.1233863

5 0.050450012 0.1296539

6 0.041491671 0.1337042

7 0.090430472 0.1258807

8 0.089528709 0.1233073

9 0.097715492 0.128588

10 0.05956965 0.1269001

11 0.061693321 0.1263849

12 0.063583462 0.1187665

13 0.071688171 0.1236659

14 0.116484851 0.0518383

15 0.138158586 0.048701

16 0.149712377 0.0245315

17 0.095426695 0.1288124

18 0.096615006 0.1236963

19 0.026369452 0.1484012

20 0.021701807 0.1561873

21 0.039452066 0.1300873

22 0.02164579 0.150083

23 0.022523022 0.1488143

24 0.023956334 0.1424718

25 0.05276081 0.118195

26 0.049151069 0.1199652

27 0.043029477 0.1328613

Finally, we will get Ci+ ( relative closeness) values by using Si

+ and Si- values according to the equation (6) and it

was shown in the below Table 9. From the Ci+ values we will get the optimal parameters as the highest value will gives the

best alternative and it is nearer to the Positive ideal solution.

Table 9: Ci+ & S/N values of Ci+

S.No.

Relative closeness value

Signal to noise ratio of Ci+

1 0.5921279 -4.55169

2 0.6110012 -4.2791594

3 0.6390346 -3.889513

4 0.6392604 -3.8864442

5 0.7198838 -2.854752

6 0.7631698 -2.3475761

7 0.5819426 -4.7023967

8 0.5793535 -4.7411274

9 0.5682104 -4.9098166

10 0.6805398 -3.3429295

11 0.6719806 -3.4528657



12 0.6513108 -3.7242349

13 0.6330346 -3.9714505

14 0.3079687 -10.229868

15 0.2606287 -11.679554

16 0.1407885 -17.028656

17 0.5744422 -4.8150739

18 0.5614615 -5.0136002

19 0.8491197 -1.4206221

20 0.8780037 -1.1300728

21 0.7672986 -2.3007124

22 0.8739536 -1.1702324

23 0.8685458 -1.224146

24 0.856056 -1.3499565

25 0.6913776 -3.2056941

26 0.7093653 -2.9826013

27 0.7553625 -2.4368918

Means of each input variable to the given levels are computed and shown in table 10. Figure 4 indicates the main

effects plot to the mean values. By the response table and the main effects plot we got the optimal combinations of input

variables as TON-time of 120 µs, Servo Voltage of 20 volts, TOFF- time of 30 µs, Input Current of 210 amps, Wire runoff

of 2 m per min and Wire Tension of 10 Kgf respectively.

Later, Analysis of variance was applied to know the most influence factor that effects the responses which is a

statistical tool to detect any differences occurred in the performance of multiple responses. The ANOVA result is shown in

table 11. The result shows that TON is having more influence on output responses.

Table 10: Response Table for Ci+ mean values

Level

Pulse ON

Pulse OFF

SV

IP

Wire Tension

Wire Runoff

1

0.632665 0.704491

0.620106

0.651645

0.596461

0.68803

2

0.498017 0.658056

0.657838

0.68803

0.651645

0.596461

3

0.805454

0.573589

0.646948

0.596461

0.68803

0.651645

Delta

0.307437

0.130902

0.037732

0.091569

0.091569

0.091569

Rank

1

2

6

3

3

3




Figure 4: Main Effects Plots for S/N Ratio

Table 11: ANOVA Results

Parameters DOF SS MS Percentage %

T ON 2 0.427510 0.213755 50.66

T OFF 2 0.079278 0.039639 9.39

SV 2 0.007167 0.003584 0.85

Input current 2 0.038262 0.019131 4.54

Wire Tension 2 0.038262 0.019131 4.54

Wire Runoff 2 0.038262 0.019131 4.54

Error 14 0.215041 0.015360 25.48

Total 26 0.843782 100

Figure 5: Residual Plots for Ci

+

5. CONCLUSIONS

According to the results, the below conclusions were drawn from experimental validation is:



Optimal arrangement of input variables as TON time of 120 µs, Servo Voltage of 20 volts, TOFF- time of 30 µs,

Input Current of 210 amps, Wire runoff of 2 m per min and Wire Tension of 10 Kgf.

By the ANOVA results we can conclude that TON time is the important factor having more influence on response

variables.

And finally we can conclude that TOPSIS is very useful to solve any MCDM problems and the results are very

effective.

REFERENCES

1. Kumar, S.S., Uthayakumar, M., Kumaran, S.T., Parameswaran, P., Mohandas, E., Kempulraj, G., Babu, B.S.R. and Natarajan,

S.A. (2015a), “Parametric optimization of wire electrical discharge machining on aluminium based composites through grey

relational analysis”, Journal of Manufacturing Processes, Vol. 20, No. 1, pp.33–39.

2. Santosh Patro, I. Harish, P Srinivasa Rao "A Fuzzy Modelling for Selection of Machining Parameters in Wire Electrical

Discharge Machining of D2 Steel”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878,

Volume-8, Issue-5, January 2020, pp. 3045-3052.

3. G.Ugrasen*, H.V.Ravindran, "Process optimization and estimation of machining performances using artificial neural network

in wire EDM", 3rd International Conference on Materials Processing and Characterisation (ICMPC 2014).

4. S. Oliver Nesa Raj* and Sethuramalingam Prabhu, "Analysis of multi objective optimization using TOPSIS method in EDM

process with CNT infused copper electrode", Int. J. Machining and Machinability of Materials, Vol. 19, No. 1, 2017.

5. A. Muniappan, C. Thiagarajan, "EFFECT OF WIRE-EDM PROCESS PARAMETERS ON CUTTING SPEED OF AL6061

HYBRID COMPOSITE", Volume 8, Issue 10, October 2017, pp. 185–189, Article ID: IJMET_08_10_023.

6. P. Senthil, S. Vinodh*," Parametric optimisation of EDM on Al-Cu/TiB2 in-situ metal matrix composites using TOPSIS

method", Int. J. Machining and Machinability of Materials, Vol. 16, No. 1, 2014.

7. Luca Watanabe Reolon1 & Carlos Augusto Henning Laurindo1, "WEDM performance and surface integrity of Inconel alloy

IN718 with coated and uncoated wires", The International Journal of Advanced Manufacturing Technology (2019) 100:1981–

1991.

8. Sarat Kumar Sahoo* & Sunita Singh Naik, "Optimisation of WEDM process parameters during machining of HCHCr steel

using TOPSIS method", Int. J. Process Management and Benchmarking, Vol. 9 No. 2, 2019.

9. G.Ugrasen, H.V.Ravindran, "Optimization of process parameters in wire EDM of HCHCr material using Taguchi’s

technique", 4th International Conference on Materials Processing and Characterization.

10. Amitesh Goswami, Jatinder Kumar, “Investigation of surface integrity, material removal rate and wire wear ratio for WEDM

of Nimonic 80A alloy using GRA and Taguchi method”, Engineering Science and Technology, an International Journal,

vol.17, pp.173-184, 2014.

11. Harish, Dr. P. Srinivasa Rao, "Optimization of Wire EDM with Brass wire as electrode on HCHCr steel by using Single

Objective Taguchi Approach", 2nd international conference on Recent innovations in management and engineering.

12. Ibrahem Maher et al. “Improve wire EDM performance at different machining parameters-ANFIS modeling”, Science Direct

IFAC-Papers online 48-1 (2015) 105-110.

13. Hanaa Elgohari, Mohammed Abdulmajeed & Ahmed Elrefaey, “Application Sarima Models on Time Series to Forecast the

Number of death in hospital”, International Journal of Applied Mathematics Statistical Sciences (IJAMSS), Vol. 7, Issue 4, pp.




9-18

14. M. M. A. El-Sheikh, R. Sallam & Nahed A. Mohamady, “New Criteria for Oscillation of Second Order Nonlinear Dynamic

Equations with Damping on Time Scales”, IMPACT: International Journal of Research in Applied, Natural and Social

Sciences (IMPACT: IJRANSS), Vol. 3, Issue 3, pp. 79-86

15. Durgesh Agnihotri & Pallavi Chaturvedi, “A Study on Customer Preference and Attitude Towards Major E-floors with

Special Reference to Kanpur”, BEST: International Journal of Management, Information Technology and Engineering (BEST:

IJMITE), Vol. 3, Issue 12, pp. 21-28

16. Abhinav Jain & Monika Mittal, “haar wavelet based computationally efficient optimization of linear time varying systems”,

international Journal of Electrical and Electronics Engineering (IJEEE), Vol . 3, Issue 3, pp. 11-20

parametric optimization of machining parameters …

Documents