ann modelling and optimization of r with corresponding … · wire electrical discharge machining...

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:14 No:03 114

1410501-1403-6767-IJMME-IJENS © June 2014 IJENS I J E N S

ANN Modelling and Optimization of Ra with

corresponding MRR on HSS T42 Steel using

WEDM Process

A. U.K.Vates,

B. N.K. Singh,

C. R.V. Singh

A. Research Scholar Deptt of Mech Engg, ISM Dhanbad India; [email protected]

B. Associate Professor (Workshop)), Deptt of Mech Engg ISM Dhanbad India; [email protected]

C. Professor & Head, Deptt of Mech Engg, MRIU Faridabad India, [email protected]

Abstract-- Present work aims to investigate experimental

process and optimize Ra (surface roughness) of HSS T42 using

Wire Electrical Discharge Machining (WEDM) process.

Fractional factorial design of experiment to conducted

experiments and Tan-sigmoid and pureline transfer functional

based four layered Back Propagation Artificial Neural Network

(BPANN) approach have been applied to develop suitable model

which affect Ra at the optimum MRR (Material Removal Rate)

by WEDM process parameters i.e., gap voltage (Vg), flush rate

(Fr), Pulse on time (Ton), pulse off time (Toff), wire feed (Wf) and

wire tension (Wt). The effect of parameters has been statistically

analyzed by training data of the best model using Analysis of

Variance (ANOVA). The adequacy of the model S1 has been

found satisfactory as correlation coefficient (R2) of the training

data and adjusted R2adj statistic are found to be 0.972 and 0.971

respectively. The optimization of Ra of HSS T42 has also been

done using root mean square error (RMSE) approach.

Index Term-- WEDC, Ra, HSS T42, BPANN, ANOVA, RMSE1.

1. INTRODUCTION

Wire Electrical Discharge Machining is metal removal process

by means of repeated spark created between the wire electrode

and work piece. It is considered as unique adaptation of

conventional EDM which uses an electrode to create sparks

within kerfs. WEDM process utilizes a regular travelling wire

anode made up of very thin copper, tungsten and brass

materials of diameter ranging 0.05- 0.35 which is used to find

very good edge sharpness (Ho K.H et al., 2004). The thermal

erosion mechanism during WEDM, primarily, makes use of

electrical energy and then turns into thermal energy through a

series of discrete electrical discharges occurring between thin

wire electrode and conductive material work piece immersed

in a dielectric medium (Tsai, H.C et al., 2003). The thermal

energy generates a channel of plasma between wire electrode

and conductive and hard work material (Shobert E.I. (1983).

However, conclusion from the literature has been drawn as

very high temperature ranging 8000°C - 12000°C is created

within the kerfs gap during machining so that material

removal may takes place by not only melting but directly

vaporization.(Boothroyd, G. Winston, A.K. 1989). Resistance

and Capacitance (R-C) circuit of WEDM converts electrical

energy to generate the pulsating or intermittent discharge in

the form of sparks with maintaining the desire gap between

the electrodes (Bawa, H.S. (2004). The electrically charged

wire has the provision to perform the movement in X-Y

direction to remove the work piece after each run of

experiment (Qu et al 2002a, b). The concept of WEDM is

illustrated in Fig.1.

Fig. 1. Schematic Diagram- Wire EDM

NOMENCLATURE

WEDM - Wire Electrical Discharge Machine

WEDC- Wire Electrical Discharge Cutting

ANN - Artificial Neural Network

Vg- Gap Voltage

FR - Flushing Rate

TON - Pulse on Time

TOFF - Pulse off Time

WF - Wire Feed Rate

WT – Wire Tension

mailto:[email protected]





ED – Electrode Diameter

SB - Stable

TM – Machining Time

T - Thickness

Ra – Surface Roughness

MRR- Material Removal Rate

MSE - Mean Square Error

RMSE – Root Mean Square Error

A simple thermal based model to increase MRR and Ra on

higher values of Ip, V or Ton (Salonitis et al. 2009). Surface

features using composite material under EDM current, voltage

and Ton reported as important surface roughness influencing

parameters (Gatto A. et al., 1997). WEDM is used for high

precision machining to all types of electrically conductive

metallic alloys, tool & die, graphite and a few ceramic as well

as composite materials of any hardness which cannot be

machined easily by conventional machining methods and Vg,

Ton, and Toff are influencing parameters of surface roughness

and MRR on tool steels (Puertas I. et al,. 2003.). WEDM

machining performance such as Ra, electrode wear rate and

MRR with copper electrode on AISI: H3 tool steel work piece

and the input parameters taken as Ip, Ton, and Toff the

optimum condition for Ra was obtained at low Ip, low Ton, and

high Toff and concluded that the Ip was the major factor

effecting both the responses MRR and Ra (Jaharah et al.,

2008). A lot of modelling techniques like ANN have already

been reported for the prediction surface roughness and MRR

of different conducting work materials under WEDM (Panda

DK et al., 2005). (Pradhan M.K. et al 2010) also worked on

same four parameters voltage, current, Ton and duty cycle for

the prediction of MRR. Hybrid models of ANN and GA have

been developed to predict the surface roughness of tool and

die steel materials where machining time, current and voltage

are inputs (Rao G. Krishna Mohana et al. 2009) RSM and

ANN based prediction models have been developed which are

important in evaluating the productivity and have considerable

influence on the properties of the material such as wear

resistant and fatigue strength (Erzurumlu O. H. et al 2007).

Fractional Factorial Design of Experiment (26-2

) to conduct

minimum Nos. of experiments (D.C. Montgomery et al.,

1991) is adopted in present investigation. 80 runs of

experimental data of five different sets have been conducted

and it is facilitated at three levels. WEDM process has been

selected depending on the HSS T42 material characteristics

and the type of responses (Ra & MRR) required to be

evaluated. Two fold cross over hypothetical technique

(TFCOHT) has been used to generate two distinguish models

„S1‟ and „S2‟ as shown in Fig 5. where 55 runs has been used

for training under the BPANN and rest 25 runs are divided

into 15 and 10 runs randomly, for validation and testing the

network respectively. Four layered BPANN architecture has

been used for modelling where independent process variables

are Vg, Fr Ton, Toff, Wf and Wt. Best model set „S1‟ (training

data) result has been tested using Analysis of Variance

(ANOVA) to determine significant factors and establish the

relation between factors and responses using BPANN.

The optimum process parameters are much essential to

achieve better surface finish with adequate material removal

rate (MRR). A lot of research techniques have been reported

for optimization of response, but present work uses sum of

root mean square error (SRMSE) approach and achieve

improvement approx more than 25% in surface smoothness

under WEDM process.

2. EXPERIMENTAL SETUP Chrome coated cylindrical pure copper wire [Resistivity

ρ = 1.68x10-8

(ohm-m), Electrical Conductivity (σ) = 5.96x107

(ohm-m)-1

, Temperature coefficient (K-1

) = 0.003862]

electrode having 0.25 mm in diameter and high tensile

strength has been selected. Copper wire electrode is suitable

(as far as conductivity is concerned) for performing cutting

operation on 18 mm diameter of HSS T42 steel rod to cut 5

mm thickness of disk using CNC operated Wire Electrical

Discharge Machine, model ELECTRONICA-MAXICUT,

SLNO.-250(F: 09:0002:01) having the facilities to hold the

work piece within the place provided with the help of

conductive fixture so that they can complete the circuit

between electrode and work piece as given in Fig.2. Very hard

and conducting material (HSS T42) has been chosen in this

case for its wide application in tool and dies manufacturing

industries.

Table I

Chemical Composition: HSS T42 grade steel

C W Cr Mo V HRC

Conductiv

ity

1.23

%

8.92

%

3.80

%

3.10

%

2.93

% 66 ± 2

1.6x 10 6

(S /m)

The experiments were run on a CNC operated Wire Electrical

Discharge Machine, model ELECTRONICA-MAXICUT,

SLNO -250, (F:09:0002:01) having the facilities to hold the

work piece within the place provided by the help of conductive

fixture, so that they can complete the circuit between electrode

and work piece. Present experiments were aimed at

considering significant effects of several controllable

independent parameters on surface roughness of HSS T42

during WEDC. The spark is created depending upon gap

voltage applied between the conductive work piece and



electrode. The machining performance is influenced by major

independent process parameters which have been selected for

experiment as characteristics of screening test. Commercials

grade of deionised water [(Density= 832 kg/m3), (Electrical

conductivity= 5.5 x 10-6

S/m)] has been used as dielectric

fluid. 18 mm cylindrical rod of HSS T42 steel has been

used as the work piece with negative polarity and the power

supply has the provision to connect the 0.25 mm chromium

coated pure copper tool electrode with positive polarity so that

the material removal may takes place by influence of heat

generated within kerfs due to applied voltage within it.

Fig. 2. HSS T42 machining using WEDM process

The surface roughness Ra of processed material have been

measured precisely by using Surftest SJ-210 Surface

Roughness Tester having least count of 0.001m for the

travel length of 0.85 mm as given in Fig. 3.

Fig. 3. Surftest SJ-210 (Mitutoyo).

Apart from the controllable independent variable factor as

Table.2, there are lot of constant parameters but they

neither play the important effect of the response nor vary

till the experimentation has been finished. The resolution

of this fractional factorial design deals with all the

significant effecting parameters on surface roughness

using WEDM. Experiments were carried out randomly

using suitable table so that repetitions of the runs were not

done throughout.

Table III

Constant Factors during WEDM

Factors Constant Values (coded)

Jog Feed

2 Low Jog 7

Toff1 7

Sensitivity 7

2.2 Design of Experiment & Objective: Five different sets of

data for Fractional Factorial Design of Experiment (26-2

= 16)

have been selected at two levels so that 80 rows of

experimental data can be observed at three levels of

replication on HSS T42 using WEDM. Screening test on HSS

T42 has been performed by authors using the D.C.

Montgomery 1991approach. Factors/Levels for screening test

are given in Table 2.

2.3 ANN Architecture & Training: Many more studies have

been reported on the development of neural networks based on

different architectures. Basically, one can characterize neural

networks by its important features, such as the architecture,

the learning algorithms and the activation functions. Each

category of the neural networks would have its own input

output characteristics and therefore, it can only be applied for

modelling some specific processes. In this present work, fast

Levenberg- Marquardt algorithm BPANN is employed for

modelling.

In order to improve the generalisation early stop is often

employed. There are two different ways in which this

algorithm can be implemented: incremental mode and batch

mode. In the incremental mode, the gradient is computed and

the weights are updated after each input is applied to the

network. In the batch mode all of the inputs are applied to the

network before the weights are updated. Variations have been

observed in the back propagation algorithm. The simplest

implementation of back propagation learning updates the

network weights and biases in the direction in which the

performance function decreases most rapidly i.e. negative of

the gradient. An iteration of this algorithm can be written as

Where, Xt+1 are a vector of current weights and biases, Xt is

the current gradient, and gt is the learning rate.

The optimal regularization parameter can be determined by

Bayesian techniques (Gencay R. et al., 2001). The hit and trial

Xt+1 = Xt – αt gt



method based on literature have been adopted to find critical

7Nos. and 10 Nos. of neurons in primary and secondary

hidden layers respectively which affects R- square statistic.

For modelling of the best prediction Tan sigmoid activation

(squashing) function is used as the infinite input to finite

output range learning capability by controllable instructed

programme in MATLAB 2010a. Steepest descent method

used for the training algorithm to train multilayer network

where values of gradient are smallest because of small

changes in weights and biases. p1, p2, p3, p4, p5 and p6 are six

input layer neurons and Oi is the single neurons in output

layer, whereas I11-I17 and I21-I30 (7 neurons present in primary

and 10 in secondary hidden layers) are hidden layers (Fig.4).

Fig. 4. Artificial Neural Network Approach

2.4 Two Fold Cross over Techniques: This is the

hypothetical technique which used to split and neutralize 80

runs of experimental data set into two parts „S1‟ and „S2‟

model set. Each model also split out sequentially as serial

order described in Fig.5 (Set A, B) (55 runs of training & rest

25 runs for validation and testing in arbitrarily). However, the

best performing model can be selected.

Fig. 5. Set-A (S1 Model)

Fig. 5. Two Fold Crossover techniques to obtain two distinguish models.

1. DATA COLLECTION AND ANALYSIS:

The models (S1 and S2) have been developed from 80 runs of

experimental data performed on HSS T42 using TFCOT.

Training, validation and testing results of the best performing

model (S1) are presented only to analyse and optimize

influencing process parameters. The correlations statistics of

both the models (S1 and S2) are given in Table.5.

It indicates 55 rows of training dataset out of total 80 rows of

experimental observations on HSS T42 grade steel material

using WEDM, whereas 15 rows and 10 rows of data was

randomly selected for validation and testing purpose during

ANN modeling. Training data is only used here to train ANN

network and optimize the process parameters. It also shows

the best model is „S1‟ compared to the „S2‟ for HSS T42

grade steel.

P1

P2

P3

P4

P5

P6

I17

oi yi

b

I1

1

I 30

I 21



Table IV

Summary of R2 values of Training Validation and Testing data

Materi

al

Model R2

Value

Equation of lines

(Correlation between

Obs. & Pred. Values

of Ra)

Average

Predicted

Ra (m)

Root Mean

Square

Error (m)

Percentage

RMSE (%)

Average

% RMSE

RMSE

Concludi

ng

remarks

HSS

T42

S1, Training 0.971 y = 0.967x + 0.047

1.5965

0.005063

0.3171 0.7353

0.7353

Accepted

(Best

model)

S1,

Validation

0.962 y = 0.926x + 0.111

1.4438 0.011573 0.8015

S1, Testing 0.933 y = 0.969x + 0.033

1.5834 0.01722 1.0875

S2, Training 0.974 y = 1.030x - 0.055

1.5563

0.004989

0.3205

0.7856

Not

Accepted

(more

error)

S2,

Validation

0.951 y = 0.934x + 0.104

1.6086 0.01636 1.0170

S2, Testing 0.945 y = 0.935x + 0.085

1.5334 0.01563 1.0193

Correlation coefficient (R2) of the best performing

model ‘S1’ (training, validation & testing data of HSS

T42 grade steel):

Fig.6, Fig.7 and Fig.8 indicate the relationship between the

observed and predicted correlation coefficients (R2) using 7

neurons and 10 neurons, in primary and secondary (hidden)

layers respectively. Fig.6, indicates 55 rows of training data

with correlation coefficient R2 = 0.991 which gives good

result. The validation data having R2= 0.988 in Fig.7 and

testing data having R2

= 0.979 in Fig.8, are also treated as

good results.

Fig. 6. Predictions against Observations of Ra for Model- S1, HSS T42,

7N (Training dataset)

Fig. 7. Predictions against Observations of Ra for Model- S1, HSS T42,

7N (Validation dataset)

Fig. 8. Predictions against Observations of Ra for ModelS1, HSS T42, 7N

(Testing dataset)

Data for Training, validation and Testing mentioning input parameters and responses are given in Tables V, VI and VII

respectively.



Table V

Training Data-Best Model, S1 (HSS T42)

SN Gap

Voltage

(Vg)

Flush

Rate

(Fr)

Spark

Time

(TON)

Spark

Time

(TOFF)

Wire

Feed

(Wf)

Wire

Tension

(Wt)

Surface

Roughness

(Ra)

(Observed)

Surface

Roughness

(Ra)

(Predicted)

Residuals

Square

Material

Removal

(MRR)

(Observed)

Volt Lit./min S S m/ min N/m

2 m m (m)

2 mg/min

1 30 6 1.05 160 5 300 1.5848 1.5803 2.025E-05 135

2 30 6 1.15 130 2 300 1.8174 1.8077 9.409E-05 122

3 30 6 1.15 160 2 600 2.0164 1.9835 0.0010824 118

4 60 4 1.05 130 5 300 1.5128 1.5267 0.0001932 126

5 60 4 1.05 160 5 600 1.547 1.5707 0.0005617 116

6 60 4 1.15 130 2 600 1.831 1.7461 0.007208 163

7 60 4 1.15 160 2 300 1.6798 1.6655 0.0002045 144

8 60 6 1.05 130 2 600 1.583 1.5913 6.889E-05 138

9 60 6 1.05 160 2 300 1.4224 1.4278 2.916E-05 135

10 60 6 1.15 130 5 300 1.3816 1.3622 0.0003764 128

11 60 6 1.15 160 5 600 1.5796 1.5275 0.0027144 125

12 30 4 1.15 160 5 600 1.8568 1.8489 6.241E-05 208

13 30 4 1.15 190 5 900 1.6838 1.7024 0.000346 200

14 30 4 1.25 160 8 900 1.7488 1.7412 5.776E-05 192

15 30 4 1.25 190 8 600 1.7876 1.7726 0.000225 185

16 90 4 1.15 190 8 900 1.3024 1.3006 3.24E-06 79

17 90 4 1.25 160 5 900 1.7304 1.7273 9.61E-06 127

18 90 4 1.25 190 5 600 1.4378 1.4423 2.025E-05 103

19 90 8 1.15 160 5 900 1.4256 1.4518 0.0006864 110

20 90 8 1.15 190 5 600 1.364 1.3533 0.0001145 78

21 90 8 1.25 160 8 600 1.7712 1.7798 7.396E-05 126

22 90 8 1.25 190 8 900 1.714 1.7105 1.225E-05 122

23 60 6 1.05 130 2 600 1.5433 1.5275 0.0002496 186

24 60 6 1.05 160 2 900 1.3614 1.3271 0.0011765 181

25 60 6 1.25 130 5 900 1.5708 1.5755 2.209E-05 198

26 60 6 1.25 160 5 600 1.5571 1.5495 5.776E-05 201

27 60 8 1.05 130 5 900 1.6146 1.6105 1.681E-05 218

28 60 8 1.05 160 5 600 1.5453 1.5825 0.0013838 165

29 60 8 1.25 130 2 600 1.8162 1.8289 0.0001613 276

30 60 8 1.25 160 2 900 1.6913 1.6528 0.0014823 204

31 60 6 1.25 130 5 900 2.1638 2.0306 0.0177422 170

32 60 6 1.25 160 5 600 2.053 2.1145 0.0037823 146

33 60 8 1.05 130 5 900 1.9320 1.966 0.001156 172

34 60 8 1.05 160 5 600 1.7130 1.6187 0.0088925 147

35 60 8 1.25 130 2 600 1.6813 1.6456 0.0012745 132

36 60 8 1.25 160 2 900 1.6088 1.6011 5.929E-05 128

37 90 6 1.05 130 5 600 1.2334 1.2735 0.001608 106

38 90 6 1.05 160 5 900 1.2158 1.2424 0.0007076 115



39 90 6 1.25 130 2 900 1.3286 1.3062 0.0005018 103

40 90 6 1.25 160 2 600 1.2813 1.3162 0.001218 96

41 30 4 1.15 190 2 900 1.7814 1.8027 0.0004537 126

42 30 4 1.25 160 8 900 1.7750 1.7471 0.0007784 219

43 30 4 1.25 190 8 300 1.7338 1.8332 0.0098804 175

44 30 6 1.15 160 8 900 1.8161 1.7912 0.00062 144

45 30 6 1.15 190 8 300 1.7842 1.7882 0.000016 150

46 30 6 1.25 160 2 300 1.8267 1.8583 0.0009986 126

47 30 6 1.25 190 2 900 1.7563 1.7502 3.721E-05 133

48 60 4 1.15 160 8 300 1.4566 1.4367 0.000396 156

49 60 4 1.15 190 8 900 1.3259 1.3106 0.0002341 154

50 60 4 1.25 160 2 900 1.3185 1.3838 0.0042641 170

51 60 4 1.25 190 2 300 1.2831 1.2226 0.0036603 159

52 60 6 1.15 160 2 900 1.4699 1.4641 3.364E-05 165

53 60 6 1.15 190 2 300 1.3536 1.3329 0.0004285 127

54 60 6 1.25 160 8 300 1.3268 1.3364 9.216E-05 158

55 60 6 1.25 190 8 900 1.3682 1.3643 1.521E-05 163

Average 1.601187 1.596570 0.005063 147

Table VI

Validation Data-Best Model, S1 (HSS T42)

SN Gap

Voltage

(Vg)

Flush

Rate

(Fr)

Spark

Time

(TON)

Spark

Time

(TOFF)

Wire

Feed

(Wf)

Wire

Tension

(Wt)

Surface

Roughness

(Ra)

(Observed)

Surface

Roughness

(Ra)

(Predicted)

Residuals

Square

Material

Removal

(MRR)

(Observed)


2 m m (m)

2 mg/min

1 30 4 1.05 130 2 300 1.852 1.8465 3.025E-05 124

2 30 4 1.05 160 2 600 1.6172 1.6278 0.0001124 116

3 30 4 1.15 130 5 600 1.715 1.638 0.005929 169

4 30 4 1.15 160 5 300 1.6418 1.6745 0.0010693 132

5 90 6 1.25 130 2 900 1.2519 1.2289 0.000529 129

6 90 6 1.25 160 8 600 1.2244 1.2694 0.002025 126

7 90 8 1.05 130 2 900 1.3217 1.3686 0.0021996 114

8 90 8 1.05 160 2 600 1.3198 1.3725 0.0027773 102

9 90 8 1.25 130 5 600 1.2758 1.2835 5.929E-05 106

10 60 6 1.05 160 2 900 1.5502 1.5885 0.0014669 134

11 90 8 1.05 130 2 900 1.1865 1.1888 5.29E-06 121

12 90 8 1.05 190 8 600 1.1768 1.1213 0.0030803 101

13 90 8 1.25 130 5 600 1.3215 1.3678 0.0021437 122

14 90 8 1.25 160 5 900 1.3105 1.3572 0.0021809 117

15 30 4 1.15 190 2 300 1.8053 1.7245 0.0065286 128

Average 1.43802 1.44385 0.002009116 122.73



Table VII

Testing Data-Best Model, S1 (HSS T42)

SN Gap

Voltage

(Vg)

Flush

Rate

(Fr)

Spark

Time

(TON)

Spark

Time

(TOFF)

Wire

Feed

(Wf)

Wire

Tension

(Wt)

Surface

Roughness

(Ra)

(Observed)

Surface

Roughness

(Ra)

(Predicted)

Residuals

Square

Material

Removal

(MRR)

(Observed)


2 m m (m)

2 mg/min

1 30 6 1.05 130 5 600 1.7048 1.6544 0.00254 152

2 30 8 1.15 160 8 900 1.6502 1.7097 0.00354 232

3 30 8 1.15 190 8 600 1.5708 1.5819 0.000123 135

4 30 8 1.25 160 5 600 1.892 1.9068 0.000219 269

5 30 8 1.25 190 5 900 1.8966 1.8536 0.001849 102

6 90 4 1.15 160 8 600 1.3962 1.4381 0.001756 269

7 90 6 1.05 130 5 600 1.5812 1.4955 0.007344 106

8 90 6 1.05 160 5 900 1.4434 1.4715 0.00079 86

9 90 8 1.25 160 5 900 1.2215 1.1844 0.001376 138

10 60 6 1.05 130 2 600 1.6389 1.5382 0.01014 146

Average 1.59956 1.58341 0.029678 163.5

Table V represents the training data of the best depicted model

and it may be used to analyse factors trained using ANOVA.

Fig.9 indicates that gap voltage, flush rate, spark on time, spark

off time, wire feed and wire tension have the most significant

effect on surface roughness of HSS T42 under WEDM.

However, it is also clear that surface roughness is inversely

proportional to gap voltage and spark off time, whereas, it is

directly proportional to spark on time and wire feed. Flush rate

and wire tension still, seem to be imparting effects on Ra, but

both opposite to each others. Only in this case of comparison,

the plot of factors effect is allowed to be used. The interaction

plot of Ra is also indicated in Fig.10. It indicates high flush rate

at moderate wire tension leads to decrease in surface roughness.

ANOVA has been used to test the null hypothesis with regard

to the training data obtained through experimental processes.

906030

1.8

1.7

1.6

1.5

1.4

864 1.251.151.05

190160130

1.8

1.7

1.6

1.5

1.4

852 900600300

Vg

Me

an

Ra

(M

icro

ns)

Fr Ton

Toff Wf Wt

Main Effects Plot for Ra: HSS T42Data Means

Fig. 9. Main Effects Plot for Ra: HSS T42



864 1.251.151.05 190160130 852 900600300

1.8

1.6

1.4

1.8

1.6

1.4

1.8

1.6

1.4

1.8

1.6

1.4

1.8

1.6

1.4

Vg

Fr

Ton

Toff

Wf

Wt

30

60

90

Vg

4

6

8

Fr

1.05

1.15

1.25

Ton

130

160

190

Toff

2

5

8

Wf

Interaction Plot for Ra: HSS T42Data Means

Fig. 10. Main interaction Plot for Ra: HSS T42

It is assumed that there is no difference in treatment

resources. Table.8 indicates the ANOVA for surface

roughness and it indicates that Fr, Ton, Toff, Wf and Wt

As the Nested ANOVA for Ra Table.8, indicates all the factors are

most or moderate significant with Ra of HSS T42 under WEDM. are

the most significant. Figures 11,12 and 13 show the normal

plots of residuals. Normal distribution of error may be recast

broadly by using these methods. In all three cases normal

probability plots from training, validation and testing dataset

P- values obtained are less than 0.05 as indicated in Fig.11,

Fig.12 and Fig.13 respectively. This indicates the model „S1‟

is verified for BPANN using ANOVA.

0.150.100.050.00-0.05-0.10

99

95

90

80

70

60

50

40

30

20

10

5

1

Residuals (Microns) from Training data

Perc

ent

Mean 0.004616

StDev 0.03761

N 55

AD 1.130

P-Value 0.005

Normal Probability Plot of Residuals: HSS T42, Training dataResponse is the Ra (Microns)

Fig. 11. Normal Probability plot of residuals from training Data



Table VIII

Nested ANOVA for Ra versus Vg, Fr, Ton, Toff, Wf, Wt

Source DF SS MS F-value P-value

Vg 2 0.7869 0.3934 9.06451 0.6831058

Fr 5 0.3620 0.0724 1.66820 0.1257571

Ton 11 0.4815 0.0438 1.00921 0.0760545

Toff 21 0.4127 0.0197 0.45391 0.0342069

Wf 5 0.2155 0.0431 0.99308 0.0748390

Wt 2 0.0069 0.0035 0.08064 0.0060771

Error 8 0.3472 0.0434 - -

Total 54 2.6127 - - -

0.100.050.00-0.05-0.10

99

95

90

80

70

60

50

40

30

20

10

5

1

Residuals (Microns)

Pe

rce

nt

Mean -0.005827

StDev 0.04600

N 15

AD 0.797

P-Value 0.030

Normal Probability Plot of the Residuals: Validation data (HSS T42)Response Ra (Microns)

Fig. 12. Normal Probability plot of residuals from validation data

0.150.100.050.00-0.05-0.10

99

95

90

80

70

60

50

40

30

20

10

5

1

Residuals (Microns)

Pe

rce

nt

Mean 0.01615

StDev 0.05484

N 10

AD 0.282

P-Value 0.556

Normal Probability Plot of Residuals: HSS T42 (Testing data)Response is Ra (Microns)

Fig. 13. Normal Probability plot of residuals from Testing Data

Training and verifying the ANN model:

In order to minimize the training difficulty and balance the

effects of surface roughness during training, collected data

were normalized between acceptable tolerances which is

shown in Fig.14.

The ANN achieved a constant training error after 88

iterations of repeated cycles and the mean square error



reaches 1.1E-08 (very close to zero). From Table.7, the

prediction and testing result also analyzed using this error.

Fig. 14. Error Curve in Training

1. OPTIMIZATION OF PROCESS

PARAMETERS

It is evident from Table.5 that six values of each

independent input parameter have been taken on the basis

of their corresponding least possible values of their squares

of residuals. However, two values from each level of input

parameters have been drawn corresponding to the lowest

possible squares of residuals. Each input factor varies at

three levels so (2*3) 6 rows have been selected for each

factor optimization and 3D scatter plot have also been

drawn as Figures 15(A-F). Single point (out of six points)

from each scatter plot has been selected, corresponding to

the minimum possible surface roughness and maximum

possible MRR. Row wise optimized values may also seen

representing in Table.9.

200

1.2 150

1.4

1.6

1.8

40 10060

80

Ra (Microns)

MRR (mg/min)

Gap Voltage Vg (V)

3D Scatterplot of Ra vs MRR vs Gap Voltage: HSS T42 (Fig. A)



2001.4

150

1.6

4

1.8

6 1008

Ra (Microns)

MRR (mg/min)

Dielectric Flush Rate (L/min)

3D Scatterplot of Ra vs MRR vs Flush Rate: HSS T42 (Fig.B)

200

1.2 150

1.4

1.6

1.04

1.8

1.121.20 100

1.28

Ra (Microns)

MRR (mg/min)

Spark on time, Ton (micro seconds)

3D Scatterplot of Ra vs MRR vs Ton: HSS T42 (Fig.C)



160

1.4

120

1.6

1.8

140160 80

180

Ra (Microns)

MRR (mg/min)

Spark off time, Toff: (micro seconds)

3D Scatterplot of Ra vs MRR vs Toff: HSS T42 (Fig.D)

160

1401.25

1.30

1.35

120

1.40

24

6 1008

Ra (Microns)

MRR (mg/min)

Wire feed rate, Wf (m/min)

3D Scatterplot of Ra vs MRR vs Wire feed rate: HSS T42 (Fig.E)

200

150

1.4

1.6

1.8

400 100600

800

Ra (Microns)

MRR (mg/min)

Wire Tension (grams)

3D Scatterplot of Ra vs MRR vs Wire Tension: HSS T42 (Fig.F)

Fig. 15. (a-f): 3D scattered plots between Ra vs. MRR vs. individual independent parameter



2. RESULT

Figures15 (A-F) show the relationship beween individual

influencing parameter (Vg, Fr, Ton, Toff, Wf & Wt) and

optimized response i.e. surface roughness (Ra) with

corresponding values of MRR. Table IX also indicates that

unique value of each influencing parameter (corresponding

to its serial numbers of table V), which gives optimum

responses, as highlighted. Again the experiment has been

conducted on HSS T42 using WEDM by setting the

optimum parametric combinations (Vg, Fr, Ton, Toff, Wf &

Wt) as 90 (Volt), 6 (Lit./min), 1.05 (S), 190 (S), 5

(m/min) and 900 (grams) respectively and the values of

Ra= 1.1462 (m) at MRR=113 (mg/min) have been found.

Table IX

Best parametric combinations and their possible responses

SN Gap

Voltage

(Vg)

Flush

Rate

(Fr)

Spark

Time

(TON)

Spark

Time

(TOFF)

Wire

Feed

(Wf)

Wire

Tension

(Wt)

Surface

Roughness

(Ra)

Obs.

Surface

Roughness

(Ra)

Predicted.

(Residual)2 Material

Removal

Predicted

(MRR)

Volt Lit./min S S m/ min Grams m m (m)

2 mg/min

37 90 6 1.05 130 5 600 1.2334 1.2735 0.001608 111

39 90 6 1.25 130 2 900 1.3286 1.3062 0.000502 105

38 90 6 1.05 160 5 900 1.2158 1.2424 0.000708 109

55 60 6 1.25 190 8 900 1.3682 1.3643 1.52E-05 148

38 90 6 1.05 160 5 900 1.2158 1.2424 0.000708 109

16 90 4 1.15 190 8 900 1.3024 1.3006 3.24E-06 89

3. CONCLUSION

It may be concluded that the model„S1‟ is the best fitted

model for material removal rate and surface roughness of

HSS T42 on the basis of P-values as shown in Fig.11-13.

BPANN modelling technique may be considered as the best

modelling tool of surface roughness of HSS T42 under

WEDM. From the best modelled training data, optimum

parametric combinations i.e. Vg, Fr, Ton, Toff Wf and Wt

observed as 90 Volt, 6 Lit./min, 1.05 S, 190 S, 5 m/min

and 900 grams respectively and the corresponding value

of Ra is 1.1462 m at MRR=113 (mg/min) whereas the

average Ra = 1.5965 (m) at MRR = 147 (mg/min). It has

been seen that this technique is able to successfully minimize

Ra by 28.2% with increase in MRR by 23.12% from its

average value on HSS T42. Such combination may be applied

for industrial application wherever needed.

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[2] Jaharah A., C. Liang, Wahid A., S.Z., Rahman M., and C. Che

Hassan, “Performance of copper electrode in electical discharge

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2008.

[3] Bawa, H.S. (2004): Manufacturing Process-II, The McGraw Hill Companies, pp.186-187.

[4] Boothroyd, G.; Winston, A.K. (1989): Non-conventional machining processes, in Fundamentals of Machining and Machine

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[5] Marcel Dekker, Inc, New York, 491. [6] Montgomery D.C., Design and Analysis of Experiments, Wiley,

Singapore, 1991.

[7] Rao G. Krishna Mohana , G. Rangajanardha , Rao D. [8] Hanumantha , Rao M. Sreenivasa, Development of hybrid model

and optimization of surface roughness in electric discharge

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[9] Gencay Ramjan, Qi Min. Pricing and bedging derivative securities with neural networks : Bayesian regularization, early

stopping and bagging. IEEE Trans Neural Networks 2001;12(4):

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[10] Ho K.H, Newman S.T., Rahimifard, S., Allen, R.D., (2004). State

of the art wire electrical discharge machining (EDM) Int J

Mach Tools Manuf. 44:1247 – 1259. [11] I. Puertas, C.J., Lues, A study on the machining parameters

optimization of electrical discharge machining, Journal of

Material Processing Technology 143-144 (2003) 521-526. [12] Pradhan Mohan Kumar, Chandan Kumar Biswas et al. Neuro-

fuzzy and neural network-based prediction of various responses in

electrical discharge machining of AISI D2 steel. Int J Adv Manuf Technol (2010) 50:591–610.

[13] Erzurumlu O., H. T., “Comparison of response surface model

with neural network in determining the surface quality of moulded parts,” Materials and Design, vol. 28, no. 2, pp. 459–465, 2007.

[14] Panda DK, Bhoi RK (2005) Artificial neural network prediction

of material removal rate in electro-discharge machining. Mater Manuf Process 20:645–672.

[15] Qu,.,Shih, A.J.,Scattergood, R.O., 2002b. Development of the

cylindrical wire electrical discharge machining process, part2: surface integrity and roughness. J. Manuf. Sci. Eng. 124 (3), 708-

714.



[16] Salonitis K, Stournaras A, Stavropoulos P, Chryssolouris G

(2009) Thermal modelling of the material removal rate and surface roughness for die-sinking EDM. Int J Adv Manuf

Technol 40(3–4):316–323.

[17] Scott,D., Boyina, S., Rajurkar, K.P., 1991 “Analysis and optimization of parameter combination in wire electrical

discharge machining.” Int. J. of production research.29 (11) 2189-

2207. [18] Shobert, E.I. (1983): What happens in EDM Electrical Discharge

Machining: Tooling, Methods and Applications, Society of

Manufacturing Engineers, Dearborn, Michigan, pp.3–4. [19] Tsai, H.C.; Yan, B.H.; Huang, F.H (2003): EDM performance of

Cr/Cu based composite electrodes, International Journal of

Machine Tools & Manufacturing, 43, 3, pp.245–252.

Mr. U. K. Vates Research Scholar- ISM Dhanbad, India) Under Supervision of Dr. N .K.

Singh and Dr. R. V. Singh Teaching & Research Experience – 10 years

Research Publications - More than 12

Dr. N .K. Singh Associate Professor (Workshop) ISM Dhanbad Teaching & Research Experience – 25 years Research Publications - More than 28.

Dr. R. V. Singh Professor and Head Deptt of Mech Engg FET, MRIU, Faridabad

Teaching & Research Experience – 18 years Research Publications - More

than 25

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