EXPERIMENTAL INVESTI GATIONS ON WIRE VIBRATION, SPARK … · using the artificial neural network (ANN), support vector machines (SVM) etc. to predict performance characteristics like

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International Journal of Mechanical Engineering and Technology (IJMET)Volume 8, Issue 8, August 2017, pp.

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ISSN Print: 0976-6340 and ISSN Online: 0976

© IAEME Publication

EXPERIMENTAL INVESTI

VIBRATION, SPARK GAP

ROUGHNESS IN WEDM FO

Post-Doctoral Fellow, Dept. of Mechanical

Professor, Dept. of

Professor, Dept. of Mechanical

Professor, Dept. of Mechanical

Dhurjati Nagar, Gudur

ABSTRACT

Performance characteristics like kerf size, metal removal rate, surface roughn

and spark gap are the most important criteria in wire electric discharge machining

(WEDM). Wire electrode which is held between the two wire guides is unstable and

vibrates when the spark is generated. That causes poor surface finish, irregular shape

of the kerf and high spark gap. In the present study, the effect of wire vibration on

spark gap, surface roughness, and metal removal rate were investigated in WEDM of

high carbon high chromium (HC

different thickness of HC-HCr steel plates. An accelerometer was used to measure the

vibration of wire in the direction perpendicular to wire feed. Influence of wire

vibration was found to be significant on spark gap and surface roughness for all plate

thicknesses. The amplitude of wire vibration was found to be less at lower currents for

all thicknesses of the plate. Predictive models were developed using the artificial

neural network to predict surface roughness, spark gap, the amplitude of wire

vibration and metal remov

Keyword: Wire vibration, Kerf size, Low machinability, Wire EDM, Vibration

measurement.

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International Journal of Mechanical Engineering and Technology (IJMET) 2017, pp. 127–139, Article ID: IJMET_08_08_15

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SSN Online: 0976-6359

Scopus Indexed

EXPERIMENTAL INVESTIGATIONS ON WIRE

VIBRATION, SPARK GAP, MRR AND SURFACE

ROUGHNESS IN WEDM FOR HC-HCR

Sivanaga MalleswaraRao Singu

, Dept. of MechanicalEngineering, JNTUA, Ananthapuramu,

Andhra Pradesh, India.

Dr. K. Hemachandra Reddy

rofessor, Dept. of MechanicalEngineering, JNTUA, Ananthapuramu,


Dr. K. Venkatarao

rofessor, Dept. of Mechanical Engineering, Vignan’s University, Vadlamudi,Guntur,


Dr. Ch.V.S. ParameswaraRao

rofessor, Dept. of Mechanical Engineering, Narayana Engineering College,

Dhurjati Nagar, Gudur, Andhra Pradesh, India.

Performance characteristics like kerf size, metal removal rate, surface roughn




f the kerf and high spark gap. In the present study, the effect of wire vibration on


high carbon high chromium (HC-HCr) steels. Experiments were conducted on

HCr steel plates. An accelerometer was used to measure the



plitude of wire vibration was found to be less at lower currents for



vibration and metal removal rate.

Wire vibration, Kerf size, Low machinability, Wire EDM, Vibration

[email protected]

T&VType=8&IType=8

GATIONS ON WIRE

, MRR AND SURFACE

R STEEL

Ananthapuramu,

Ananthapuramu,

dlamudi,Guntur,

, Narayana Engineering College,

Performance characteristics like kerf size, metal removal rate, surface roughness




f the kerf and high spark gap. In the present study, the effect of wire vibration on


HCr) steels. Experiments were conducted on

HCr steel plates. An accelerometer was used to measure the



plitude of wire vibration was found to be less at lower currents for



Wire vibration, Kerf size, Low machinability, Wire EDM, Vibration

Sivanaga MalleswaraRao Singu, Dr. K. Hemachandra Reddy, Dr. K. Venkatarao and Dr. Ch.V.S.

ParameswaraRao

http://www.iaeme.com/IJMET/index.asp 128 [email protected]

Cite this Article: Sivanaga MalleswaraRao Singu, Dr. K. Hemachandra Reddy, Dr.

K. Venkatarao and Dr. Ch.V.S. ParameswaraRao, Experimental Investigations On

Wire Vibration, Spark Gap, Mrr And Surface Roughness In Wedm For Hc-Hcr Steel,

International Journal of Mechanical Engineering and Technology 8(8), 2017,

pp. 127–139.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=8

1. INTRODUCTION

Machining of hard materials like super alloys and tool steels became very difficult and costly

in conventional machining. Recently, the WEDM has become a popular method in the

industry to machine such type of hard metals. The WEDM is capable of making complicated

shapes [1]. High carbon high chromium (HC-HCr) steel is a cold worked alloy tool steel

having a high percentage of carbon and chromium. It is deep hardened D2 type steel having

high wear resistance and used to make dies for forming and shearing. Due to high hardness,

its machinability is very poor and conventionally difficult to machine. Unconventional

machining processes were introduced and developed during the Second World War to

machine such kind of materials. Wire cut electric discharge machining (WEDM) process is

one of the processes used to machine such hard materials. It is a non-contact and violent

thermal process which produces series of electric sparks to remove unwanted material from

the work piece by melting and evaporation. Due to its ability of precision cutting, it is often

used in making of metal moulds, tools, dies etc [2].

Experimental studies have been conducted to evaluate the effect of process parameters

like current, pulse on, pulse off, and servo voltage on the performance of WEDM. The

performance of the WEDM process was evaluated and developed by researchers using

different techniques. Some researchers have introduced different optimization techniques like

Taguchi, analysis of variance (ANOVA), Response surface methodology (RSM), grey

relation analysis (GRA), genetic algorithm (GA), simulated annealing (SA), particle swarm

optimization (PSO) and etc. to study effect process parameters on responses and optimize the

process parameters [3-5]. Some researchers have developed predictive/mathematical models

using the artificial neural network (ANN), support vector machines (SVM) etc. to predict

performance characteristics like tool wear, surface roughness, kerf size, metal removal rate

(MRR) [6].

The geometry of kerf is a critical characteristic that defines the performance of the process

[7]. Studies were carried out to measure the effect of the process parameters on kerf width for

different materials. Gupta et al. studied kerf geometry and effect of peak current, spark

voltage, pulse on time and pulse off time on kerf width in WEDM of a hard alloy like high

strength low alloy steel. A mathematical model was developed for the kerf width using RSM

to correlate the process parameters to kerf width. The four process parameters were found to

be significant on the kerf width [8]. Mehmet et al. have made an attempt to investigate the

effect of heat treatment and process parameters on kerf size in WEDM of Ti6Al4V using GA.

Servo reference voltage, ignition pulse current, the time between 2 pulses, wire speed and

wire tension are considered as process parameters with three levels and experiments were

conducted on six heat treated Ti6Al4V samples. Among the six samples, one sample which

had low conductivity and hardness showed best kerf values [9].

The thickness of the plate is one of the critical parameters that influence the setting of

process parameters to get required MRR and kerf width. Hoang and Yang [10] analyzed kerf

geometry and effect of process parameters on kerf size and MRR in dry micro-WEDM of

titanium alloy. Capacitance, feed rate, air injection pressure and open voltage were considered

as process parameters and experiments were conducted using Taguchi L27 design of

Experimental Investigations on Wire Vibration, Spark Gap, Mrr and Surface Roughness in Wedm for

Hc-Hcr Steel


experiments. Air injection pressure, wire feed rate, and capacitance were found to be

significant parameters on kerf size. The thickness of the plate is also found to be a significant

factor on the kerf size. This is because machining of thick plates needs a high amount of

current that vibrates the wire and therefore kerf size increases. Prasad and Gopalakrishna [11]

developed mathematical models for kerf size and evaluated wire wear ratio in WEDM of

AISI-D3 metal. A global optimization technique is combined with harmony search algorithm

to search optimum process parameters for minimum kerf size as well as wire wear rate.

During the electric discharge between wire and work piece, the wire gets deformed and its

size gets affected and there by the kerf size is also affected. Deformation of wire or change of

wire diameter depends on the process parameters and work piece material. Kerf size at top of

the work piece is always larger than at the bottom because the wire size changes continuously

[12].

Wire displacement or vibration is the most common phenomenon considered in WEDM.

Wire tension is a significant parameter that causes wire vibration during the machining of

hard metals. Unstable wire causes wire displacement, irregular shape of the kerf, surface

roughness, and wires breakage also [13]. Habiba and Okadab studied wire displacement using

a high-speed camera in machining of SKD11 material. They concluded that the amplitude of

work vibration and its frequency are mainly depended on wire tension [13]. Kamei et al. have

also used a high-speed camera to investigate displacement of wire electrode in fine WEDM.

They suggested that the amplitude of wire vibration can be reduced by adjusting the position

of work piece [14]. Nishikawa and Kunieda [15] also mentioned that the kerf size is affected

by the vibration of the wire. The behavior of the wire during machining is complex due to

bubble expansion, electromagnetic and electrostatic forces and it is difficult to measure the

vibration of the wire during machining. They have used an optical sensor for on line

measurement of wire vibration.

Based on the above literature, it is observed that the wire vibration has a significant effect

on the performance characteristics, kerf size, surface roughness, spark gap etc. In the present

study, the effect of the amplitude of wire vibration on the kerf size, surface roughness, and

spark gap is investigated in WEDM of HC-HCr D2 steel. Experiments have been conducted

on different plates of thickness. The vibration of the wire is measured with an accelerometer

in the direction perpendicular to work feed. Prediction models have been developed for the

performance characteristics using the artificial neural network to predict them for given sets

of process parameters.

2. EXPERIMENTAL SETUP

HC-HCrD2 Steel is cold worked high carbon high chromium steel widely used in the making

of blades for metal shearing, punches, rolls for cold rolling, punches and dies for forming etc.

The addition of 0.91% of vanadium to this steel improves wear resistance and toughness. HC-

HCr has low machinability and it is possible to machine only in the annealed condition. That

is why this material has been selected in the present work for studying its machining

characteristics. The HC-HCr D2 Steel has 1.54% of Carbon, 0.32% of Silicon, 0.34%

Manganese, 12.0% of Chromium, 0.76% of Molybdenum, 0.91% Vanadium and remain is

Ferrous. Brass wire is used in this work as an electrode. The Brass wire consisting of 66% of

copper and 34% Zink of is commonly used in WEDM due to its high tensile strength,

reasonable electric conductivity, good flush ability and low cost. Brass wires with a diameter

of 0.1 to 0.3mm are commonly used in WEDM. In the present work, Brass wire having a

diameter of 0.25mm is used. The experimental setup was prepared as shown in Figure 1. An

accelerometer was placed at the bottom of wire feeding unit to measure the vibration of the

wire during the machining process. Nineteen HC-HCr steel specimens were prepared with the


ParameswaraRao


size of 20x40 mm and thicknesses of 5, 7.5, 10, 12.5, 15, 17.5,20,25, 30, 35, 40, 45, 50, 55,

60, 65, 70, 75 and 80mm.

Figure 1 Experimental Setup

The following process parameters are used in the process:

Dielectric Fluid : De-ionized water

Wire Material : 66-34 Brass

Gap Voltage : 90 volts

Wire Velocity : 2.5 m/min

Wire Diameter : 0.25mm

Wire Tension : 16 N

Dielectric Conductivity : 48 S/m

Flushing pressure : 1.5KN/mm2

As shown in Figure 1, experiments were conducted on ELCUT 334 (Electronica Made)

model WEDM machine. A PCB model 356A22 type accelerometer was used to measure the

amplitude of wire vibration. As shown in Figure 1, the accelerometer was fixed to the top

wire guide and it was adjusted to touch the wire. The distance between the wire guides was

taken as 205mm for 5 mm thickness plate. The distance between the wireguides was adjusted

by keeping the length of wire 100mm above and 100mm below the workpiece. On nineteen

plates, cutting was made in the shape of "[" by varying the current according to a thickness of

plates. For each plate, cutting was carried out at five levels current. The current for different

thickness of plates was selected from the range of currently recommended by the machine

manufacturer. During machining, the vibration of wire was measured with an accelerometer

in the form of acoustics emission signals which were converted into the frequency domain

using a Fast Fourier transformer. After each machining, surface roughness on the machined

surface was measured using Talysurf. The kerf width was measured on profile projector and

spark gap was calculated as follows:

2diameter)/ Wire- Width(Kerf = (SG) gapSpark (1)

Experimental results of machining characteristics such as Spark gap (SG), the amplitude

of wire vibration (Vib. Amp.), MRR and surface roughness (Ra) were given in Table 1.


Hc-Hcr Steel


Table 1 Experimental results of machining characteristics.

S.No Thickness

(mm)

Current

(A)

Vib. Amp.

(µm)

S G

(µm)

MRR

(mm3/min)

Ra

(µm)

1 5 2.0 2.8 26 2.87 1.52

2 5 2.15 2.9 27 3.04 1.74

3 5 2.3 2.6 27.5 3.05 2.05

4 5 2.35 2.25 28 3.45 2.24

5 5 2.4 2.2 28 3.36 2.65

6 7.5 2.4 1.8 27 4.10 1.73

7 7.5 2.45 1.9 28 4.36 2.04

8 7.5 2.52 1.94 28 4.47 2.12

9 7.5 2.6 1.9 28 4.36 2.29

10 7.5 2.65 1.85 27 4.22 2.47

11 10 2.65 2.6 27 4.86 1.84

12 10 2.70 2.65 28 5.05 1.99

13 10 2.72 2.71 29 5.28 2.08

14 10 2.74 3.74 30 5.40 2.21

15 10 2.78 3.72 30 5.33 2.35

16 12.5 2.8 3.42 29 5.47 1.74

17 12.5 2.85 4.48 30 5.73 1.95

18 12.5 2.90 4.56 31 6.08 2.17

19 12.5 2.95 4.59 31 6.03 2.36

20 12.5 3.00 4.54 31 6.00 2.51

21 15 3.0 4.35 30 6.27 1.87

22 15 3.10 4.40 31 6.59 2.04

23 15 3.15 4.46 32 6.9 2.35

24 15 3.20 4.45 32 6.82 2.49

25 15 3.22 4.4 32 6.59 2.79

26 17.5 3.25 5.25 32 6.87 1.54

27 17.5 3.30 5.3 33 7.19 1.76

28 17.5 3.36 5.35 34 7.52 2.48

29 17.5 3.40 5.34 34 7.46 2.71

30 17.5 3.43 5.32 34 7.34 3.02

31 20 3.43 5.1 32 6.91 1.64

32 20 3.48 5.2 33 7.58 1.85

33 20 3.52 5.24 34 7.88 2.25

34 20 3.55 7.26 35 8.08 2.52

35 20 3.60 7.24 35 7.93 3.02

36 25 3.60 7.9 34 7.155 1.57

37 25 3.70 7.95 35 7.6 1.98

38 25 3.80 9.0 37 8.1 2.46

39 25 3.92 9.1 38 8.97 2.64

40 25 4.00 9.05 38 8.55 3.08

41 30 4.00 9.86 37 8.36 1.93

42 30 4.10 9.94 39 9.25 2.46

43 30 4.27 10.97 40 9.65 2.74

44 30 4.35 10.98 40 9.70 3.10

45 30 4.40 10.96 40 9.50 3.47

46 35 4.40 10.8 41 9.29 1.78


ParameswaraRao


47 35 4.50 11.82 42 9.58 2.04

48 35 4.55 13.84 43 9.88 2.31

49 35 4.6 14.86 43 10.15 2.75

50 35 4.65 14.84 43 9.88 3.04

51 40 4.70 14.65 44 8.79 1.91

52 40 4.80 14.72 45 9.79 2.42

53 40 4.90 15.76 46 10.41 2.80

54 40 4.95 16.74 46 10.12 3.19

55 40 5.00 16.73 46 9.98 3.71

56 45 5.10 16.63 45 9.64 2.27

57 45 5.15 17.66 47 10.22 2.51

58 45 5.18 18.67 48 10.48 2.95

59 45 5.20 18.67 48 10.43 3.23

60 45 5.25 18.65 47 10.06 3.49

61 50 5.30 18.56 48 9.69 2.48

62 50 5.35 20.57 49 9.92 2.61

63 50 5.40 20.58 50 10.15 2.89

64 50 5.44 20.59 52 10.48 3.14

65 50 5.50 20.59 51 10.38 3.53

66 55 5.55 21.48 52 9.34 2.42

67 55 5.60 21.51 53 9.98 2.63

68 55 5.67 24.52 54 10.25 2.94

69 55 5.70 25.53 55 10.49 3.21

70 55 5.75 25.51 54 10.04 3.72

71 60 5.75 25.44 55 9.50 2.51

72 60 5.80 25.45 56 9.77 2.84

73 60 5.88 25.46 5 10.04 3.39

74 60 5.92 26.46 56 9.99 3.58

75 60 5.95 26.45 56 9.77 4.10

76 65 5.90 27.35 56 8.23 2.42

77 65 5.95 27.37 57 8.75 2.78

78 65 6.0 28.39 59 9.32 3.24

79 65 6.07 30.4 59 9.59 3.51

80 65 6.10 30.39 59 9.32 3.96

81 70 6.10 30.33 60 8.55 3.16

82 70 6.20 30.34 61 8.85 3.44

83 70 6.23 30.35 62 9.17 3.72

84 70 6.25 32.35 62 9.16 4.21

85 70 6.30 32.34 62 8.90 4.75

86 75 6.30 32.27 62 7.57 3.44

87 75 6.35 33.28 63 7.89 3.87

88 75 6.38 34.29 64 8.26 4.04

89 75 6.40 34.29 64 8.22 4.55

90 75 6.43 34.28 64 7.94 4.98

91 80 6.40 35.22 65 6.69 3.42

92 80 6.45 35.23 66 7.02 3.99

93 80 6.50 35.24 67 7.39 4.25

94 80 6.55 35.24 67 7.37 4.78

95 80 7.0 35.23 67 7.06 5.01


Hc-Hcr Steel


RESULTS AND DISCUSSION

In WEDM, wire displacement is one of the important parameters that affect the spark gap,

MRR and surface roughness. In the WEDM process, the wire vibrates in X and Y directions

due to the tension of wire and the current applied. The displacement of wire in the X direction

is a little larger than the displacement of wire in the Y direction. As shown in the Figures 2 (a

& b), the wire vibrates in X as well as Y directions. Habiba and Okadab also found the similar

effect in the WEDM of SKD11 material. The amplitude of wire vibration in the X direction

was found to be more than the amplitude of wire in Y direction [13]. In X direction, there is

no significant effect of wire displacement on the spark gap, surface roughness, and MRR,

because it vibrates in the direction of feed. But, the wire displacement in the Y direction has a

significant effect on the spark gap, surface roughness, and MRR.

Figure 2 (a) Vibration of wire in X direction and (b) Vibration of wire in Y directions

In the present study, an attempt was made to investigate the effect of wire vibration on

machining characteristics. The accelerometer was used to measure the amplitude of wire

vibration in the Y direction in the form of acoustic emission signals. These acoustic optic

emission signals were processed using a Fast Fourier Transformer into the frequency domain

and it is easy to read maximum amplitude of wire vibration. The frequency domain shows the

amplitude of wire vibration at different frequencies. As shown in Figure 3 (a), the maximum

amplitude of wire vibration was found to be 1.1 µm at a frequency of 1690 Hz when the wire

was used without machining. Figure 3 (b) shows that the maximum amplitude of wire

vibration (2.9 µm at a frequency of 1610Hzs) while machining at T=5mm and I=2Amps. The

red color vertical line in the two figures shows the maximum amplitude of wire vibration. As

shown in Table 1, the amplitude of wire displacement increases as the thickness of plate and

current is increased. During WEDM, the wire is vibrated by gap force due to the discharge of

current between wire and work piece. In machining of thick plates, the amplitude of wire

vibration is larger because of increased current [16].

Figure 3 (a) Amplitude of wire vibration when there is no machining (frequency domain)


ParameswaraRao


Figure 3 (b) Amplitude of wire vibration while machining at T=5mm and I=2Amps (frequency

domain)

The effect of the interaction of the current and thickness on the amplitude of wire

vibration during machining process is shown in Figure 4 (a). The displacement of wire while

machining was observed to be increased when the current and thickness were increased. The

amount of current is required to be increased as the thickness of the plate is increased. In

EDM, the current is discharged between work plate and wire in the form of pulses. At high

values of current, the amount of current discharge is also high and it causes vibration of wire

in X and Y directions. As shown in Figure 4 (a), the amplitude of wire vibration increases

with the increase of thickness and current. But the amplitude of wire was greatly affected by

current. The wire vibration was found to be very high (35.23µm) at 7Amps of current for the

80mm thickness of the plate. Machining of thick plates needs a high amount of current, that

vibrates the wire and therefore kerf sizeincreases [10].

In the present study, the spark gap was increased when the current was increased for the

higher thickness of plates, but the current has more effect on the spark gap. High spark gap of

67 microns is found at 7Amps of current for the 80mm thickness of the plate. The effect of

current on spark gap for different thicknesses while machining was shown in Figure 4(b).

Machining of thick plates needs more energy to melt the materials so that current is required

to be increased. As described in the previous section, higher current causes wire vibration due

to which the spark jumps and creates wider cut by melting more material and therefore spark

gap increases.

The effect of current on MRR for different thicknesses was shown in Figure 4(c). The

MRR values were computed for all the experiments. As the thickness of plate was increased,

the current was also increased to give more energy input and therefore more material was

melted and evaporated. The MRR values were also observed to increase with the increase of

current in the plate thickness up to 60mm and then drop. As mentioned earlier, the current

should be increased for thick plates. But, the wire diameter is a parameter that affects the

current carrying capacity. For the plates beyond 60mm thickness, the wire is unable to

discharge high amount of currents that is why the MMR has dropped. During the process, the

intense heat of electric discharge generated between the plate and wire electrode controls the

MRR, surface quality, kerf geometry [17]. The HC-HCr steels were having good thermal

conductivity and therefore heat dissipation from the cutting zone is conducive for

machinability.

Figure 4(d) describes the interaction effect of the work piece thickness and current on the

surface roughness of the machined surfaces. The surface plot represents the quality of surface

roughness with changing work piece thickness and current. As shown in Figure 4 (d), the

surface roughness increases along with the increase of thickness and current. But the current

Experimental Investigations on Wire Vibration, Spark Gap, Mr

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has a greater influence on the surface r

machining of thick plates, uneven larger sparks were generated due to wire vibration and

therefore more roughness is found on the machined surfaces.

Figure 4 (a) Effect of current and thickness on vibration amp

on spark gap, (c) Effect of current and thickness on MRR

Artificial Neural network is an efficient technique used to establish a relationship between

performance/machining characteristics and input process parameter. This technique develops

predictive or mathematical models for the machining characteristics to predict them for given

for different input process parameters. Maity and Mishra [18] stated that

powerful tool to predict responses for a given set of process parameters. They have used the

ANN to predict MRR, over cut effect and recast layer thickness in micro EDM of Inconel

718. Angelos et al. [19] also used the ANN to predict surface r

difficult to machine steels and obtained satisfactory results. This technique is also used in

multi response optimization of process parameters for better performance of the process [20].

In the present study, the ANN is used t

of wire vibration and MRR. The ANN models have been developed using the following

equation [21]:

� � � ��The magnitude of the output is calculated using the above equation, where the x is used to

represent inputs and the w is used to represent weight or synapses efficiencies

effect is inhibitory when the weight between two neurons is negative and the input neuron

effect is excitatory when the weight is positive. A single neuron is considered as a simple

processing unit, the processing capacity of the network can

numbers of neurons.

n Wire Vibration, Spark Gap, Mrr and Surface Roughness

Hc-Hcr Steel

IJMET/index.asp 135 [email protected]

has a greater influence on the surface roughness. When higher currents were used in


therefore more roughness is found on the machined surfaces.

(a) Effect of current and thickness on vibration amplitude, (b) Effect of current and thickness

current and thickness on MRR (d) Effect of current and thickness on

surface roughness


formance/machining characteristics and input process parameter. This technique develops


for different input process parameters. Maity and Mishra [18] stated that



718. Angelos et al. [19] also used the ANN to predict surface roughness in EDM of different



In the present study, the ANN is used to predict surface roughness, spark gap, the amplitude


��

� ��. �2 The magnitude of the output is calculated using the above equation, where the x is used to

represent inputs and the w is used to represent weight or synapses efficiencies



rocessing capacity of the network can be improved by adding many

nd Surface Roughness in Wedm for

[email protected]

oughness. When higher currents were used in


litude, (b) Effect of current and thickness

(d) Effect of current and thickness on


formance/machining characteristics and input process parameter. This technique develops


for different input process parameters. Maity and Mishra [18] stated that the ANN is a



oughness in EDM of different



o predict surface roughness, spark gap, the amplitude


The magnitude of the output is calculated using the above equation, where the x is used to

represent inputs and the w is used to represent weight or synapses efficiencies. Input neuron



be improved by adding many


ParameswaraRao


Figure 5 Neural network architecture (2-8-8-4)

As shown in Figure 5, a neural network (2-8-8-4) was constructed with one input layer,

two hidden layers, and one output layer. Input layer consists of two neurons such as the

thickness of plate and current, the two hidden layers consist of 8 neurons and output layer

consists of the amplitude of wire vibration, spark gap, MRR and surface roughness. The

number of hidden layers and neurons in each hidden layer were selected based on the training

error [22, 23]. As shown in Figure 6, the proposed network found to have an average training

error of 0.00003479 which is less than target error (0.01).

The experimental results were divided into two parts, 78 samples were used to train the

proposed network and 17 samples were selected randomly used to test the network. Among

78 samples, 17 samples were used to validate the training. The network training was carried

out by adopting weights to the connections between neurons in each layer. The proposed

network was trained by feed forward back propagation algorithm using Easy NN plus

software. In training, the target error was set to 0.01 and trained at a learning rate of 0.6 and

momentum of 0.8. The network was trained until the training error got less than target error of

0.01. As shown in Figure 6, the average training error was found to be 0.00003479 after

27000 learning cycles. The maximum training was also found to be less than target error.

During the training, the weight of 16 was taken for the connection between the input layer and

hidden layer 1, the weight of 64 was taken for the connection between hidden layer 1and

hidden layer 2 and connection between hidden layer 2 and output layer has adopted weight of

32. The training was stopped after 27000 cycles and the trained model is used to predict the

amplitude of wire vibration, spark gap, MRR and surface roughness. Both experimental and

predicted values of performance characteristics have been presented in Table 2. The predicted

values were foundto be much closer to the experimental values.


Hc-Hcr Steel


Figure 6 Learning progress graph with maximum, average and minimum training error.

Table 2 Experimental and predicted machining characteristics.

S.

No

Thickn

ess

(mm)

Curre

nt (A)

Vib. Amp. (µm) S G (µm) MRR

(mm3/min)

Ra (µm)

Exp. ANN Exp ANN Exp. ANN Exp. ANN

1 5 2.3 2.2 2.32 27 26 3.05 3.43 2.05 2.14

2 7.5 2.52 1.94 1.87 28 27 4.47 5.21 2.12 2.19

3 10 2.74 3.74 3.86 30 29 5.40 5.91 2.21 2.14

4 12.5 2.95 4.59 4.84 31 30 6.03 6.15 2.36 2.35

5 15 3.15 4.46 4.09 32 32 6.9 7.06 2.35 2.47

6 17.5 3.36 5.35 5.91 34 33 7.52 7.94 2.48 2.52

7 20 3.55 7.26 7.18 35 34 8.08 7.01 2.52 2.58

8 25 3.92 9.10 9.92 38 37 8.97 8.67 2.64 2.61

9 30 4.27 10.97 10.85 40 38 9.65 9.43 2.74 2.90

10 40 4.9 15.76 16.06 46 44 10.41 9.92 2.80 2.63

11 45 5.18 18.67 18.74 48 49 10.48 11.12 2.95 2.91

12 50 5.44 20.59 21.05 52 50 10.48 11.57 3.14 3.46

13 55 5.67 25.53 26.18 55 54 10.49 10.97 3.21 3.53

14 60 5.88 25.46 26.04 56 58 10.04 10.51 3.39 3.42

15 65 6.07 30.4 30.92 59 61 9.59 9.58 3.51 3.58

16 75 6.38 34.29 34.93 64 65 8.26 8.86 4.04 4.19

17 80 6.5 35.24 36.88 67 66 7.39 8.05 4.23 4.20

5. CONCLUSION

The present work investigates the effect of wire vibration on kerf size, surface roughness and

MRR WEDM of HC-HCr D2 steel. An accelerometer is used to measure the amplitude of

wire vibration during machining. The following conclusions can be drawn from the present

investigation:


ParameswaraRao


• During the WEDM, the wire electrode vibrates in two directions such as table feed

direction and direction perpendicular to feed. In table feed direction, there is no

significant effect of wire vibration on the spark gap, surface roughness, and MRR,

because the wire is vibrating in the slot. But the wire displacement in the direction

perpendicular to table feed has a significant effect on the spark gap, surface roughness,

and MRR.

• There is a significant effect of wire vibration on spark gap and surface roughness for

all the plate thicknesses.

• For the plates beyond 60mm thickness, the same diameter of the wire is unable to

discharge high amount of currents due to which the MMR is found to drop.

• Machining of thick plates needs more energy to melt the materials so that current is

required to be increased. Higher current causes wire vibration and so the spark jumps

and creates wider cut by melting more material and therefore spark gap increases.

• When higher currents were used in machining of thick plates, uneven larger sparks

were generated due to wire vibration and therefore more roughness was found on the

machined surfaces.

• Predictive models have been developed for the responses using ANN. The predicted

values are found to be very close to the experimental values. The ANN model can be

used to select proper level of process parameters to reduce the wire displacement and

wire breakage.

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EXPERIMENTAL INVESTI GATIONS ON WIRE VIBRATION, SPARK … · using the artificial neural network (ANN), support vector machines (SVM) etc. to predict performance characteristics like