Optimization of EN24 Steel on EDM Machine using Taguchi &ANOVA Technique

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ScienceDirect Materials Today: Proceedings 5 (2018) 27974–27981

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ICCMMEMS_2018

Optimization of EN24 Steel on EDM Machine using Taguchi & ANOVA Technique Gurdev Singha, Sandeep Singhb, Dhiraj Parkash Dhimanc, Vikas Gulatid, Tasveer Kaure a Asst. Professor,Dept. of Mechanical Engineering, IET Bhaddal Ropar, Punjab, India. Reserch Scholar, Dept. of Production Engineering, G.N.D.E.C. Ludhiana, Punjab,india. c Asst. Professor,Dept. of Mechanical Engineering S.V.I.E.T Banur, Punjab, India. d Assoc. Professor, School of Mechanical Engineering LPU Phagwara, Punjab,India e Reserch Scholar, Dept. of Mechanical Engineering, S.B.S.S.T.C. Ferozepur, Punjab,india. b

Abstract The accuracy and efficiency of traditional machining process is limited in case of the processed modern materials. So these new developed materials are partial accepted for different application in industry because of its difficulty and costly machining process. Similarly material like EN24 Steels having very good technical properties so it did not economically machine with normally tool by traditional process but such type of constraint do not subject in Electric Discharge Machining. All type of hard material can be machined but the lack of correlation of input parameters for efficient response factors effect on operating cost and quality of products. So to improve accuracy, reduces its operating cost and lower the machining wastage. This paper proposed a research work on EN 24steel with copper electrode. The main focus of this research work to optimize the process parameters like Pulse on time, pulse current, Pulse off time, Duty cycle and Gap voltage for response factors like Tool wear rate (TWR) and Material removal rate (MRR). In the resultant Pulse on time and have large effect in MRR and TWR responses respectively. In this research Taguchi is used to set up the experimental data and ANOVA is used to verify the result for proposed work . © 2018 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of International Conference on Composite Materials: Manufacturing, Experimental Techniques, Modeling and Simulation (ICCMMEMS-2018). Keywords:Electric discharge machining; Parameters; Optimization; MRR; TWR.

1. Introduction Electrical Discharge machining (EDM) with the passage of time is continuously improved and upgraded from mid of 20 century. In 1940 USA first used EDM for the production and manufacturing for defence, development of military aircraft and automobile. In a die sinking machining, a relatively soft electrode (graphite) or metallic electrode can be used to cut hardened steel, or even carbide [1]. *E-mail address [first author a]: [email protected] 2214-7853© 2018 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of International Conference on Composite Materials: Manufacturing, Experimental Techniques, Modeling and Simulation (ICCMMEMS-2018).

Gurdev Singh, Sandeep Singh, Dhiraj Parkash Dhiman,Vikas Gulati,Tasveer Kaur /Materials Today: Proceedings 5 (2018) 27974–27981

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In EDM waste content of the work piece is removed through erosion, evaporation and melting. EDM is a spark erosion machining process [2]. In this machining operation, an electrode held’s at a short distance from work material to generate a high potential difference across them through dielectric [3]. To apply the power supply localized high temperature areas are formed with the high potential difference between work piece and electrode. This potential difference generate spark between electrode and work piece [4-5]. Due to high spark the material start erode from the surface of work piece. Most of the molten debris materials from the space between job and electrode are carried with the help of dielectric fluid during flow. The machining operation controlled by the input variables. From the past few decades, many research works carried out to find the various effects of the parameters on the response variables [6]. Fukuzawa et al.[7] presented experiment on stainless steel (ss 304) with sialon and chromium electrode tool material. They found that wear resistance and corrosion resistance increase by coating the work piece with electrode material. Yan et al.[8] worked on pure titanium metal with adding a urea particles in distilled water as a dielectric medium and studied the nitrogen element distributed on the work piece surface and form a layer resulting in good wear resistance. Zhang et al. [9] experimentally proof by using aluminium oxide based ceramic. They found with increasing current and Pulse on time increases the surface roughness and material removal rate. Pradhan and Biswas [10] used AISI D2 steel as work material for 2 responses variables (material removal rate and Surface roughness) with input variables like duty cycle, voltage, pulse on time and Pulse current. So in this present research paper practical work performed on EN 24 material with 5 input parameters to know about the effects on Material removal rate (MRR) of work piece and Electrode wear rate (TWR). 2. Experimental procedure In this experimentation, copper material used as the electrode and the process parameters like Voltage, Pulse on time (TON), Duty cycle, Pulse off time (TOFF) and Pulse current are used to perform experiment. Furthermore responses are MRR and TWR. Controllable factors values have chosen based upon the literature review. 2.1Assumptions of experimentations During the complete process input power supply, input current supply, Environmental effects and Gap between tool and work piece during all the experiment is constant. Specifications of work material used for experimentation are mentioned below in table 1: Table 1. Work Material Specifications Material EN 24 Length of material

25mm

Material diameter

30 mm

Material for tool

Copper

Vice

70 X 70 X 50

Throat depth

270

Media

Wet

Dielectric

Kerosene oil

2.2Material properties EN24 is widely used in fabricating industry, riveting, bridge, building etc. EN 24 steel comes in the categories of high tensile strength alloys. It is used where high wear resistance and strength is required. In other words it is used, where high stress is applied like air craft landing gear, connecting rod, gears and propeller shaft. The composition of steel element is shown in table 2 below:

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Table 2. EN 24 Steel Element Composition Element

C

Mn

P

S

Cu

Si

Ni

Cr

Percentage

0.39

0.62

0.0102

0.0189

0.110

0.250

1.25

0.945

2.3 Levels of input factors From the previous researches, it has found that very less research work is done on the EN tool steel. In this present paper studied the different parameters and their effects on the responses variables. Table 3 below depicts the input control variables along with the levels used during experimentation: Table 3. Input control variables with levels Levels Variables

Parameters

Responses L1

L2

L3

L4

L5

1

Ton

20

50

100

150

200

2

Pulse Current

6

8

10

12

14

3

Voltage

30

35

40

45

50

4

Pulse off Time

10

12

14

16

18

5

Duty cycle

10

20

30

40

50

Material removal rate, Tool wear rate.

2.4 Results and discussions Analysis of MRR & TWR:- The material removal rate is the difference between the weight of work piece before the machining and after the machining to the density of material and time of operation. MRR= ( − )/( ∗ ) mm3/min Whereas Wi= work piece weight before machining, Wo = work piece weight after machining, q = density and T= operation time. Similarly TWR= ( − )/( ∗ ) mm3/min Whereas Ei=Electrode Weight before machining, Eo= Electrode weight after machining, q= density of electrode and T= time. L25 orthogonal array is used shown in table 4 to find out the influence of parameters on the responses variables. The MRR and TWR values are recorded after the each operation. These values are mentioned in table 4. Table 4. Experimentation of Orthogonal Array Ex. No 1 2 3 4 5 6 7 8 9 10 11 12 13 14

TON 20 20 20 20 20 50 50 50 50 50 100 100 100 100

Current 6 8 10 12 14 6 8 10 12 14 6 8 10 12

Voltage 30 35 40 45 50 35 40 45 50 30 40 45 50 30

TOFF 10 12 14 16 18 14 16 18 10 12 18 10 12 14

Cycle 10 20 30 40 50 40 50 10 20 30 20 30 40 50

MRR 8.99 8.25 8.24 9.20 7.35 10.80 7.65 8.57 9.88 8.57 7.84 8.35 8.25 8.00

S/N MRR 19.075 18.329 18.318 19.275 17.325 20.668 17.673 18.659 19.895 18.659 17.886 18.433 18.329 18.061

TWR 5.00 6.27 5.84 6.34 6.30 7.14 8.79 9.41 7.60 6.51 8.99 7.60 6.84 6.99

S/N TWR -13.979 -15.954 -15.328 -16.041 -15.988 -17.074 -18.879 -19.472 -17.617 -16.272 -19.075 -17.616 -16.701 -16.889

Gurdev Singh, Sandeep Singh, Dhiraj Parkash Dhiman,Vikas Gulati,Tasveer Kaur /Materials Today: Proceedings 5 (2018) 27974–27981

15 16 17 18 19 20 21 22 23 24 25

100 150 150 150 150 150 200 200 200 200 200

14 6 8 10 12 14 6 8 10 12 14

35 45 50 30 35 40 50 30 35 40 45

16 12 14 16 18 10 16 18 10 12 14

10 50 10 20 30 40 30 40 50 10 20

6.04 10.05 6.82 8.82 6.70 6.47 7.65 6.14 6.55 6.35 7.05

15.620 20.043 16.675 18.909 16.521 16.218 17.673 15.763 16.324 16.055 16.963

8.78 6.84 8.99 5.97 6.50 7.37 7.07 6.97 5.61 8.11 7.50

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-18.869 -16.701 -19.075 -15.519 -16.258 -17.349 -16.988 -16.864 -14.979 -18.180 -17.501

Table5. MRR & TWR ANOVA MRR Source

TWR

DF Adjss

Adjms

F values

P values

Adj SS

Adj MS

F values

P values

TON

4

18.489

4.6222

17.61

0.008

19.132

4.7829

15.30

0.011

Current

4

12.89

3.2226

12.28

0.016

4.557

1.1392

3.64

0.119

Voltage

4

6.295

1.5737

6

0.055

11.066

2.7666

8.25

0.029

TOFF

4

3.316

0.829

3.16

0.146

4.581

1.1452

3.66

0.118

Duty cycle

4

3.68

0.9201

3.51

0.126

6.239

1.5597

4.99

0.074

Error

4

1.05

0.26

1.25

0.31

Total

24

45.72

46.824

The effect of the above mentioned process parameters on TWR and MRR has been evaluated by utilizing the data shown in table 5. The analysis of variance (ANOVA) helps to find out the significant and insignificant factors [6,10, 11]. Variance is defined as the sum of squared deviation to the mean or the mean squared deviations about to the mean divided by degree of freedom [12]. In this research the tabulated values are found to be lower than the calculated F- ratios values with 95 % confidence level and hence the models are considered to be adequate. [11-12]. Table 6 shows the delta values of MRR and TWR. The delta values are ranked according the highest values to lower values. Table 6. Response Table for Signal to Noise Ratio for MRR and TWR S/N larger is better for MRR Level Ton

Current

Voltage

Toff

1

18.46

19.07

18.09

17.99

2

19.11

17.38

17.49

3

17.67

18.11

4

17.67

17.96

5

16.56

Delta Rank

S/ N smaller is better for TWR Duty

Ton

Current

Voltage

Toff

Duty cycle

17.22

-15.46

-16.76

-15.90

-16.31

-17.92

18.28

18.4

-17.86

-17.68

-16.63

-16.76

-17.13

17.23

18.14

17.92

-17.83

-16.40

-17.76

-17.17

-16.94

18.68

17.83

18.05

-16.98

-17.00

-17.47

-17.26

-16.81

16.96

17.98

17.23

17.89

-16.90

-17.20

-17.27

-17.53

-16.69

2.56

2.11

1.44

1.05

1.18

2.41

1.28

1.86

1.22

1.42

1

2

3

5

4

1

4

2

5

3

cycle

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The above observations that are shown in table 4, S/N ratio is found out. Graph for AVOVA is shown with the help of software. The Taguchi method is used. The importance of machining parameters is known with AVOVA and on the basis of S/N ratio. Main Effects Plot for SN ratios

Main Effects Plot for Means

Data Means

Ton 19.0

Pulse Current

19.11

Voltage

Data Means

Pulse off Time

Duty Cycle

Ton

19.06

Pulse Current

Voltage

Pulse off Time

Duty Cycle

9.0

18.28

18.39

8.5

M ean o f M ean s

M ea n o f S N ra tio s

18.67 18.5

18.0

17.5

8.0

7.5

17.0 7.0

16.5 20 50 100 150 200 6

Signal-to-noise: Larger is better

8 10 12 14 30 35 40 45 50 10 12 14 16 18 10 20 30 40 50

20 50 100 150 200 6

8

Figure1. S/N Graph for MRR.

Figure 2. Mean Graph for MRR

Area Graph of Ton, Pulse Current, Voltage, Pulse off Time, Duty Cycle

Contour Plot of Ton vs Pulse Current, Voltage 14

12

Variable Ton Pulse Current Voltage Pulse off Time Duty Cycle

300

Ton < 50 50 – 100 100 – 150 150 – 200 > 200

13

250

200

11

D ata

P u lse C u rren t

10 12 14 30 35 40 45 50 10 12 14 16 18 10 20 30 40 50

10 9

150

100

8

50

7 6 30

35

40

45

Voltage

Figure 3.Contour Graph for MRR.

50

0

2

4

6

8

10

12

14

16

18

20

22

24

Index

Figure 4. Area Graph for MRR

From Fig.1 select the large values levels to obtain the optimal machining performance parameters for higher MRR value. Similarly from the tables, the most significant factor is found out. In this TON most effect the MRR by following Pulse current. The respective values are 2.56 and 2.11 of TON and Pulse current. The Fig. 2 shows the S/N graph in MRR case. Above table and Figures for MRR found out the optimum parameters like Gap voltage 30, TON 50, Duty cycle 30, Pulse Current 6 and TOFF 10. It means combination of these parameters will give the large material removal rate. Fig 3 plots the interaction among the Gap voltage, TON and Pulse current for MRR. Analysis of Tool wear rate:The Table 4 shows the Experimental model for TWR. This experiment performed to know about the effect of parameters on Tool wear rate. The Table 5 shows the delta statics on the behalf of rank that compared the relative

Gurdev Singh, Sandeep Singh, Dhiraj Parkash Dhiman,Vikas Gulati,Tasveer Kaur /Materials Today: Proceedings 5 (2018) 27974–27981

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magnitude of factors. The Fig. 5 and Fig. 6 clear the pictures of high impact parameters for the less tool wear rate moreover these figures also shows the other factors that gave the negative effect on tool wear rate. Main Effects Plot for Means

Main Effects Plot for SN ratios

Data Means

Data Means

Ton

Pulse Current

Voltage

Pulse off Time

Duty Cycle

Ton

Voltage

Pulse off Time

Duty Cycle

8.0

-16.0

M ean of M ean s

M e a n o f S N ra tio s

-15.5

Pulse Current

-16.5

-17.0

7.5

7.0

6.5

-17.5

-17.53

-17.67

-17.76

-17.86

-18.0

20 50 100 150 200 6

-17.93

6.0

8 10 12 14 30 35 40 45 50 10 12 14 16 18 10 20 30 40 50

20 50 100 150 200 6

Signal-to-noise: Smaller is better

Figure. 5 S/N ratio for TWR

8 10 12 14 30 35 40 45 50 10 12 14 16 18 10 20 30 40 50

Figure. 6 Mean for TWR

2.5 Regression Equation MRR Equation:-= 11.14 - 0.01044 Pulse on time - 0.1678 Pulse of current + 0.0150 Gap voltage - 0.0939 Pulse off time + 0.0094 Duty Cycle (i) Regression Equation for TWR = 3.77+0.00225 Ton – 0.0024 Pulse current + 0.0564 Gap voltage + 0.1236 Toff – 0.0264 Duty cycle ( ii) 2.6 Confirmation Test The confirmation test is performed to obtain the validation of experimentation data within predicted value. In order to find the best fit combination of the machining process parameters such as TON (Pulse on Time), Gap voltage, Duty Cycle, TOFF (Pulse off Time) and Pulse current for MRR and TWR is analyzed. The average response to three experimental repetitions is 9.8453mm3/min and predicted value 9.4414 mm3/min (from equation) of MRR is obtained and similarly in case of Tool wear rate the average response to three run repetitions is 7.77 mm3/min and predicted value from linear equation is 8.0801 by using respective optimal levels of parameters in with respect to MRR and TWR. The entire populations are lies in the predicted ranges. Thus the validation and confirmation test justify the adequacy of additively. Table 7 below shows the confirmation test results: Table-7: Confirmation Table Experimental value Response

Level

Predicted value

Errors % E1

E2

E3

Avg E

MRR

A2 B1 C4 D2 E2

9.4414

9.940

9.760

9.810

9.8453

4.0117%

TWR

A2 B2 C3 D5 E1

8.0801

7.640

7.920

7.750

7.7733

3.990%

In the present work pulse on time has on larger influence on the both MRR and TWR responses variables. Another researcher performed experiment on AISI 4340 steel and found that material removal rate (MRR) increase with increasing the value of pulse current. However increasing pulse duration result in a reduction material removal rate

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[1]. Another researcher found that different process parameters have different- different effects on machining process. In this work rotating equipment increases the material removal rate. Taguchi method of experiments and response surface methodology is used for analysis and optimization of the design [13]. In next paper studied and evaluate the effects of machining alloy aluminium powder MRR, TWR, by SR using EDM with reverse polarity. The input parameters of the study are the size of grains of concentration and aluminium powder. By using aluminium powder, they found surface roughness and wear rate decreases but increase the concentration of TWR with the increase in MRR [11]. An investigator proposed the experiment on Al2O3 ceramic composite material in this paper discharge current is more effective factor that makes major cause an increase in the discharge energy due to which increase the rate of melting and evaporation due to this melting increase MRR [14]. In another experiment performed with factorial method to set up the experiment model for different parameters. In this multiple linear regression is applied to found out a modelling work for optimum process parameters [15]. An experiment performed on Al/Sic with different percentage of Silicon carbide (Sic) in aluminium matrix, in this MRR increase with increase of Ton, peak voltage and gap voltage moreover TWR also increase with increasing peak current, Ton and decreasing with increment of gap voltage and Sic percentage [12]. Jabbaripour et al.[16] revelled the results with increasing the pulse energy (pulse current and Pulse on Time), increases material removal rate and also increase the micro hardness of recast layer. 3. Conclusions The model developed on the behalf of Taguchi Technique and utility concept a model is developed. With the help of this model, we can get most effective and significant factors [TON (Pulse on time), Gap voltage and Pulse current] for good MRR. But Ton has very large effects on MRR and for TWR. In the case of rest factors Pulse current have little more effect than Gap voltage in MRR case. But in the case of TWR only Gap voltage is dominating factor after Pulse on time. MRR is significantly increases with Ton from starting to second stage. There is a critical value of current above which it has a reverse effect. In case of TWR, it decrease firstly and it is a rising tendency with increase of pulse on time.  

The best parameters of Material removal rate are TON (50μs), Gap Voltage (45V), Pulse Current (6A), Duty cycle (20) and TOFF (12 μs) that results having 9.8453 mm3/min. For the Tool Wear rate the optimum results are TON (50μs), Gap Voltage (40V), Pulse Current (8A), Gap Voltage (40V), TOFF (18 μs) and Duty cycle (10) having TWR 7.7733mm3 / min.

Acknowledgements The authors would like to express their deep gratitude to Mr. Sukhdev Singh Grewal for guidance and financial supports. References [1]. Husain Aizvi, Sanjay Agarwal, An investigation on surface integrity in EDM process with copper tungsten electrode, 18th CIRP Conference on Electro Physical and Chemical Machining (ISEM XVIII). (2016) 612- 617. [2]. H.T Lee, J.P Yur, Characteristics analysis on EDM surfaces using Taguchi method approach, Material and manufacturing processes, 6(2000) 781– 806. [3]. S.H Lee, Li, X.P, Study of the effect of machining parameters on the machining characteristics in electrical discharge machining of tungsten carbide, Journals of Material Processing Technology, 115(2001), 344-358. [4]. Lin J.L, Lin C.L, The use of the orthogonal array with grey relational analysis to Optimize the electrical discharge machining process with multiple performances and Characteristics, International Journal of Machine Tools & Manufacture, 42(2002) 237–244. [5]. D Duowen Keith, S Hargrove, Determining Cutting parameters in EDM Based on Work Piece Surface Temperature Distribution, International Journal of Advance Manufacturing Technology, 154(2006) 24-27. [6]. Sarabjeet Singh Sidhu, Ajay Batish&Andsanjeev Kumar, Study of Surface Properties in Particulate-Reinforced Metal Matrix Composites (MMCs) Using Powder-Mixed Electrical Discharge Machining (EDM), Materials and Manufacturing Processes, 29(2014) 46–52. [7]. Y.Fukuzawa, Y.Kojima,T.Tani,E.Sekiguti,N.Mohri,Fabrication of surface modification layer on stainless steel by electric discharge machining. Materials and Manufacturing Processes, 10(1995)195–203.

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