Experimental Investigation of EN 8 Steel through Taguchi’s ...Dev Bhoomi Group of Institution,...

Experimental Investigation of EN 8 Steel through Taguchi’s Technique to

optimize Cutting Rate

Tirath Singh

Assistant Professor, Department of Mechanical Engineering

Dev Bhoomi Group of Institution, Dehradun

Uttarakhand, India.

Vijay Singh Chauhan



Uttarakhand, India

Yasar Khan Assistant Professor, Department of Mechanical Engineering


Uttarakhand, India

Rahul Chamola



Uttarakhand, India

Abstract Wire Electrical discharge Machining (WEDM) has a large

range of applications that are continuing to grow day by day.

The present applications of WEDM can be seen in the

aerospace, defense, medical, electronic, dental, optical,

jewellery and automotive industries. In this study, optimum

values of cutting process parameters for EN 8 Steel using

WEDM Process are studied. The EN8 steel is extensively

used for making gear, axles, stressed pins, studs, keys,

spindles, very small components, complex or intricate

automotive and general engineering parts. It is also preferable

for heat treatment processes where enormous strength is the

main concern. EN8 steel has been selected for the machining

on WEDM machine (EURO CUT MARK II) of Electronica

Machine Tools Ltd because of its ample number of

applications in manufacturing sectors. A brass metal wire of

0.30 mm diameter is taken as a tool for cutting operation of

the workpiece specimen. The response of cutting rate (CR) for

decided process variables is considered for enhancing the

machining efficiency of the WEDM process. The Experiments

are performed using Taguchi’s L18 orthogonal array (OA)

under umpteen cutting factors of the wire tension (WT), peak

current supply (Ip) and pulse on duration (Ton). The best-

operating conditions of process parameters for performance

parameter are obtained by using the Taguchi Method (TM).

Results show that all parameters are significant and WT has

the highest effect on cutting rate (CR) followed by Ip and Ton.

Keywords: Wire EDM, Pulse on time, cutting rate, Taguchi

method (TM), ANOVA, etc.

Introduction WEDM is a nontraditional manufacturing process in which

workpiece is machined by rapid spark generation between

workpiece and electrode wire (usually brass) which is

continuously supplied throughout the entire process.

Dielectric fluid (basically de-ionized water) is used to provide

a better cutting environment. WEDM process is mainly used

for machining complex and intricate components regardless of

their hardness, chemical composition. Wire-cut EDM can cut

plates as thick as 400 mm and is used to make dies, punches

and tools from hard metals that are difficult to produce by

other machining techniques. When low residual stresses are

our basic need, wire EDM is the best alternative because tool

electrode and workpiece are kept away from each other.

Literature Review

Tosun et al. determined the effect of input characteristics (viz.

wire speed (WS), circuit voltage (SV), pulse duration,

dielectric fluid pressure) on eroded wire electrode craters size

for the wire electrode in WEDM. AISI 4140 steel was used as

workpiece materials and 0.25 mm diameter brass wire as a

tool for the analysis. It was found that increases in circuit

voltage, wire speed and pulse duration increases the size of

the crater, whereas an increase in the pressure of dielectric

flushing reduces the size of the crater. By applying a power

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 9, 2019 (Special Issue) © Research India Publications. http://www.ripublication.com

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function, the variation of wire crater size with machining

parameters was modeled. The effect of the process

characteristics on the wire crater size was established through

analysis of variance (ANOVA) [1]. Kuriakose et al. conducted experiments on titanium alloy (Ti-

6Al-4V) using Taguchi’s L18 orthogonal array (OA). Then by

using non dominated sorting genetic algorithm, the process

parameters are visualized and finally surface roughness (SR)

and cutting velocity (CV) for the titanium alloy (Ti-6Al-4V)

are optimized [2].

Tosun et al. determined optimal cutting conditions for kerf

width and material removal rate (MRR) in Wire EDM process

by using TM. Input parameters which have been chosen are

varying open WS, pulse duration, SV and dielectric fluid

pressure. The response of cutting kerf width and MRR was

determined by ANOVA method under umpteen cutting

conditions of input process parameters and for analyzing the

variation of kerf width and MRR, Regression analysis

technique was used [3].

Miller et al. determined the effect of most influencing range of

Ton and spark cycle (SC) on different materials (titanium, Nd

Fe–B magnetic metal and carbon bipolar thin plate) after

performing the cutting process in WEDM. Electrostatic and

thermal forces are used to find the failure behaviour of thin

section of the materials in WEDM [4].

Ramakrishnan et al. investigated the Wire EDM process for

Inconel 718 workpiece and used the multi-response

optimization technique and artificial neural network (ANN)

model to determine the best cutting condition for WEDM.

Experiments were conducted on Inconel 718 by 0.25 diameter

brass wire as tool electrode. Taguchi’s orthogonal array (OA)

L9 was selected and experiments were conducted under

different settings of Ton, delay time, ignition current and wire

feed speed (WF) [5].

Methods Three process parameters one at two levels (WT) and two at

three levels (Ton and Ip) are used for the present experimental

work. It is essential to use three levels of each input process

parameter for obtaining true behaviour of output characteristic

under most favorable cutting conditions, but here two levels

of wire tension have been used for the convenience of the

process. Process parameters with their most effective levels

are given in Table 1.

Table 1: Effective Levels of Process Parameters

Input Factors Process

Parameters

Effective Levels

L1 L2 L3

A Wire Tension 6 12

B Pulse on

Time

100 110 120

C Current 6 9 12

As per TM, Two levels set of WT have one degree of freedom

(DOF) and three levels set of Ton and Ip have two DOF.

Hence, total 5 DOF for all three factors is available for present

experimental work. Taguchi's L18 Standard Orthogonal Array

is given in Table 2 which is used to find the most favorable

cutting conditions of input process parameters (WT, Ton and

Ip) over the response characteristic (CR).

Table 2: Taguchi's L18 OA used for experiments

S.N. A B C

1 Level Level 1 Level 1

2 Level 1 Level 1 Level 2

















Results The experimental values for CR are given in Table no. 3. 18

experiments is performed using Taguchi experimental design

technique for determining S/N ratio. Minitab 17 software is

used for the analysis, design, and plotting of data through

tables and graphs.

Table 3: Experimental values of CR by using L18 orthogonal

array

S.N. Wire

Tension Ton Current

Cutting

Rate

1 6 100 6 0.39

2 6 100 9 0.40

3 6 100 12 0.42

4 6 110 6 0.41

5 6 110 9 0.43

6 6 110 12 0.45

7 6 120 6 0.42

8 6 120 9 0.44


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9 6 120 12 0.44

10 12 100 6 0.45

11 12 100 9 0.46

12 12 100 12 0.47

13 12 110 6 0.44

14 12 110 9 0.49

15 12 110 12 0.46

16 12 120 6 0.46

17 12 120 9 0.47

18 12 120 12 0.50

Analysis of CR for Process Parameters

For visualizing the effect of various input characteristic

parameters on the CR, experiments were executed by using

L18 OA and the average response values of CR for all Process

Parameters at each value for S/N ratio and raw data are

graphed in Fig. 1 and Fig. 2 respectively. Fig. 1 and Fig. 2

illustrate the values of CR which are increasing continuously

when we are increasing levels of factors (WT, Ton and Ip).

This is happening because discharge energy rises uniformly

with the levels of WT, Ton and Ip. It can also be seen that CR

has the lowest value at first level of WT and has the highest

value at second level of WT. Fig. 3 and Fig. 4 show very less

intersection between the input factors for CR since the

response of input factors at a given level is almost parallel

when compared with other factor’s value at the same level.

There is some interaction between Ton and Ip and Ton at the

second level.

Fig. 1: Response graph of CR for Process Parameters (Raw

Data)

Fig. 2: Response graph of CR for Process Parameters (S/N Data)

Fig. 3: Interaction Graph of CR for Process Parameters

Interactions (Raw Data)

Fig. 4: Interaction graph of CR for Process Parameters

Interactions (S/N Data)

Optimal levels selection Process Parameters

ANOVA was performed to determine the optimal levels of the

process variables for CR. Tables 4 and 5 show values of

ANOVA for the S/N and the raw statistics for CR. It is clear

from the tables that all process characteristics (WT, Ton and

Ip) are significant and all are affecting the CR tremendously.

Tables 6 and 7 are response tables which indicate the

significance of selected input factors for CR. The tables

exhibit ranks and delta statistics values, which show the

comparative importance of each and every process variable.


Page 201 of 203

The difference between the maximum and minimum average

for each factor is called delta value. Ranks are decided on

Minitab software by analyzing delta. Maximum value of delta

is shown as rank 1, second highest as rank 2 and rank 3 to

minimum value of delta. Rank shows the significance of WT,

Ip and Ton for the CR. Delta and rank values predicted that

WT is the most significant parameter for CR and is followed

by Ip and Ton in the same order. As CR is the characteristic of

“higher the better” type, it can be visualized from Fig. 1 that

the value of WT at second level (A2), the value of Ton at third

level (B3) and the value of current at third level (C3) provide

maximum CR. Fig. 2 also shows that the same levels of the input factors (A2, B3, and C3) as the optimal factors level for

maximum CR in WEDM.

Table 4: ANOVA for CR (S/N Ratio)

Source DO

F

Seq of

sum of

squares

Adj of

sum of

squares

Adj of

mean of

squares

F P

WT 1 3.4201 3.4201 3.42011 62.8

0

0.00

0

Ton 2 0.6699 0.6699 0.33494 6.15 0.01

5

I 2 0.9832 0.9832 0.49158 9.03 0.00

4

RE 12 0.6535 0.6535 0.05446

Total 17 5.7267

Table 5: ANOVA for CR (Raw Data)

Source DO

F

Seq of

sum of

squares

Adj of

sum of

squares

Adj of

meanof

squares

F P

WT 1 0.008889 0.0088

89

0.0088

89

61.5

4

0.0

00

Ton 2 0.001678 0.0016

78

0.0008

39

5.81 0.0

17

I 2 0.002544 0.0025

44

0.0012

72

8.81 0.0

04

RE 12 0.001733 0.0017

33

0.0001

44

Total 17 0.014844

Table 6: Response Analysis Table of CR (S/N Ratio )

Level Wire

Tension

Ton I

1 -7.498 -7.319 -7.378

2 -6.626 -7.014 -6.987

3 -6.853 -6.821

Delta 0.872 0.465 0.557

Rank 1 3 2

Table-7: Response Analysis Table of CR (Raw Data) Level Wire

Tension

Ton I

1 0.4222 0.4317 0.4283

2 0.4667 0.4467 0.4483

3 0.4550 0.4567

Delta 0.0444 0.0233 0.0283

Rank 1 3 2

Validation

The best machining condition for CR is obtained at A2 (WT),

B3 (Ton) and C3 (Ip) from Fig. 1 and Table 7 which are

optimal levels of input process parameters. The expected

mean of the CR is calculated as [6].

= + + - (1)

Where,

T = mean of the CR = (R)/18 =0.44444mm/min

R = CR values (taken from third)

= CR at the second level of WT = 0.4667mm/min

= CR at the third level of Ton = 0.4550mm/min

= CR at the third level of Ip = 0.4567mm/min

Putting the values in the above equation

= 0.4667 + 0.4550 + 0.4567 – 2 (0.44444) =

0.48952 mm/min

confirmation experiments (CICE) and population (CIPOP) are

calculated at 95% confidence intervals by using the equations

2 and 3[6], [7].

= (2)

= (3)

Here = F ratio at the confidence level of (1-α) when

DOF 1 and error DOF .

= {18/ (1+5)} =3

N = Number of experiments conducted = 18

R = Performed Sample size for CE which is one

= Error variance = 0.000144 (Table 5)

= error DOF= 12 (Table 5)

(1, 12) = 4.7472 (Tabulated value of F)

= ± 0.03019, and = ± 0.01509

The expected confidence interval for confirmation

experiments is:

Mean - < < Mean +


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0.45933 < < 0.51971

The confidence interval of the population at 95% is:

Mean – < < Mean +

0.47443< <0.50461

The best operating values of input process variables at their

selected levels are listed as follows:

WT value at second level A2 : 12 machine unit

A third level value of Ton (B3) : 120 machine unit

A third level value of Ip (C3): 12 ampere

Confirmation Experiment

For validation of results obtained, CE has been conducted for

the output performance characteristics (CC) at optimal values

of WT, Ton and Ip. CR’s final experimental value is obtained

and analyzed with the forecast value. The final experimental

and predicted results are shown in table 7.

Table 7: Predicted optimal data and Confirmation Experiment

result

Performance

Measures/

Responses

Predicted

Optimal

level

Predicted CI (

95%

Confidence

value)

Calculated

Value (CE)

Cutting Rate 0.48952

mm/min

0.45933 <

< 0.51971

0.47443 <

< 0.50461

0.51 mm/min

The values of CR obtained through CE are within the 95% of

CICE of response characteristic. It is clearly seen that these

optimal values are within the range of process variables.

Conclusion Conclusions which can be derived from the analysis of all the

data are list below.

The value of CR increases with the increasing values

of WT, Ton and current.

For CR, WT is the most significant process

parameter and it is followed by Ip and Ton.

It is clear from the figures the interaction plots are

showing very weak interactions between the WT, Ip

and Ton in affecting the CR.

References [1] Tosun, N. and Cogun, C., An investigation of wire

wear in WEDM. Journal of Materials Processing

Technology 134: 273-278, 2003.

[2] Kuriakose. S, Shunmugam, M.S., Characteristics of

wire-electro discharge machined Ti6Al4V surface.

Materials Letters, 58: 2231– 2237, 2004.

[3] Tosun, N., Cogun, C. and Tosun, G., A study on kerf

and material removal rate in wire electrical discharge

machining based on the Taguchi method. Journal of

Materials Processing Technology, 152: 316-322, 2004.

[4] Miller, S.F., Chen, C.K., Shih, A.J., Qu, J.,

Investigation of wire electrical discharge machining of

thin cross-sections and compliant mechanisms.

International Journal of Machine Tools &

Manufacture, 45: 1717–1725, 2005.

[5] Ramakrishnan R. and Moorthy K. L., Modeling and

multi-response optimization of Inconel 718 on

machining of CNC WEDM process, Journal of

materials processing technology, 207: 343–349, 2008.

[6] Ross, P.J., Taguchi techniques for quality engineering,

McGraw-Hill Book Company, New York,1988.

[7] Roy, R.K., A primer on Taguchi method, Van Nostrand

Reinhold, New York, 1990.


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