experimental investi gations on wire vibration, spark … · using the artificial neural network...
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International Journal of Mechanical Engineering and Technology (IJMET)Volume 8, Issue 8, August 2017, pp.
Available online at http://www.iaeme.com/IJME
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
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=8
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,
Andhra Pradesh, India.
Dr. K. Venkatarao
rofessor, Dept. of Mechanical Engineering, Vignan’s University, Vadlamudi,Guntur,
Andhra Pradesh, India.
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
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
f 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-HCr) steels. Experiments were conducted on
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
plitude 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 removal rate.
Wire vibration, Kerf size, Low machinability, Wire EDM, Vibration
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
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
f 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
HCr) steels. Experiments were conducted on
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
plitude 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
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
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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
Sivanaga MalleswaraRao Singu, Dr. K. Hemachandra Reddy, Dr. K. Venkatarao and Dr. Ch.V.S.
ParameswaraRao
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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.
Experimental Investigations on Wire Vibration, Spark Gap, Mrr and Surface Roughness in Wedm for
Hc-Hcr Steel
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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
Sivanaga MalleswaraRao Singu, Dr. K. Hemachandra Reddy, Dr. K. Venkatarao and Dr. Ch.V.S.
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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
Experimental Investigations on Wire Vibration, Spark Gap, Mrr and Surface Roughness in Wedm for
Hc-Hcr Steel
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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)
Sivanaga MalleswaraRao Singu, Dr. K. Hemachandra Reddy, Dr. K. Venkatarao and Dr. Ch.V.S.
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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
machining of thick plates, uneven larger sparks were generated due to wire vibration and
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
Artificial Neural network is an efficient technique used to establish a relationship between
formance/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 roughness in EDM of different
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 to predict surface roughness, spark gap, the amplitude
of wire vibration and MRR. The ANN models have been developed using the following
������
� ���. �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
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
rocessing capacity of the network can be improved by adding many
nd Surface Roughness in Wedm for
oughness. When higher currents were used in
machining of thick plates, uneven larger sparks were generated due to wire vibration and
litude, (b) Effect of current and thickness
(d) Effect of current and thickness on
Artificial Neural network is an efficient technique used to establish a relationship between
formance/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 the ANN is a
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
oughness in EDM of different
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].
o predict surface roughness, spark gap, the amplitude
of wire vibration and MRR. The ANN models have been developed using the following
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
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
be improved by adding many
Sivanaga MalleswaraRao Singu, Dr. K. Hemachandra Reddy, Dr. K. Venkatarao and Dr. Ch.V.S.
ParameswaraRao
http://www.iaeme.com/IJMET/index.asp 136 [email protected]
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.
Experimental Investigations on Wire Vibration, Spark Gap, Mrr and Surface Roughness in Wedm for
Hc-Hcr Steel
http://www.iaeme.com/IJMET/index.asp 137 [email protected]
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:
Sivanaga MalleswaraRao Singu, Dr. K. Hemachandra Reddy, Dr. K. Venkatarao and Dr. Ch.V.S.
ParameswaraRao
http://www.iaeme.com/IJMET/index.asp 138 [email protected]
• 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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