Parameter Optimization of Wire EDM in a Range of Thickness for EN8 Die Steel

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303

Parameter Optimization of Wire EDM in a Range of Thickness for EN8 Die Steel S.Vignesh1

M.Sivakumar2

R.Shanmugaprakash3

Department of Mechanical Engineering, Bannari Amman Institute of Technology, 1 Anna University vignezmech@gmail.com

Department of Mechanical Engineering, Bannari Amman Institute of Technology, 2 Anna University muthusivaa@gmail.com

Department of Mechanical Engineering, Bannari Amman Institute of Technology, 3 Anna University summugaa1691@gmail.com

Abstract— Wire Electrical Discharge Machining (WEDM) is also known as an electro thermal production process in then, nor electricity) allows the wire to cut through the metal by the use of heat from electrical sparks. The present work process approach to parameter optimization in wire electric discharge machining of EN-8 die steel (of thickness 50 and 75mm) using Taguchi method which optimizes four process parameters (factors) such as pulse ON time, pulse OFF time, current, voltage and two important response such as material removal rate (MRR) and in surface roughness (Ra –average roughness) of machined workpiece is considered in this study. Taguchi is an efficient search technique, is used to obtain the optimal setting of the desired responses and to avoid the conflicting nature of response. Taguchi designs focus on reducing variability, as well as setting the mean to target. Index Terms— EN8 die steel, Thickness, Material removal rate, Surface roughness, Taguchi technique. ————————————————————

1 INTRODUCTION Wire EDM is a non-traditional thermo-electric process which erodes material from the workpiece by a series of discrete sparks by continuously moving wire electrode. The horizontal movement of the worktable controlled by computer numeric controlsystem, which determines the path of the cut. During the machining operations, dielectric medium is to stabilize the spark erosion path by increasing the conductivity of the medium and to flush-out the eroded metal from the cutting zone. There is virtually no cutting force to the machining parts during the process, because the wire electrode and workpiece never make contact. WEDM is a special form of thermal machining process used for precision machining of conductive materials with varying hardness and complex shapes.

2

LITERATURE REVIEW

Chattopadhyay et al. have used Taguchi’s design of experiment (DOE) approach to conduct experiment on rotary EDM using EN8 steel and copper as work piece-tool combinations and developed empirical relations between performance characteristics (MRR and EWR) and process parameters such as peak current, pulse-on-time and rotational speed of the tool electrode.It is found

that peak current and rotational speed of the tool significantly on both the responses[1].

electrode

influence

Selvakumar et al have used Taguchi experimental design method for the selection of the most optimal machining parameter combinations for wire electrical discharge machining (WEDM) of 5083 aluminium alloy. A series of experiments were performed by considering pulse-on time, pulse-off time, peak current and wire tension as input parameters. The surface roughness and cutting speed

were considered responses. The cutting speed has been found to have an increasing trend with the increase of pulse-on time and peak current. The surface finish decreases, namely Ra increases, with the increase in pulse-on time and peak current [2]. Mevada et al investigated the wire wear rate at different machining parameter Such as peak current, pulse on time, pulse off time, based on a full factorial design of experiment method.Different setting of peak current, pulse on time and pulse off time with three levels. Molybdenum wire of 0.18 mm diameter with and medium strength EN-8 steel material was used as a tool and work piece material.From the experimental work, results and study shows that peak current, pulse on time, pulse off time are the main parameter for the wire wear. With the increase in peak current, wire wear increases, but it is slowly. With the increase in pulse on time, wire wear increases rapidly. Pulse off time is not much effects on the wire wear rate [3]. Singh et al in their paper have discussed about the effect of various process parameters of WEDM like pulse on time (TON), pulse off time (TOFF), gap voltage (SV), peak current (IP), wire feed (WF) and wire tension (WT) to reveal their impact on MRR of hot die steel (H-11). The optimal set of process parameters is identified to maximize the MRR, using one variable at a timeapproach [4]. Kanlayasiri et al have studied the effect of machining parameters on surface roughness of wire EDMed DC53 die steel. The investigated machining parameters were pulse-on time, pulse-off time, pulse-peak current, and wire tension. Results of ANOVA shows that pulse-on time and pulse-peak current are significant variables to surface roughness of wire-EDMed DC53 die steel [5]. From the literature several researchers have been applied Taguchi method to optimize the performance parameters in wire edm process [6]. From the literature survey, the gap identified was different materials were machined using wire electrical discharge machining

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 process. But only limited studies were found in EN-8 die steel material as workpiece. Also the research work carried so far were of 10mm and 20 mm thickness. So machining of EN-8 die steel material of thickness 50mm and 75mm were selected as a workpiece material in this study. In the present work, medium carbon steel EN8 is considered for measuring the output parameters like material removal rate, surface roughness using Taguchi method.

3 EXPERIMENTAL DESIGN The experiments were performed on a WEDM of CNC-E3 (MCJDK7220CH) is as shown in fig1. This machine consists of X-Y coordinate worktable, U-V auxiliary table, wire running system, wire frame, microcomputer based control cabinet and liquid dielectric supply system.

Aluminium

0.021

3.2 Formation of Orthogonal Array The experiments are planned based on the orthogonal array to conduct small, highly fractional factorial experiments up to fullfactorial experiments. The use of orthogonal arrays is just one methodology to design an experiment, but probably the most flexible in accommodating a variety of situations and yet easily for non-statistically oriented people to execute on particular basis. The design of experiments (or) experimental design was based on L18 (33 *12).The control factors considered for the study are pulse-ON time (Ton), pulse-OFF time (Toff), Peak Current (IP) and, voltage(V). The various level of control factors in this level of experimental study are as given in table II. TABLE II LEVELS OF VARIOUS CONTROL FACTORS

Symbol

Machining parameter

Unit

Level 1

Level 2

Level 3

A

Pulse ON Time

µs

15

25

35

B

Pulse OFF Time

µs

4

5

6

C

Peak Current

Amps

2

3

4

D

Voltage

V

75*

100*

-

*Machine input parameters are 0 for 75V and 1 for 100V Fig. 1: Wire EDM Machine

3.1 Material Selection The workpiece material selected in this study is hardened EN-8 die steel of 50mm and 75mm thickness [7]. The chemical composition of EN-8 die steel material is as shown in table I. Demineralized water is chosen as a dielectric fluid in this study [8]. The tool material chosen for cutting the selected material is Molybdenum wire of 0.18mm diameter

The DOE L18 orthogonal array formed is as shown below in table III. This array formed is used for machining both 50mm and 75mm of EN8 material.The experiments are conducted according to L18 orthogonal array (33 *12) design obtained from minitab16 software. The L18 orthogonal array contains 18 experimental runs at various combinations of four input variables. Three factors of three levels and two factors of two levels were chosen for performing the experiments. TABLE III L18 ORTHOGONAL ARRAY

TABLE I CHEMICAL COMPOSITION OF EN8 DIE STEEL ELEMENT Iron Carbon Silicon Manganese Phosphrous Sulphur

COMPOSITION IN % 98.23 0.425 0.258 0.952 0.952 0.016

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S.NO

A Ton(µS)

B Toff(µS)

C IP(Amps)

D* V(Volts)

1

15

4

2

0

2

15

5

3

0

3

15

6

4

0

4

25

4

2

0


INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 5

25

5

3

0

6

25

6

4

0

7

35

4

3

0

8

35

5

4

0

9

35

6

2

0

10

15

4

4

1

11

15

5

2

1

12

15

6

3

1

13

25

4

3

1

14

25

5

4

1

15

25

6

2

1

16

35

4

4

1

17

35

5

2

1

18

35

6

3

1

using surface roughness tester, SURFTEST SJ-210 series. The surface roughness parameter chosen in this experimental study is Roughness Average(Ra ).

4 RESULTS AND DISCUSSION The results obtained are analyzed using signal-to-noise (S/N) Ratios, Response table and Response Graphs with the help of Minitab16 software. Minitab is a computer program designed to perform basic and advanced statistical functions. It is a popular statistical analysis package for scientific applications, in particular for design and analysis of experiments.The S/N ratio measures how the response varies relative to the nominal or target value under different noise conditions. The mathematical model suggested here is the regression equation is an algebraic representation of the regression line and is used to describe the relationship between the responses and predictor variables. The regression equation takes the form of, Response = Constant + Coefficient (predictor) + ... + Coefficient (predictor) (Or) đ?‘Œ = đ?‘?0 + đ?‘?1 đ?‘‹1 + đ?‘?2 đ?‘‹2 +. . . +đ?‘?đ?‘˜ đ?‘‹đ?‘˜

*Machine input parameters are 0 for 75V and 1 for 100V

3.4 Determination of MRR The experiments were performed based on the run order as per mentioned in above OA. The time taken for removing (5*5*50)mm and for (5*5*75)mm square pieces of different parameter combinations as per L18 DOE formed is taken and then material removal rate is calculated as per the following formula.

The following table IV shows the experimental results and then it is analyzed to obtain the optimum process parameters for the material removal rate (MRR) and average roughness(đ?‘…đ?‘Ž ) of EN8 50mm and EN8 75mm thickness workpiece. The signal-to-noise (S/N) ratio is calculated for each factor level combination. The formula for the larger-is-better S/N ratio using base 10 log is, đ?‘†/đ?‘ = −10 ∗ log

(1/đ?‘Œ 2 ) /đ?‘›

Where Y = responses for the given factor level combination and n = number of responses in the factor level combinations. The signal-to-noise (S/N) ratio is calculated for each factor level combinations. The formula for the smaller-is-better S/N ratio using base 10 log is:

MRR=Vc*b*h (mm3 /min) Where, Vc =Cutting length/time taken (mm/min) h =Height of the workpiece (mm)

đ?‘†/đ?‘ = −10 ∗ log

(1/đ?‘Œ 2 ) /đ?‘›

b =Breadth of the workpiece (mm) Where Y = responses for the given factor level combinations and n = number of responses in the factor level combinations. The objective of the present study is to maximize the Material Removal Rate (MRR) and minimize surface roughness (đ?‘…đ?‘Ž ) [9][10][11].The combined objective function considered in this study is as given below.

b =2Wg+d (mm) Where, Wg = Spark gap (mm), d=Diameter of the wire (mm)

3.5 Determination of Surface Roughness The surface roughness of the machined workpieces is measured

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303

TABLE IV RESULTS OF OUTPUT PARAMETERS

Results for EN8 50mm thickness workpiece S.No

Results for EN8 75mm thickness workpiece

Mean MRR

Mean Ra

S/N Ratio of MRR

S/N Ratio of Ra

Value of Z

Mean MRR

Mean Ra

S/N Ratio of MRR

S/N Ratio of Ra

Value of Z

1

5.63236

4.02500

15.01381

-12.09532

0.19460

5.65747

2.87500

15.05244

-9.17276

0.119927

2

6.22700

4.37200

15.88558

-12.81360

0.20865

6.97232

3.22200

16.86755

-10.16251

0.118601

3

7.50085

4.79700

17.50221

-13.61939

0.21222

7.66373

3.64700

17.68880

-11.23872

0.139952

4

6.40839

4.02800

16.13498

-12.10179

0.17545

6.46298

2.87800

16.20866

-9.18182

0.100062

5

9.16667

4.64600

19.24423

-13.34158

0.15800

9.41244

3.49600

19.47404

-10.87143

0.082507

6

8.04976

5.08475

18.11566

-14.12539

0.22248

8.91892

3.93475

19.00624

-11.89834

0.134732

7

10.00000

5.03575

20.00000

-14.04128

0.16964

10.88032

3.88575

20.73283

-11.78950

0.081242

8

10.47619

5.60875

20.40407

-14.97732

0.20549

10.86957

4.45875

20.72424

-12.98426

0.133602

9

5.32301

4.62900

14.52315

-13.30974

0.25267

5.58754

3.47900

14.94441

-10.82909

0.176584

10

17.84266

3.81550

25.02919

-11.63103

-0.12810

17.07191

4.11250

24.64564

-12.28212

-0.05293

11

7.33333

3.21800

17.30603

-10.15172

0.08483

8.03702

3.51500

18.10190

-10.91851

0.118620

12

9.85222

3.62525

19.87068

-11.18676

0.05579

9.98185

3.92225

19.98422

-11.87071

0.107022

13

16.50413

4.12250

24.35185

-12.30321

-0.06906

16.23217

4.41950

24.20753

-12.90746

-0.00403

14

18.24212

4.50850

25.22151

-13.08064

-0.08034

17.07191

4.80550

24.64564

-13.63477

0.010066

15

7.84034

3.71325

17.88670

-11.39508

0.11342

7.98451

4.01025

18.04497

-12.06343

0.164955

16

19.89150

4.71575

25.97335

-13.47102

-0.10430

19.41176

5.01275

25.76130

-14.00152

-0.02959

17

9.85222

3.53925

19.87068

-10.97822

0.04863

9.05350

3.83625

19.13633

-11.67814

0.122413

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 18

14.35095

4.47300

23.13761

-13.01198

0.01397

12.53323

4.77000

21.96126

-13.57037

0.120306

Fig. 4. Response Graphs for Means of Ra

(Minimization) Z= (0.5)*(Ra/Max. Ra value) - (0.5)*(MRR/Max. MRR Value) The Fig2 and Fig3 shows the response graph and signal to noise graphs of material removal rate (MRR).It shows that MRR increases with the increase in pulse on time(A), peak current(C) and voltage(D) .Fig 4 and fig5 shows that Ra decreases with the increase in pulse on time(A), pulse off time(B) and peak current(C).

The Fig2, Fig3 Fig4 and, Fig5 shows the response graph for EN8 50mm thickness workpiece. The Fig6 and Fig7 shows the response graph and signal to noise graphs of material removal rate (MRR).

Main Effects Plot for SN ratios Data Means

Main Effects Plot for Means Data Means A

14

B

B

20 Mean of SN ratios

12 10 Mean of Means

A

22

8 15

25

35

4

5

C

14

6

D

18 16 15

25 C

35

2

3

4

22

4

5 D

6

20

12 10

18

8 2

3

4

75

16

100

75

100

Signal-to-noise: Larger is better Fig. 2. Response Graphs for Means of MRR Fig. 3. Response Graphs for S/N ratios of MRR

Main Effects Plot for Means Data Means A

4.8

Main Effects Plot for SN ratios

B

Data Means

4.6

B

-12.0

4.2

-12.5

4.0 15

25

35

4

5

C

4.8

Mean of SN ratios

Mean of Means

A

-11.5

4.4

6

D

4.6 4.4

-13.0 -13.5 15

25

35

4

5

C

-11.5

6

D

-12.0 -12.5

4.2

-13.0

4.0

127 2

3

4

75

100

-13.5 2

3

Signal-to-noise: Smaller is better

4

75

100


INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 dependent variable, using several independent variables [12]. Fig. 5. Response Graphs for S/N ratios of Ra

It shows that MRR increases with the increase in pulse on time(A), peak current(C) and voltage(D) Fig8 and fig9 shows that Ra decreases with the increase in pulse on time(A), pulse off time(B) peak current(C) and voltage(D).

Main Effects Plot for SN ratios Data Means A

22.5

Main Effects Plot for Means

21.0

Data Means B

Mean of SN ratios

A

14 12

Mean of Means

10 8 15

25

35

4

5

C

14

B

6

D

19.5 18.0 15

25

35

4

5

C

22.5

6

D

21.0 19.5 18.0

12 10

2

3

4

75

100

Signal-to-noise: Larger is better

8 2

3

4

75

100

Fig. 7. Response Graphs for S/N ratios of MRR Fig. 6. Response Graphs for Means of MRR

Main Effects Plot for Means

Main Effects Plot for SN ratios

Data Means

Data Means

B

4.2

-10.5

4.0

-11.0

3.8

-11.5 Mean of SN ratios

Mean of Means

A

3.6 3.4 15

25 C

35

4

5 D

6

4.2 4.0

A

B

-12.0 -12.5 15

25

35

4

5

C

-10.5

6

D

-11.0 -11.5

3.8

-12.0

3.6

-12.5

3.4 2

3

4

75

100

2

3

4

75

Signal-to-noise: Smaller is better

Fig. 8. Response Graphs for Means of Ra Fig. 9. Response Graphs for S/N ratios of Ra

The multiple regression analysis is carried out to explain a model that explains as much as much as possible, the variability in a

128

100


INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 The regression equation for EN8 50mm thickness workpiece for, MRR = - 13.4 + 0.129 A - 1.95 B + 3.30 C + 0.235 D

S = 1.28221 R-Sq = 94.3% R-Sq(adj) = 92.5% The regression equation for, Ra = 4.41 + 0.0346 A + 0.0483 B+0.448 C - 0.0289 D

TABLE V COMPARISON OF EXPERIMENTAL & OPTIMIZED RESULTS

S = 0.158807 R-Sq = 95.0% R-Sq(adj) = 93.4% The regression equation for EN8 75mm thickness workpiece for, MRR = - 9.59 + 0.108 A - 1.92 B + 3.19 C + 0.200 D S = 1.02195 R-Sq = 95.6% R-Sq(adj) = 94.2% The regression equation for, Ra = - 1.08 +0.0346 A +0.0483 B +0.448 C +0.0290 D S = 0.158807 R-Sq = 95.0% R-Sq(adj) = 93.5%

5 SELECTION OF OPTIMAL PARAMETER COMBINATION By substituting the mean of MRR and Ra values of EN8 50mm thickness workpiece in the combined minimization objective equation,

En8-50mm

Experimental Results

Taguchi Results

Deviation

MRR

17.84266

17.435

0.40766

Ra

3.81550

4.0242

0.2087

En8-75mm

Experimental Results

Taguchi Results

Deviation

MRR

17.07191

17.11

0.03809

Ra

4.11250

4.3242

0.2117

The results obtained from the experiment and Taguchi technique is depicted in the table V. The above table also illustrates the deviation of experimental and Taguchi results of MRR and Ra of EN8-50mm and EN8-75mm thickness workpiece respectively.

(MINIMIZATON) Z=(0.5)*(Ra/6)-(0.5)*(MRR/ 20)

6 CONCLUSION

The optimal value is obtained and the minimum value of Z=-0.128108, and the corresponding combinations for this minimum value is (15-4-4-1).

From the combined minimization objective equation results it was found that MRR maximization and minimization of surface roughness for both EN8-50mm and EN8-75mm thickness workpiece is obtained in the following parameter combinations (15-4-4-1), that is pulse on time = 15µs, pulse off time = 4µs, current = 4amps, and voltage = 1(100volts).

The corresponding values of MRR and R a for EN8 50mm thickness from the above equation are, 3

MRR=17.435 mm /min

REFERENCES [1]

Chattopadhyay KD, Verma S, Satsangi PS, Sharma PC. ―Development of empirical model for different process parameters during rotary electrical discharge machining of copper – steel (EN-8) system‖, Journal of Materials Processing Technology, Vol. 209, pp. 1454-1465, 2009

[2]

Selvakumar G, Sornalatha G, Sarkar S, and Mitra S, ―Experimental investigation and multi-objective optimization of wire electrical discharge machining ( WEDM ) of 5083 aluminum alloy‖, ELSEVIER, vol. 24, pp. 373–379, 2014.

[3]

Mevada J R, Shah C D, and Khatri B C, ―A Wear Investigation of Repeatedly Used Wire in Wire Cut Electrical Discharge Machine‖, International Journal of Emerging Technology and Advanced Engineering, vol. 2, pp. 1–4, 2012.

[4]

Singh H and Garg R, ―Effects of process parameters on material removal rate in WEDM‖, Journal of Achievements in Materials and Manufacturing Engineering, vol. 32, pp. 70–74, 2009.

Ra =4.0242 µm By substituting the mean of MRR and R a values of EN8 75mm thickness in the combined minimization objective equation, (MINIMIZATON)Z=(0.5)*(Ra/5.5)-(0.5)*(MRR/ 20) The optimal value is obtained and the minimum value of Z=-0.052934, and the corresponding combinations for this minimum value is (15-4-4-1). The corresponding values of MRR and Rafor EN8 75mm thicknessfrom the above equation are, MRR=17.11 mm3 /min Ra =4.3242 µm

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303 Method‖, International Journal of Modern Engineering Research, vol. 3, pp. 2281–2286, 2013. [5]

Kanlayasiri K and Boonmung S, ―An investigation on effects of wire-EDM machining parameters on surface roughness of newly developed DC53 die steel‖, Journal of Materials Processing Technology, vol. 188, pp. 26–29, 2007.

[6]

Mahapatra S S and Amar Patnaik, ―Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method‖, International Journal of Advanced Manufacturing Technology, pp. 911–925, 2007.

[7]

Ashish Bhateja, Aditya Varma, Ashish Kashyap , and Bhupinder Singh, ―Study the Effect on the Hardness of three Sample Grades of Tool Steel i.e. EN-31 , EN-8 , and D3 after Heat Treatment Processes Such As Annealing , Normalizing , and Hardening & Tempering,‖ The International Journal of Engineering And Science, pp. 253–259, 2012.

[8]

SivanagaMalleswararaouthor S and Parameswararao C V S, ―Optimization and Influence of Process Parameters for Machining with WEDM‖, International Journal of Innovative Research in Science, Engineering and Technology, vol. 3, pp. 8667–8672, 2014.

[9]

Bhaskar Reddy C, Diwakar Reddy V, Eswara Reddy C, ―Experimental Investigations on MRR and Surface Roughness of EN 19 & SS 420 steels in WIRE- EDM using Taguchi method‖, International Journal of Engineering Science and Technology , vol. 4, pp. 4603–4614, 2012.

[10] Suresh Kataria, Manish Bajaj, Sunil Kadiyan, and Anand Kumar, ―Experimental Investigation on WEDM Machine [11] Using Taguchi Techniques for Optimization of Process Parameter‖, International Journal of research In Mechanical engineering & technology, vol. 5762, pp. 234–238, 2013 [12] ParameswaraRao C V S and Sarcar M, ―Evaluation of optimal parameters for machining brass with wire cut EDM‖, Journal

Author Profile:  S.Vigneshis currently pursuing master’s degree program in Engineering Design at Bannari Amman Institute of Technology, India. PH-09659124659. E-mail: vignezmech@gmail.com  M.Sivakumar is currently working as associate professor in department of Mechanical Engineering at Bannari Amman Institute of Technology, India E-mail: muthusivaa@gmail.com  R.Shanmugaprakashis currently pursuing master’s degree program in Cad Cam at Bannari Amman Institute of Technology, India. E-mail: sunmugaa1691@gmail.com of scientific and industrial research, vol. 68, pp. 32–35, 2009. [13] Lokeswara Rao T and Selvaraj N, ―Optimization of WEDM Process Parameters on Titanium Alloy Using Taguchi

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