Materials and Manufacturing Processes
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/lmmp20
Feasibility of Jatropha and Rice bran vegetable oils
as sustainable EDM dielectrics
Tapas Chakraborty, Deepti Ranjan Sahu, Amitava Mandal & Bappa Acherjee
To cite this article: Tapas Chakraborty, Deepti Ranjan Sahu, Amitava Mandal & Bappa Acherjee
(2022): Feasibility of Jatropha and Rice bran vegetable oils as sustainable EDM dielectrics,
Materials and Manufacturing Processes, DOI: 10.1080/10426914.2022.2089891
To link to this article: https://doi.org/10.1080/10426914.2022.2089891
Published online: 21 Jun 2022.
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MATERIALS AND MANUFACTURING PROCESSES
https://doi.org/10.1080/10426914.2022.2089891
Feasibility of Jatropha and Rice bran vegetable oils as sustainable EDM dielectrics
Tapas Chakraborty
a
, Deepti Ranjan Sahu
b
, Amitava Mandal
b
, and Bappa Acherjeec
a
Department of Mechanical Engineering, Saroj Mohan Institute of Technology, Hooghly, India; bDepartment of Mechanical Engineering, Indian
Institute of Technology (ISM), Dhanbad, India; cDepartment of Production and Industrial Engineering, Birla Institute of Technology, Ranchi, India
ABSTRACT
ARTICLE HISTORY
Electrical discharge machining (EDM) is a nontraditional machining process used for machining hard
conductive materials by employing an electrically conductive tool and dielectric. In present days, biodi­
electric fluids are being used as substitutes with some exceptional attributes in EDM. In that context, the
objective of the current work is to study the effectiveness of vegetable oil as dielectric fluid in EDM. In this
article, experiment has been conducted using Jatropha biodiesel (Jatropha BD), Rice bran biodiesel (Rice
bran BD) and EDM oil as dielectric fluid. The experimental results have reported that the material removal
rate (MRR) patterns of Jatropha BD and Rice bran BD oil are almost similar to those of EDM oil, whereas in
most of the cases, Jatropha BD displays better surface quality than the Rice bran BD oil. However, both the
vegetable oils show superior surface quality compared to EDM oil. Evolvements of unhygienic and toxic
gases and generation of nonbiodegradable wastes are few of the critical issues for inferior sustainability
and biodegradability of dielectrics. Based on the test results of dissolved gas analysis, transesterified
Jatropha oil and Rice Bran oil have been introduced as sustainable and biodegradable dielectrics in EDM.
Received 9 February 2022
Accepted 31 May 2022
Introduction
EDM is one of the extensively used nontraditional machining
processes. For the materials that are inconvenient to be
machined by traditional machining processes, electrodischarge
machining can be employed to shape those materials
effectively.[1] In EDM, two electrically conductive electrodes
are used and are joined with positive or negative terminals
according to the type of material selected and the kind of
machining process. The tool–workpiece interface is submerged
into the dielectric fluid. With the application of voltage, the
tool moves toward the workpiece, and the interelectrode gap
gradually reduces. As a consequence, voltage increases and
electrical intensity in the tool–workpiece interface outreaches
the dielectric strength. The dielectric fluid starts to break down
and ionize. The short-duration consecutive electrical sparks
generated in the gap and strikes on the workpiece surface result
in huge heat generation. This causes material removal by
thermal erosion.[2,3] In today’s manufacturing, EDM has
proved its capability due to several reasons. It makes one
capable of machining harder materials and intricately shaping
them precisely. Automation is also another inherent applic­
ability of EDM in the modern manufacturing system.[4–8]
Increasing production along with maintaining quality standard
is the primary objective of an industry. If it is performed by
decreasing the machining time, cost increases. Hence, the
thought for optimization of input parameters came
forward.[9] The proper selection of dielectric in EDM also
plays a significant role in productivity, cost, and quality of the
machined component.[10] To inquire into the most favorable
aspects of suitable dielectric in EDM, various kinds of dielectric
fluids have been employed and their performance has also been
CONTACT Amitava Mandal
© 2022 Taylor & Francis
amitava03@gmail.com
KEYWORDS
Biodegradable; biodielectric;
biodiesel; electrical;
discharge; machining;
transesterified; sustainable
examined by numerous past researchers. Chronological devel­
opment of the dielectric oil has been occurring from the past
kerosene oil to present synthetic and eco-friendly oil consider­
ing the improvement of dielectric properties, its performance,
and level of pollution. Gopalakannan et al.[11] investigated the
EDM performances using kerosene dielectric. Here, it is con­
cluded that the increase of pulse duration and pulse current
deteriorates the surface quality. Also, due to the rise of voltage,
surface roughness tends to deteriorate to a certain value and
then improves. As well, MRR is significantly influenced by
pulse current, pulse duration, and pulse off time. In addition,
EWR grows up with the increase of pulse current and pulse
duration. Conversely, EWR declines when pulse off time
increases. In another experiment,[12] it is also witnessed that
the larger pulse off time declines the electrode wear rate, but
both the pulse duration and pulse current enhance the elec­
trode wear rate, whereas the material erosion rate improves
remarkably with the rise of pulse current. Acceptability of any
fabrication process can be ameliorated by reducing its effect on
the environment, decreasing the cost of production, increasing
the utilization of energy, and also minimizing the health risk of
operating personnel, functional safety, and proper waste
management.[13] Specifically, vegetable oils have large favor­
able attributes compared to other dielectrics. These are free
from toxic halogens, aromatic compounds, and volatile or
semivolatile organics, which may be available in mineral oils
or other dielectrics. Rajurkar et al.[14] nicely instructed the
importance of different properties of dielectric fluids.
Singaravel et al.[15] made a comparison of electrochemical
and electrophysical properties of sunflower oil, canola oil,
and Jatropha oil with conventional kerosene oil. Again, during
Department of Mechanical Engineering, Indian Institute of Technology (ISM), Dhanbad 826004, India
2
T. CHAKRABORTY ET AL.
EDM of titanium alloy, a comparable value of surface rough­
ness was obtained in the same dielectric with reference to
kerosene oil. These biodielectrics were found to be preferable
over conventional dielectrics in concern with operator’s health
and safety. Moreover, it promotes sustainability by reducing
environmental hazards.[16] Besides, the waste vegetable oil
dielectrics also exhibit favorable MRR, Electrode Wear Ratio
(EWR), and TWR values compared to kerosene.[17] Biodiesel
improves MRR and lowers the Electrode Wear Rate in com­
parison with kerosene. Moreover, transformer oil and biodiesel
exhale fewer smoke and smells. Biodiesel is economically fea­
sible and commercially practicable. It enhances
productivity.[18,19] Shah et al.[20] stated that hydrocarbon and
synthetic oils can be replaced by vegetable oil-based esters for
more favorable dielectric properties. Martin et al.[21] employed
transesterified biofluids as dielectrics in power transformers for
their favorable better properties compared to mineral-based
and hydrocarbon oils. Biofluids have superior biodegradability,
higher flash point, larger oxygen value and breakdown voltage,
inferior volatility, and minor poisonous emissions.[18,22] Valaki
et al.[23] conducted a comparative study on the effect of current,
gap voltage, and pulse on time and pulse off time using
Jatropha biodiesel and kerosene as dielectrics separately. The
consequence of the experiment clears out that the Jatropha
biodiesel presents higher MRR and lower surface roughness
and develops surface hardness to the workpiece material with
respect to kerosene. Ng et al.[24] selected Canola and Sunflower
BD as dielectrics in electrodischarge micromachining of bulk
metallic glass and titanium alloy (Ti-6Al-4 V). In this work,
a remarkable improvement of MRR and tool wear ratio was
noticed using Canola and Sunflower BD over the conventional
dielectric. Using the same work piece material and transester­
ified neem dielectric oil, Das et al.[25] were able to enhance the
MRR by 6.2% to 15.6% and lower the SR by 12.25% to 15.45%
with respect to kerosene and justified the sustainability of neem
oil as a dielectric. Sadagopan et al.[19] examined the compara­
tive consequence of peak current, pulse duration, and pulse off
time on the material erosion rate, electrode wear rate, and
surface roughness using transformer oil, kerosene oil, and
palm oil BD. Viscosity of vegetable oil is high due to the
existence of the hydroxyl group in it. The high viscous dielec­
tric enhances the material erosion capability because of high
energy density. Hence, it is an important property for prefer­
ring vegetable oil over mineral oil. Contrarily, the high viscos­
ity also declines flushing capability.[26] Similarly, the clearing
efficiency of the debris from the machining zone improves
surface roughness and MRR of the workpiece material.[17]
Considering the health risk of the EDM operators, improve­
ment of machining efficiency as well as the surface quality,
transesterification of the high viscosity vegetable dielectric oil
is a legitimate step toward improving few vital properties. In
transesterified Jatropha oil and waste vegetable oil, the free
fatty acids and existing impurities are reduced to below 1%.[27]
In earlier days, although few works have been performed on
the performance of vegetable oils and waste vegetable oil by the
researchers, these are limited to Palm oil, Neem oil, Canola oil,
Sunflower oil, Soybean oil, Polanga oil, Jatropha Carcus oil,
and Waste vegetable oil only. However, different dielectric oils
have different properties and hence behave differently during
the EDM process. Further, there is a scope to improve the
properties of vegetable oils by transesterification, and again,
this has not been explored as an EDM dielectric to date. Hence,
considering the forthcoming demand of using biodielectric in
EDM, in this experiment, machining has been conducted with
transesterified Jatropha oil and transesterified Rice bran oil for
comparing their effects on machining performance with
respect to EDM oil and the biodegradability and sustainability
of such biodielectrics have also been evaluated by investigating
their properties.
Materials and methods
In this work, aluminum alloy doped with 1 volume % of B4
C has been used as workpiece material with a size of 30 mm
x 30 mm x 160 mm. The chemical composition of the material
is silicon 0.15%, manganese 0.03%, magnesium 0.44%, iron
0.34%, chromium 0.05%, titanium 0.05%, zinc 0.05%, and
impurity 0.45%, and the remainder is aluminum. Electrical
conductivity of the composite material has been measured as
per the ASTM E-1004-99 standard using the electromagnetic
(eddy current) method. The measured value has been 49.92 s/
m, which is quite above the minimum level of electrical con­
ductivity (1 s/m) for EDM.[28] A solid cylindrical rod of elec­
trolytic copper of 12.7 mm diameter has been chosen as a tool.
The working end of the electrode has been fabricated by wireEDM to obround shape with a thickness of 6.3 mm. Jatropha
oil and Rice Bran oil have been transesterified. Hence, transes­
terified Jatropha oil, transesterified Rice Bran oil, and EDM oil
have been selected as dielectrics for machining. The vegetable­
oils have been taken and heated to 60°C for ten minutes to
drive out moisture from them. Then, two hundred milliliters of
methyl alcohol/l of oil has been taken in a glass bottle by
measuring with a graduated beaker. Next, it is mixed with 5
gm of Potassium Hydroxide where KOH acts as a catalyst. The
mixture has been stirred properly for having a perfect mixture.
Then, the methoxide solution is added to the heated biooil.[24]
Methyl Alcohol vaporizes at 64.7°C. So, the solution has been
heated at 55°C below its evaporation temperature for 30 min.
Then, it is cooled and kept overnight for settling. Glycerin
substance settles at the bottom layer, whereas lighter biodiesel
accumulates as the upper layer. Biodiesel was made free of
catalyst and methyl alcohol by washing repeatedly three
times. Few important properties such as breakdown voltage,
kinematic viscosity, and density are very essential to be
improved by the transesterification process for increasing the
performance of machining. Here, after transesterification, the
breakdown voltage for Jatropha oil is improved from 36 kV to
40.1 kV, and for Rice bran oil, it is enhanced from 34 kV to 38
kV. Similarly, the kinematic viscosity for Jatropha oil is
reduced from 62.537 cst to 4.76 cst, and for Rice bran oil, it is
decreased from 89.818 cst to 8.92 cst. Moreover, the density is
also reduced for Jatropha oil from 0.9133 g/cm3 to 0.8627 g/
cm3, and for Rice bran oil, from 0.9167 g/cm3 to 0.8682 g/cm3.
In this experiment, the Sparkonix ZNC/ENC35 die sinking
EDM machine has been used. A separate stainless steel con­
tainer with a diameter of 350 mm and a height of 150 mm has
been employed for bio-dielectrics. A dielectric pump with
a proper flushing arrangement as well as a filtration system
MATERIALS AND MANUFACTURING PROCESSES
3
Figure 1. Experimental setup.
has been incorporated. Perfect clamping arrangement for hold­
ing the workpiece within the container is included. The com­
plete setup is shown in Fig. 1. Here, after extensive literature
review and conducting pilot runs of the experiment, three
important factors, viz., peak current, pulse on time, and gap
voltage with three levels each, are chosen to look into the effect
of MRR and surface roughness value. Design of experiment
(DOE) is a well-structured traditional methodology under qual­
ity management. Adopting this in a systematic way, enormous
data can be made available with minimum resources. It also
approximates the interactions between variables. It is an effec­
tive tool for controlling input factors with a view to optimize
output responses. Some of the important DOE techniques that
are employed in industry are as follows: One factor at a time
(OFAT), Latin square, Full factorial, Fractional factorial,
Taguchi experimental design, Plackett-Burman design, Halton,
Faure and Sobol sequences, and Response surface method.
OFAT is costly and does not give any idea about interaction
between the factors. Latin square design is used in education,
psychological, medical, agricultural, and industrial sectors. In
full factorial design, the number of experimental runs increases,
which leads to the increase of unnecessary higher order inter­
actions and cost. Moreover, it becomes difficult and laborious
with the increase of input factors. By using fractional factorial
design, the number of runs can be reduced to a fraction of full
factorial for reducing time and cost of production. Using
Taguchi’s orthogonal array approach, larger factor space can
be studied with few numbers of experimental runs in such a way
that the key factors that have a significant effect on the output
responses are detected rapidly.[29] But the results obtained by
the Taguchi technique are relative. It is unable to detect the
factors that have the most significant effects on output perfor­
mance. Moreover, the orthogonal array is not capable of testing
all possible interactions between factors. Due to its offline
nature, it is not perfect for the continuous changing process.
In the case of product development, it can be effectively used in
the designing phase but incapable of correction of inferior
quality.[30] The Adaptive Neuro Fuzzy Inference system
(ANFIS) model is getting paramount importance over robust
and expensive physics-based models. In this context, Pantula
et al. have integrated KERNEL, an intelligent parameter free
ANFIS building algorithm with Sobol-based Hyper cube sample
size resolution algorithm in a polymerization case study. As
a consequence, the surrogate-based optimization speed is inten­
sified 9 times faster than the conventional method along with
online implementation. KERNEL grows the capability of dis­
covering optimal knowledge starting with erroneous
knowledge.[31] Plackett-Burman designs, in which the number
of runs is one less than the number of parameters, act as
a screening procedure to detect the highly significant para­
meters with a fewer numbers of experiments.[32] It is not sui­
table for optimization and not so effective due to partial
interactions with main effects. After detection of critical factors
by Plackett-Burman designs, central composite design (CCD) or
Box Behnken design of response surface method (RSM) may be
applied for optimization. CCD gives important predictions of
linear and quadratic interaction effects between parameters. It
includes axial, central, and cubic points obtained from factorial
design. In Box Behnken design, the number of runs is mini­
mized in the quadratic model and efficiently can be used in
the second-order polynomial model for perfectly exploring lin­
ear interactions and quadratic effects. No extreme values are
taken at the experimental nodes. This design proves to be more
labor-efficient compared to FFD and CCD. In CCD, during
searching of optimum conditions, the predicted data may fall
outside the range of chosen input factors. But for Box Behnken
design, during searching of optimum conditions, the predicted
data always exist within the range of chosen input factors.[33]
4
T. CHAKRABORTY ET AL.
Table 1. Experimental matrix and output responses.
Process variables
Expt.
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Ton
(µs)
20
320
20
320
20
320
20
320
170
170
170
170
170
170
170
Gap
IP Voltage
(A)
(V)
4
50
4
50
12
50
12
50
8
30
8
30
8
70
8
70
4
30
12
30
4
70
12
70
8
50
8
50
8
50
Using
Jatropha BD
MRR
(mg/
min)
25
30
65
275
60
225
85
100
90
255
50
175
155
185
160
Ra
(µm)
3.713
5.786
4.887
8.826
4.670
8.314
4.151
7.935
7.484
9.233
6.166
9.198
8.510
8.834
7.217
Using Rice
Bran BD
MRR
(mg/
min)
40
45
70
245
60
190
90
110
95
235
60
170
140
180
185
Using EDM Oil
MRR
(mg/
min)
40
45
80
225
70
180
90
110
80
235
60
155
150
160
180
Ra
(µm)
4.008
5.769
5.013
9.465
4.902
8.772
4.454
8.019
6.962
9.497
5.850
9.218
8.535
8.845
7.258
Ra
(µm)
4.985
5.935
6.075
13.299
5.698
11.072
6.772
11.208
6.919
11.514
5.925
11.475
10.951
10.409
11.459
generated for the material removal rate and surface roughness
values, which have been obtained from the experiment apply­
ing the response surface method,
MRR ðmg= minÞ for Jatropha BD
¼ 166:8 þ 0:973Ton þ 31:16IP þ 2:43GV
0:002065ðTon Þ2 1:341ðIPÞ2 0:0068ðGV Þ2
þ 0:0854Ton IP 0:01250Ton GVs 0:1250IPGV
(1)
MRRðmg= min Þ for Rice bran BD
¼ 163:3 þ 0:891Ton þ 28:1IP þ 3:35GV
0:002130ðTon Þ2 1:276ðIPÞ2 0:0198ðGV Þ2
þ 0:0708Ton IP 0:00917Ton GV 0:094IPGV
MRRðmg= minÞ for EDM oil
¼ 202:6 þ 0:790Ton þ 37:06IP þ 3:82GV
The experiments have been designed by adopting the response
surface method using the Box Behnken approach. According to
the design, fifteen experimental runs have been conducted.
Three input parameters, viz. pulse on time (Ton) at three levels
20, 170, and 320 in µsec; peak current (IP) at three levels 4, 8,
and 12 in Ampere; and gap voltage (GV) at three levels 30, 50,
and 70 in Volt have been selected for experiment, and duty
factor, sensitivity, and spark time remain constant at 50%, 50%,
and 3 sec, respectively. For each run, machining has been con­
ducted for 2 minutes. The workpiece has been cleaned properly
before conducting the experiment. Before and after each run of
the experiment, the workpiece material has been weighed care­
fully on a Sartorius make digital weighing machine of model no.
BSA4202S-CW. Then, subtracting the final value from the
initial value and then dividing with the time for each run,
MRR has been calculated using the following equation:
Material Removal Rate ¼
Wb
Wa
t
0:001907ðTon Þ2
þ 0:0583Ton IP
(i)
Results and discussion
The response surface method is a very successful technique for
optimization and enormously being used by the researchers.
This method can be used to investigate and analyze the rela­
tionship between important independent input factors with
output response.[34] The response surface models have been
0:00750Ton GV
¼ 4:51 þ 0:03565Ton þ 0:264IP
0:000092ðTon Þ2
(3)
0:1875IPGV
0:0853GV
0:0197ðIPÞ2
(4)
2
þ 0:000372ðGV Þ þ 0:000778Ton IP
þ 0:000012Ton GV þ 0:00401IPGV
SR ðμmÞ for Rice bran BD
¼ 3:38 þ 0:03007Ton þ 0:413IP
2
0:000078ðTon Þ
þ 0:001121Ton IP
0:0505GV
2
0:0251ðIPÞ þ 0:000178ðGV Þ2
(5)
0:000025Ton GV þ 0:00260IPGV
R ðμmÞ for ED Moil
3:47 þ 0:02546Ton þ 1:535IP þ 0:0989GV
0:000081ðTon Þ2
þ 0:002614Ton IP
where Wb is the weight of the workpiece before machining
(mg), Wa is the weight of the work piece after machining (mg),
and t is the time of machining (min).
After machining the surface, the roughness value of the
workpiece has been measured with Mitutoyo SURFTEST SJ210. Surface roughness has been measured with a sampling
length of 0.8 mm and a speed of 0.25 mm/sec and measuring
range in auto mode. Eight readings have been taken for each
machined surface at different spots, and the arithmetic mean
value is recorded. All experimental results with the design
experiment matrix are presented in Table 1.
0:0198ðGV Þ2
SRðμmÞ for Jatropha BD
¼
;
1:432ðIPÞ2
(2)
0:0967ðIPÞ2
0:001084ðGV Þ2
(6)
0:000078Ton GV þ 0:00298IPGV
The analysis of variance (ANOVA) has been carried out to
investigate the contribution of input process factors to the
output response parameters, i.e. MRR and surface roughness,
and also for testing the feasibility of the constructed regression
models using MINITAB statistical software. The data are pre­
sented in Table 2.
It has been noticed that for all the dielectrics in evaluating
MRR, each and every factors are significant. However, for SR,
only gap voltage is insignificant. This ANOVA table reveals
that all R2 values are closer to unity. So, the response model
perfectly fit the obtained data (Table 2). Here, predicted data
approach actual data. For MRR, the predicted R2 value in
Jatropha BD is observed to be 95.22%, in Rice bran BD, it is
84.56%, and in EDM oil, it is 84.89%, whereas the adjusted R2
values in Jatropha BD are found to be 97.83%, in Rice bran BD,
92.92%, and in EDM oil, 95.40%. Likewise, for SR, the pre­
dicted R2 values in Jatropha BD are observed to be 84.12%, in
Rice bran BD, 82.99%, and in EDM oil, 80.64%, whereas the
adjusted R2 values in Jatropha BD are found to be 90.53%, in
MATERIALS AND MANUFACTURING PROCESSES
5
Table 2. ANOVA table.
Source
DOF
Adj. SS
Adj. MS
F
P
(a) For MRRJatropha BD
Ton
1
19,503.1
19,503.1
134.89
.000
IP
1
41,328.1
41,328.1
285.84
.000
GV
1
6050.0
6050.0
41.84
.001
Square
3
9174.6
3058.2
21.15
.003
Interaction
3
16,531.3
5510.4
38.11
.001
Error
5
722.9
144.6
Lack of fit
3
206.2
68.7
0.27
.848
Pure error
2
516.7
258.3
Total
14
93,310.0
S = 12.0243; R2 = 99.23%; R2(pred) = 95.22%; R2(adj) = 97.83%
(c) For MRRRice bran BD
Ton
1
13,612.5
13,612.5
40.23
.001
IP
1
28,800.0
28,800.0
85.12
.000
GV
1
2812.5
2812.5
8.31
.034
Square
3
9551.7
3183.9
9.41
.017
Interaction
3
10,475.0
3491.7
10.32
.014
Error
5
1691.7
338.3
Lack of fit
3
475.0
158.3
0.26
.851
Pure error
2
1216.7
608.3
Total
14
66,943.3
S = 18.3938; R2 = 97.47%; R2(pred) = 84.56%; R2(adj) = 92.92%
(e) MRREDM Oil
Ton
1
9800.0
9800.0
52.04
.001
IP
1
27,612.5
27,612.5
146.62
.000
GV
1
2812.5
2812.5
14.93
.012
Square
3
8268.3
2756.1
14.63
.007
Interaction
3
7825.0
2608.3
13.85
.007
Error
5
941.7
188.3
Lack of fit
3
475.0
158.3
0.68
.642
Pure error
2
466.7
233.3
Total
14
57,260.0
S = 13.7235; R2 = 98.36%; R2(pred) = 84.89%; R2(adj) = 95.40%
Cont (%)
20.90
44.29
6.48
9.83
17.72
0.77
0.22
0.55
100.00
20.23
42.80
4.18
15.44
15.57
1.77
0.71
1.065
100.00
17.11
48.22
4.91
14.44
13.67
1.64
0.83
0.82
100.00
Rice bran BD, 90.68%, and in EDM oil, 95.46%, respectively.
The above values explore that the adjusted R2 values and
predicted R2 values for all the dielectrics are nearer adequate.
So it validates the developed models with vigorous prediction
ability.
The intensity of the relationship between predictors and
the responses is decided by the correlation coefficient. In
the MRR-IP effect, r values are observed to be 0.666, 0.656,
and 0.694 for Jatropha BD, Rice bran BD, and EDM oil,
respectively. In the MRR-TON effect, those are obtained
0.457, 0.451, and 0.413, respectively. In MRR-GV, those
are observed to be 0.249, 0.337, and 0.351 for those oils,
respectively. The results reveal that r values are positive in
all combination effects for all dielectrics. It divulges an
association between them in all the combinations. It implies
that one will increase with the increase of the other.
Moreover, r values for MRR-IP have been observed to be
highest, whereas for MRR-GV, it is observed to be lowest.
Therefore, strongest association is detected in MRR-IP, in
contrast, weakest association in MRR-GV. Finally, among
MRR-IP, MRR-TON, and MRR-GV, r values for MRR-IP
are found to be largest for each dielectric. Hence, strongest
association is detected there.
Similarly, focusing on the average surface roughness condi­
tion, in the SR-IP effect, r values are found to be 0.438, 0.510, and
0.612 for Jatropha BD, Rice bran BD, and EDM oil, respectively.
In the SR-TON effect, Pearson’s r values are found to be 0.655,
0.659, and 0.592. In the SR-GV effect, r values are noted to be
0.806, 0.803, and 0.744. It is observed that r values for SR-IP,
SR-TON, and SR-GV are all positive. It ensures an association in
Source
DOF
Adj. SS
Adj. MS
F
P
(b) For Ra Jatropha BD
Ton
1
22.5792
22.5792
63.41
.001
IP
1
10.1138
10.1138
28.40
.003
GV
1
0.6334
0.6334
1.78
.240
Square
3
16.2676
5.4225
15.23
.006
Interaction
3
1.2869
0.4290
1.20
.398
Error
5
1.7805
0.3561
Lack of fit
3
0.3167
0.1056
0.14
.925
Pure error
2
1.4638
0.7319
Total
14
52.6614
S = 0. 596,747; R2 = 96.62%; R2(pred) = 84.12%; R2(adj) = 90.53%
(d) For Ra RicebranBD
Ton
1
23.2835
23.2835
65.15
.000
IP
1
14.0556
14.0556
39.33
.002
GV
1
0.8398
0.8398
2.35
.186
Square
3
11.7226
3.9075
10.93
.012
Interaction
3
2.0071
0.6690
1.87
.252
Error
5
1.7869
0.3574
Lack of fit
3
0.3718
0.1239
0.18
.905
Pure error
2
1.4151
0.7076
Total
14
53.6955
S = 0. 597,814; R2 = 96.67%; R2(pred) = 82.99%; R2(adj) = 90.68%
(f) For Ra EDM oil
Ton
1
40.4280
40.4280
107.96
.000
IP
1
43.2400
43.2400
115.47
.000
GV
1
0.0040
0.0040
0.01
.923
Square
3
19.7110
6.5703
17.55
.004
Interaction
3
10.2890
3.4296
9.16
.018
Error
5
1.8720
0.3745
Lack of fit
3
1.3210
0.4403
1.60
.407
Pure error
2
0.5510
0.2757
Total
14
115.5440
S = 0. 611,935; R2 = 98.38%; R2(pred) = 80.64%; R2(adj) = 95.46%
Cont (%)
42.88
19.20
1.20
30.89
2.44
3.38
0.60
2.78
100.00
43.36
26.18
1.56
21.83
3.74
3.33
0.69
2.64
100.00
34.99
37.42
0.00
17.06
8.90
1.62
1.14
0.48
100.00
each combination. Moreover, the r values for SR-GV are max­
imum over SR-IP and SR-TON for each dielectric. Hence, SR-GV
ensures the strongest association.
Optimization formulation
The functional relationship between the input and output
factors is represented by surrogate models. Hence, the techni­
ques such as response surface methodologies, kriging, artificial
neural networks (ANN), and SVR are used to construct surro­
gate models. Such model development and optimization stu­
dies often include uncertainty analysis as an important
procedure. In this context, a novel clustering algorithm devel­
oped by Inapakurthi et al.[35] has been found to yield results
better than the box technique. In another instance, a novel
data-based intelligent sampling technique for CCP integrating
the machine learning techniques with the Fuzzy C-means
algorithm has been found to be very helpful in dealing with
uncertainty.[36] The Artificial Neural Network modeling tech­
nique with neural networks composed of different layers of
interconnected artificial neurons plays a major role in under­
standing different linear and nonlinear engineering problems.
The information is provided by the interlinked weights, which
are modified during the learning phase.[37] Support vector
regression (SVR) based on the same basic idea as the support
vector machine acknowledges the presence of nonlinearity in
the data and provides a proficient prediction model.[38] Kriging
is one of the effective spatial interpolation models with low
prediction error and low execution time, which predicts the
6
T. CHAKRABORTY ET AL.
Table 3. Optimization formulation.
Objective Function
Jatropha BD
Maximize MRR Jatropha BD
Minimize SR Jatropha BD
Rice bran BD
Maximize MRR Rice bran BD
Minimize SR Rice bran BD
EDM Oil
Maximize MRR EDM Oil
Minimize SR EDM Oil
Constraints
Decision variables
20 µs < Ton <320 µs, 4 A < IP < 12 A, 30 V < GV <70 V
20 µs < Ton <320 µs, 4 A < IP < 12 A, 30 V < GV <70 V
Ton, IP, and GV
Ton, IP, and GV
20 µs < Ton <320 µs, 4 A < IP < 12 A, 30 V < GV <70 V
20 µs < Ton <320 µs, 4 A < IP < 12 A, 30 V < GV <70 V
Ton, IP, and GV
Ton, IP, and GV
20 µs < Ton <320 µs, 4 A < IP < 12 A, 30 V < GV <70 V
20 µs < Ton <320 µs, 4 A < IP < 12 A, 30 V < GV <70 V
Ton, IP, and GV
Ton, IP, and GV
unknown value of a function at a point on the basis of some
known values in the neighboring points. But, since the software
programs necessary for fitting of Kriging models are rarely
available, hence, more effort is required for building Kriging
models than a simple response surface model. In general, RSMs
use first- or second-order functions for model regression. In
optimization, an efficient algorithm can be prepared combin­
ing Kriging with RSM as the surrogate model. This combined
model strengthens its capability of finding global optimum as
compared to only RSM.[39,40] However, RSM is capable of
designing the experiment and predicting the optimum para­
metric conditions for the considered output parameters.
Subsequently performing an analysis of variance can give
insights into the reliability of the developed model. Hence,
one does not have to go for multiple algorithms or software
packages for conducting these three tasks, i.e. designing the
experiment, optimization, and reliability checking of the devel­
oped model. Considering all these factors, RSM has been opted
for the optimization process. The optimization formulation is
presented in Table 3.
The established regression equations have been used to inves­
tigate the effect of variation of input process parameters over the
material removal rate and average surface roughness. Keeping
two input parameters constant for each dielectric oil, the effect
of another parameter on each output response has been
observed. Using Origin software, individual graphs are plotted.
Effect of the variation of process parameters on MRR
The effect of the variation of pulse on time, peak current, and
gap voltage on the material removal rate for three dielectrics is
shown in Fig. 2(a,c,e), respectively. The MRR for both the biodielectrics is higher compared to EDM oil for a larger pulse
duration, whereas for lower pulse on time, MRR for EDM oil
shows the reverse result with respect to bio-dielectrics. Due to
low specific heat, bio-dielectrics ensure effective heat utiliza­
tion. This is because less heat is required per 0C change in
temperature per unit mass, which removes wastage of
energy.[28] Jatropha BD bestows better MRR than EDM oil
after a pulse duration of 130 µs. Higher viscosity, higher ther­
mal conductivity, and higher breakdown voltage enable the
dielectric to enhance ionization capability for an adequate
time. As a consequence, MRR intensifies relatively for biodielectrics.[41,42] Next, when the pulse duration crosses
a specific value, MRR for all dielectrics tends to decrease.
This could be because of the fact that pulse off time shows
the inverse nature with pulse on time since duty factor remains
unchanged. Hence, beyond a certain value of pulse on time,
pulse off time becomes too low to clear out the debris from the
machining zone. As a consequence, MRR decreases.[43] The
graph shown in Fig. 2(c) exhibits the comparative response
characteristics for the effect of peak current on MRR. Due to
the increase of peak current, discharge energy also increases for
a constant pulse on time value and consequently increases the
material erosion rate.[44] Here, the increase of MRR with peak
current is observed for all dielectrics. The highest MRR is
obtained for Jatropha BD. This may occur due to the higher
oxygen value, higher thermal conductivity, and relatively
higher density of Jatropha BD[17,23,24]. For higher oxygen
value, higher temperature is obtained, and for higher density,
higher flushing efficiency is achieved, which leads to high
melting and vaporization capability. Moreover, higher thermal
conductivity ensures higher transfer of thermal energy in the
sparking zone. Initially, MRR for EDM oil is more than that for
Jatropha BD, and after 7 Amp MRR for Jatropha, BD exceeds
EDM oil. This may be because initially, the ion dissociation
rate of Jatropha BD is somewhat low and later, it increases.
Hence, after complete dissociation, MRR again improves.[45]
It is also noticed in the plot shown in Fig. 2(e) that MRR
comes out to be better at lower gap voltage. It displays a reverse
effect if the gap voltage increases. As a consequence of higher
gap voltage, the spark gap also increases, leading to the
decrease of spark intensity, and MRR gradually declines.[43]
In addition, at lower gap voltage, bio-dielectrics remove more
materials than using EDM oil when pulse on time and peak
current remain constant. Due to detained breakdown of dielec­
tric, the discharge energy might be increased and results in
more MRR.[46]
Effect of variation of process parameters on average
surface roughness
The effect of pulse on time, peak current, and gap voltage on
average surface roughness for three dielectrics is shown in
Fig. 2(b,d,f), respectively. The pattern of these graphs con­
cludes that bio-dielectrics are more favorable for achieving
good surface finish than EDM oil. Relatively higher viscosity
of these bio-dielectrics creates a sticky adhesive wet surface to
achieve finer surface finish.[25] Here, the graph also exhibits the
diminishing trend of the surface quality with the increase of
peak current and pulse on time. It happens because of larger
available pulse energy. Consequently, larger and deeper craters
MATERIALS AND MANUFACTURING PROCESSES
7
Figure 2. Effect of pulse on time, peak current, and gap voltage on the material removal rate and average surface roughness.
are generated causing more rough surface formation.[47,48] It is
also perceived that with the increase of gap voltage, biodielectrics prove to be more effective to improve surface quality
than EDM oil when pulse on time and peak current remain
constant. In EDM oil, it aggravates first up to a certain limit
and then improves again. The spark gap increases with gap
voltage, and simultaneously, the spark intensity decreases, due
to which minor and shallow craters are generated, leading to
the development of lower surface roughness.[49]
Using Jatropha BD, at a pulse on time of 20 µs, a peak
current of 4 amp, and a gap voltage of 70 v and a pulse on
time of 320 µs, a peak current of 12 amp, and a gap voltage of
30 V, the optimum values for surface roughness and MRR are
obtained to be 3.39 µm and 330 mg/min, respectively, whereas
the predicted values are 2.9769 µm and 343.5417 mg/min,
respectively. Three readings for each were taken, and their
average was taken. Hence, the errors were found to be
13.876% and 3.94% in SR and MRR, respectively. Likewise,
8
T. CHAKRABORTY ET AL.
using EDM oil, optimum values for surface roughness and
MRR are obtained for the pulse on time, peak current, and
gap voltage of 20 µs, 4 amp, and 70 v and for the pulse on time,
peak current, and gap voltage of 320 µs, 12 amp, and 30 v,
respectively. In the case of SR and MRR each, three readings
have been taken, and their average was taken. The optimum
values of SR and MRR have been obtained to be 4.565 µm and
290 mg/min, respectively, whereas the predicted values are
4.1175 µm and 274.5833 mg/min, respectively. The errors
were found to be 10.87% and 5.61%, respectively. Similarly,
using Rice bran BD, the optimum values for surface roughness
and MRR have been obtained at a pulse on time of 20 µs, a peak
current of 4 amp, a gap voltage of 70 v and a pulse on time of
320 µs, a peak current of 12 amp, and a gap voltage of 30 v,
respectively. Three readings have been taken for SR and MRR
each, and their average was taken. The optimum values of SR
and MRR are found to be 3.7020 µm and 320 mg/min, respec­
tively, whereas the predicted values are 3.320 µm and 287.5833
mg/min, respectively. The errors were found to be 11.50% and
10.50% in SR and MRR, respectively. The test result concludes
that the optimum value of Jatropha BD for SR is least, i.e. 3.39
µm, and then comes the Rice bran BD, i.e. 3.7020 µm, and the
highest value is observed for EDM oil, i.e. 4.565 µm. Similarly,
the optimum value for Jatropha BD on MRR is 330 mg/min
and then comes for Rice bran BD, i.e. 320 mg/min, and the
lowest is observed for EDM oil, i.e. 290 mg/min.
Analysis of surface topography
Recast layer thickness
The recast layer thickness in the EDM process is primarily due
to incomplete flushing of the molten metal, which can be due
to the material removal rate being too high for the flushing rate
used, low discharge gap due to high breakdown voltage, or high
viscosity of the dielectric fluid used. Here, for getting the recast
layer thickness first, the workpiece samples obtained under the
optimum parametric condition for minimum surface rough­
ness are cut crosswise. They are polished with polishing paper
sequentially with grit numbers 600, 1000, 1500, and 2000
followed by diamond polishing on velvet cloth. Then, the
samples are etched with a solution of 2 ml of HF (48%), 3 ml
of HCl, 5 ml of HNO3, and 190 ml of H2O (Kellers reagent) for
15 seconds. Then, the samples are cleaned with acetone and
dried for 30 minutes. Then, the recast layer image has been
acquired by FESEM (make: ZEISS, Germany, Model: SUPRA
55) and the recast layer thickness has been measured by pro­
cessing these FESEM images using Image-J software.[50] From
the obtained recast layer thickness values, it has been observed
that the recast layer thickness is maximum for Rice bran BD,
i.e. 10 µm, followed by EDM oil, i.e. 268.89 nm, and is mini­
mum in the case of Jatropha BD, 123.5 nm. If we observe the
MRR values under optimum conditions for maximum MRR,
then least MRR for EDM oil as compared to other two dielec­
trics should give a least recast layer thickness, but a least recast
layer thickness is obtained in the case of Jatropha BD. Hence,
MRR is not the governing factor for the recast layer thickness
in this case. Then, on comparing the dielectric properties as
given in Table 6, it is seen that breakdown voltage is minimum
for Rice bran BD, whereas maximum for EDM oil. Hence, the
recast layer thickness should be minimum for Rice bran BD.
But in reality, it is just the opposite, i.e. the recast layer thick­
ness is maximum for Rice bran BD. Hence, breakdown voltage
is also not the prime cause for the recast layer thickness. But if
we compare the measured kinematic viscosity values for the
three dielectrics as presented in Table 6, it is maximum for Rice
bran BD followed by EDM oil and is minimum for Jatropha
BD. The measured recast layer thickness values also follow the
same order, i.e. maximum for Rice bran BD followed by EDM
oil and minimum for Jatropha BD. Hence, it can be ascertained
that viscosity is the dominant factor here responsible for the
obtained trend of the recast layer thickness.
Surface morphology
The machined surfaces obtained under optimum parametric
conditions for minimum surface roughness for each of the
dielectric conditions have been observed through FESEM for
studying the surface morphology. The FESEM images as
shown in Fig. 3 indicate that the craters in the case of EDM
oil are larger as compared to other two dielectric conditions.
The surface obtained in the case of Jatropha BD has minimum
unevenness, whereas that in the case of Rice bran BD lies in
between the other two dielectric conditions.
Subsurface hardness
For the measurement of microhardness, the transverse section
of the workpiece samples obtained under optimum parametric
conditions are polished with polishing paper sequentially with
grit numbers 600, 1000, 1500, and 2000 followed by diamond
polishing on velvet cloth. Then, starting from the surface, three
indents have been made at a specific depth at different loca­
tions. This process is repeated at equal intervals of depth along
the transverse section. The microhardness measurement has
been carried out using a microhardness tester (Mitutoyo HM200 Autovics) with a load of 0.3 Kgf and a dwell time of 10
second. The error plot for microhardness is presented in Fig. 4.
It is observed that for all the three dielectric fluids, the mean
value of microhardness varies within a similar range of 48 HV
to 70 HV. Near to the machined surface, a higher value of
microhardness is observed, which gradually decreases and
stabilizes around 48 HV to 53 HV. Heating and quenching
phenomena in the EDM process might be the reason for higher
hardness values near to the machined surfaces, and as the
region of parent material, i.e. region unaffected by heating
and quenching effect, is reached, the hardness values might
be stabilizing at a lower value. Again, the hardness values
obtained for the two vegetable oil dielectrics are not much
different from those of EDM oil.
Multiobjective optimization
So far, the individual objectives have been studied for each of
the dielectric oils. But for overall performance evaluation of
each oil, it is necessary to determine the optimum parametric
condition with maximization of MRR and minimization of SR.
Hence, a multiobjective optimization (MOO) has been carried
out using Minitab statistical software, and the findings are
summarized in Table 4. In the RSM approach for
MATERIALS AND MANUFACTURING PROCESSES
9
Figure 3. (a–c) Recast layer thickness measured for different dielectric conditions; (d–f) surface texture obtained for different dielectric conditions at optimum surface
roughness.
Figure 4. Microhardness profiles along the transverse section of the machined samples.
Table 4. Responses after multiobjective optimization for all the dielectrics.
Ton (µs)
Jatropha BD
55
55
EDM oil
90
90
Rice bran BD
65
65
Peak current (A)
Gap voltage (V)
9
9
54
54
12
12
9
9
Output response
Predicted value
Obtained value
Error %
SR (µm)
MRR (mg/min)
5.929
111
6.512
120
9.83
8.11
30
30
SR (µm)
MRR (mg/min)
8.165
159
9.121
170
11.71
6.7
54
54
SR (µm)
MRR (mg/min)
6.337
122
6.891
130
8.74
6.23
10
T. CHAKRABORTY ET AL.
multiobjective optimization, the desirability approach is used.
In the desirability approach, each response (yi) is given
a number between 0 and 1 by the desirability function where
0 and 1 correspond to the completely undesirable value and
completely desirable value, respectively. The individual desir­
ability function is calculated using Eqs. (7), (8), and (9) respec­
tively, for maximization, minimization, and target, which are
the best types of optimization problems. Then, the overall
desirability (D overall) is calculated using the geometric mean
by combining the individual desirability (desi),
Doverall ¼ ðdes1 ðy1 Þ � des2 ðy2 Þ � des3 ðy3 Þ
. . . : � desn ðyn ÞÞ1=n ;
where y1, y2 . . . , yn are individual responses and n is the
total number of responses. This overall desirability (D overall) is
then maximized to get the most favorable parametric condi­
tions for the MOO problem. But during the calculation of
Doverall, the individual desirability values can be 0 or 1 depend­
ing upon a completely unacceptable or completely desirable
value, respectively. In case, the individual desirability value
becomes zero, the Doverall becomes zero. Hence, to avoid this
in reality, practice fitted values of the individual responses are
used,
0
ðyfit
Lowi Þ=
i ðxÞ
desi ðyfit
i Þ ¼
ðTari Lowi ÞÞs
1
yfit
i ðxÞ < Lowi
Lowi � yfit
i ðxÞ � Tari ;
yfit
i ðxÞ > Tari
(7)
1
ððyfit
Upi Þ
fit
i ðxÞ
desi ðyi Þ ¼
=ðTari Upi ÞÞs
0
yfit
i ðxÞ < Tari
Tari � yfit
i ðxÞ � Upi ;
yfit
i ðxÞ > Upi
(8)
0
ððyfit
Lowi Þ
i ðxÞ
s
=ðTar
Low
i
i ÞÞ
desi ðyfit
Þ
¼
i
ððyfit
Upi Þ
i ðxÞ
=ðTari Upi ÞÞt
0
maximum value obtained for the ith response in the considered
input data range, and Tari is the target value for the ith
response.[51]
The formulations for multiobjective optimization for each
of the dielectric fluids are presented in Table 5.
After MOO for Jatropha BD, the obtained SR is found to be
92.09% higher than that obtained for single optimization of SR,
and MRR is found to be 63.64% lower than that obtained for
single optimization of only MRR. After MOO for Rice bran BD,
the obtained SR is found to be 86.14% higher than that obtained
for single optimization of SR, and MRR is found to be 59.38%
lower than that obtained for single optimization of only MRR.
After MOO for EDM oil, the obtained SR is found to be 99.80%
higher than that obtained for single optimization of SR, and MRR
is found to be 41.38% lower than that obtained for single optimi­
zation of only MRR.
For studying the surface topography and the recast layer thick­
ness, the work samples obtained for each of the dielectrics under
parametric conditions obtained by multiobjective optimization
have been prepared following the same procedure as discussed
earlier. Depending upon the visibility, the FESEM images have
been obtained at different magnifications and analyzed through
proper scale setting in “image j” software for the measurement of
the recast layer thickness. The images for the recast layer and
surface texture are presented in Figure 5(a–e). It has been
observed that the recast layer thickness is maximum for Rice
bran BD followed by EDM oil and minimum for Jatropha BD.
This trend is similar to that obtained for single optimization
condition for each of the dielectrics. From Fig. 5(d–f), it is
observed that the machined surface for EDM oil is uneven with
some deposits. But that for Jatropha BD is smooth and more even
with lesser deposits. The machined surface obtained for Rice bran
BD has some deposits, and its overall texture lies in between EDM
oil and Jatropha BD.
yfit
i ðxÞ < Lowi
Lowi � yfit
i ðxÞ � Tari
;
Tari � yfit
i ðxÞ � Upi
yfit
i ðxÞ > Upi
(9)
where desi (yifit) is the value given by the desirability func­
tion corresponding to the fitted value of ith response, yifit is the
fitted value of ith response, Lowi is the lowest value obtained for
the ith response in the considered input data range, Upi is the
Analysis of sustainability aspects
The use of fossil fuels is alarming as it is supposed to be run out
by the middle of the twenty-first century. Moreover, the con­
ventional oils are incapable to abide by the health and environ­
mental law due to low values of the flash point and high toxic
harmful gas emission, which leads to poor sustainability. In
this regard, bio-dielectrics in EDM have potentiality in remov­
ing mostly such kinds of problems. Therefore, the researchers
are favoring vegetable oils as dielectric with respect to conven­
tional oil on the sustainability and degradability issue.
However, the dielectric fluids used in EDM need certain prop­
erties like high flash point, low viscosity, low density, high
breakdown voltage, and higher thermal conductivity.
Therefore, to access capabilities of the Jatropha BD and Rice
Table 5. Formulation for multiobjective optimization for each of the dielectric fluids.
Objective Function
Maximize DJatropha BD
Maximize DRice bran BD
Maximize DEDM Oil
Constraints
20 µs < Ton <320 µs, 4 A < IP < 12 A, 30 V < GV <70 V
20 µs < Ton <320 µs, 4 A < IP < 12 A, 30 V < GV <70 V
20 µs < Ton <320 µs, 4 A < IP < 12 A, 30 V < GV <70 V
Decision
variables
Ton, IP, and GV
Ton, IP, and GV
Ton, IP, and GV
MATERIALS AND MANUFACTURING PROCESSES
11
Figure 5. (a–c) Recast layer thickness for different dielectric conditions; (d–f) Surface texture for different dielectric conditions at validation for multiobjective
optimization.
Table 6. Properties of the used dielectrics.
Sl. No.
1
2
3
4
5
6
7
8
9
10
11
12
a
b
c
d
e
f
g
Parameters
Flash point (oC)
Pour point (oC)
Kinematic viscosity
at 40°C (cSt)
Thermal conductivity
at 20°C (Wm−1K−1)
Specific heat (KJKg−1K−1)
Specific gravity
at 35 °C
Breakdown voltage (KV/mm gap)
Dielectric constant
Moisture content (%)
Acidity (neutralization value)
(Mg of KOH/gm of sample)
Density at 15°C (gm/cm3)
Dissolved gas analysis
Hydrogen as H2 (ppm)
Carbon Dioxide as CO2 (ppm)
Ethane as C2H2 (ppm)
Ethylene as C2H4 (ppm)
Acetylene as C2H2 (ppm)
Carbon monoxide as CO (ppm)
Methane as CH4 (ppm)
Testing method
IS 1448 [P:21] 2004
IS 1448 [P: 10] 1976 (RA 2007)
IS 1448 [P:25] 1976 (RA 2007)
EDM Oil
106
−25
5.54
Rice Bran BD
150
−10
8.92
Jatropha BD
165
−18
4.76
IS 3346–1980
0.18
0.20
0.23
ASTM
IS 1448 [P:16] 1991 (RA 2003)
1.96
0.7975
1.58
0.8690
1.67
0.8634
IS 6792: 1992
IS 6262: 1971 (RA 2006)
IS 1448 [P:40] 1991 (RA 2003)
IS 1448 [P:2] 2007 2nd Rev
43.5
2.3
14
0.06
38.0
2.7
1.50
0.18
40.1
2.9
12.0
0.64
IS 1448 [P:16] 1991 (RA 2003)
IS 9434: 1992 (RA − 2003)
0.7968
<5.0
14
<5.0
<5.0
<5.0
9
<5.0
0.8682
<5.0
12
<5.0
<5.0
<5.0
8
<5.0
0.8627
<5.0
9
<5.0
<5.0
<5.0
6
<5.0
Bran BD vegetable oils, the important properties of the two
vegetable oils and standard EDM oil have been tested and are
presented in Table 6.
Low-viscosity fluid enhances pumping capability, flushing
efficiency, and cooling capacity. As a consequence, the scope
of fumes and vapor formation reduces. Therefore, using the
transesterification process, the viscosity of vegetable oils has
been reduced. The viscosity of Jatropha oil has been lowered
by 92.38%, and of Rice bran oil, 90%. Here, the viscosity for
Jatropha BD is found to be lowest, i.e. 4.76 cSt, whereas it is
slightly more for other two dielectrics. As the values are nearly
equal, no such variation of the effects appears. The insulating
characteristic of a dielectric is determined by breakdown voltage
or dielectric strength. The higher the dielectric strength, the
higher its insulating property. After transesterification, the
breakown voltage for Jatropha oil has been improved by
11.38%, and for Rice bran oil, it is enhanced by 11.76%. The
breakdown voltage values obtained for EDM oil, Jatropha BD,
and Rice Bran BD are nearly closer, i.e. 43.5 V, 40.1 V, and 38.0
V, respectively, and this minor variation does not create any
significant effect on the result. Moreover, the lower value of
density is preferable for efficient flushing of debris. In this
context, after transesterification, density for Jatropha oil has
been reduced by 5.54%, and for Rice bran oil, by 5.29%. The
12
T. CHAKRABORTY ET AL.
density obtained for both the bio-dielectrics is nearly the same
and little higher than that obtained for the EDM oil (Table 6).
From the test results, the flash point for EDM oil is found to be
106°C, whereas in Rice bran BD and Jatropha BD, it is found to
be 150°C and 165°C, respectively. The tested values ensure that
the bio-dielectrics are operationally safer than EDM oil based on
fire prevention. Higher thermal conductivity and higher specific
heat are preferable for a better cooling effect of both the electro­
des and material integrity. The thermal conductivity obtained
for EDM oil, Rice Bran BD, and Jatropha BD is 0.18, 0.20, and
0.23 W.m−1.K−1, and the specific heat is found to be 1.96, 1.58,
and 1.67 KJ.kg−1.K−1, respectively. Here, for all the dielectrics,
the values are almost nearer. As the acid value is less for all the
three dielectrics, hence, chance of health hazard is less.
The dielectrics have also been tested for dissolved gas ana­
lysis. The results are displayed in Table 6. The test result reveals
that the concentration of hydrogen gas and organic gases such
as ethane, ethylene, acetylene, and methane is below 5 ppm in
all the dielectrics. Due to the low value of organic matter, the
propensity of degradability is higher. The carbon dioxide and
carbon monoxide level in EDM oil has been observed to be
more than that in other two bio-dielectrics, whereas least in
Jatopha BD. The test result explores that the carbon dioxide
value in EDM oil, Rice Bran BD, and Jatropha BD is 14, 12, and
9 ppm, respectively, whereas carbon monoxide values are 9, 8,
and 6 ppm, respectively. Therefore, the chance of toxic carbon
monoxide and carbon dioxide gas emission is more for EDM
oil, and it is least in Jatropha oil BD. Therefore, the biodielectrics can replace the conventional dielectrics in regard
to their potentiality.
●
●
●
●
●
●
the least MRR and the highest SR values were obtained for
EDM oil among three dielectrics, i.e. 290 mg/min and
4.565 µm, respectively.
Comparing the overall performance of each dielectric fluid,
maximum MRR has been obtained for EDM oil followed
by Rice bran BD and Jatropha BD. But with respect to
average surface roughness, Jatropha BD outperformed the
other two dielectrics with a Ra value of 6.512 µm.
From the ANOVA result with a R2 predicted value and R2
adjusted value, the significance of the developed model
has been noticed.
After FESEM, the recast layer thickness obtained is max­
imum for Rice bran BD followed by EDM oil and is
minimum in the case of Jatropha BD.
From the FESEM images, the craters found in the case of
EDM oil are larger as compared to other two dielectric
conditions. The surface obtained in the case of Jatropha
BD has minimum unevenness, whereas that in the case of
Rice bran BD lies in between the other two dielectric
conditions.
Higher values of microhardness have been observed near
to the machined surface, which gradually decreases
downward along the depth. The microhardness values
obtained for the both vegetable oil dielectrics are in the
similar range as that of the conventional EDM oil
dielectric.
The dissolved gas analysis report reveals that the chances
of toxic carbon monoxide and carbon dioxide gas emis­
sion are highest in EDM oil, whereas it is least in Jatropha
oil BD. The chance of this toxic gas emission in Rice bran
BD is less than that in EDM oil.
Conclusions
A comparative study of die-sinking electrodischarge machin­
ing performance of Al 6063 material incorporated with 1 vol.
% of B4C has been carried out employing three dielectrics
such as Jatropha biodiesel, Rice bran biodiesel, and EDM oil
in this work. Peak current, pulse on time, and gap voltage
have been selected as input process parameters together with
MRR and SR as two output responses. In addition, surface
topographical images have been captured using a field emis­
sion scanning electron microscope to compare and analyze
the surface morphology of the machined surfaces. So on
account of the above investigations, the following conclusions
are derived:
● The obtained MRR values are nearly identical for all
dielectrics, but at higher peak current value and gap
voltage, the MRR values for vegetable oils have been
found to be better than those for the EDM oil.
● The SR values for both bio-dielectrics have been observed
to be less than those for EDM oil for all parameter
settings. In addition, a finer surface has been obtained
by Jatropha BD than Rice bran BD.
● At the optimal setting of process parameters for Jatropha
BD, the highest MRR of 330 mg/min and at an IP of 4 A,
a TON value of 20 µs, and a GV value of 70 V, the finest
surface finish of 3.39 µm have been achieved. In reverse,
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Tapas Chakraborty
http://orcid.org/0000-0002-8901-8703
http://orcid.org/0000-0002-2657-0381
Deepti Ranjan Sahu
Amitava Mandal
http://orcid.org/0000-0002-2486-380X
References
[1] Dhakar, K.; Chaudhary, K.; Dvivedi, A.; Bembalge, O. An
Environment-Friendly and Sustainable Machining Method:
Near-Dry EDM. Mater. Manuf. Process. 2019, 34(12), 1307–1315.
DOI: 10.1080/10426914.2019.1643471.
[2] Luis, C. J.; Puertas, I.; Villa, G. Material Removal Rate and
Electrode Wear Study on the EDM of Silicon Carbide. J. Mater.
Process. Technol. 2005, 164–165, 889–896. DOI: 10.1016/j.
jmatprotec.2005.02.045.
[3] Marafona, J.; Chousal, J. A. G. A Finite Element Model of
EDM Based on the Joule Effect. Int. J. Mach. Tools Manuf.
2006, 46(6), 595–602. DOI: 10.1016/j.ijmachtools.2005.07.017.
[4] Dong, H.; Liu, Y.; Li, M.; Zhou, Y.; Liu, T.; Li, D.; Sun, Q.;
Ji, R. Experimental Investigation of Water-In-Oil
Nanoemulsion in Sinking Electrical Discharge Machining.
Mater. Manuf. Process. 2019, 34(10), 1129–1135. DOI:
10.1080/10426914.2019.1628266.
MATERIALS AND MANUFACTURING PROCESSES
[5] Kumar, S.; Ghoshal, S. K.; Arora, P. K.; Nagdeve, L. Multi-Variable
Optimization in Die-Sinking EDM Process of AISI420 Stainless
Steel. Mater. Manuf. Process. 2021, 36(5), 572–582. DOI: 10.1080/
10426914.2020.1843678.
[6] Karthik, S.; Prakash, K. S.; Gopal, P. M.; Jothi, S. Influence of
Materials and Machining Parameters on WEDM of Al/alcocrfe­
nimo 0.5 MMC. Mater. Manuf. Process. 2019, 34(7), 759–768. DOI:
10.1080/10426914.2019.1594250.
[7] Snoeys, R.; Staelens, F.; Dekeyser, W. Current Trends in
Non-Conventional Material Removal Processes. CIRP Ann. 1986,
35(2), 467–480. DOI: 10.1016/S0007-8506(07)60195-4.
[8] Kunieda, M.; Lauwers, B.; Rajurkar, K. P.; Schumacher, B. M.
Advancing EDM Through Fundamental Insight into the Process.
CIRP Ann. Manuf. Technol. 2005, 54(2), 64–87. DOI: 10.1016/
s0007-8506(07)60020-1.
[9] Das, S.; Paul, S.; Doloi, B. Assessment of the Impacts of
Bio-Dielectrics on the Textural Features and Recast-Layers of
EDM-Surfaces. Mater. Manuf. Process. 2021, 36(2), 245–255.
DOI: 10.1080/10426914.2020.1832678.
[10] Leão, F. N.; Pashby, I. R. A Review on the Use of
Environmentally-Friendly Dielectric Fluids in Electrical
Discharge Machining. J. Mater. Process. Technol. 2004, 149(1–3),
341–346. DOI: 10.1016/j.jmatprotec.2003.10.043.
[11] Gopalakannan, S.; Senthilvelan, T. EDM of Cast Al/sic Metal
Matrix Nanocomposites by Applying Response Surface Method.
Int. J. Adv. Manuf. Technol. 2013, 67(1–4), 485–493. DOI: 10.1007/
s00170-012-4499-z.
[12] Gopalakannan, S.; Senthilvelan, T. A Parametric Study of Electrical
Discharge Machining Process Parameters on Machining of Cast
Al/b4c Metal Matrix Nanocomposites. Proc. Inst. Mech. Eng. Part
B J. Eng. Manuf. 2013, 227(7), 993–1004. DOI: 10.1177/
0954405413479505.
[13] Zhong, Z.-W. Processes for Environmentally Friendly And/or
Cost-Effective Manufacturing. Mater. Manuf. Process. 2021, 36
(9), 987–1009. DOI: 10.1080/10426914.2021.1885709.
[14] Rajurkar, K. P.; Hadidi, H.; Pariti, J.; Reddy, G. C. Review of
Sustainability Issues in Non-Traditional Machining Processes.
Procedia Manuf. 2017, 7, 714–720. DOI: 10.1016/j.
promfg.2016.12.106.
[15] Singaravel, B.; Chandra Shekar, K.; Rao, K. M.; Reddy, G. G.
Study of Vegetable Oil and Their Properties for as an
Alternative Source to Mineral Oil-Based Dielectric Fluid in
Electric Discharge Machining. National Conference on
Advances in Mechanical Engineering and Nanotechnology
(AMENT2018). Int. J. Mod. Eng. Res. Technol. Hyderabad 2018,
5, 237–244.
[16] Singaravel, B.; Shekar, K. C.; Reddy, G. G.; Prasad, S. D.
Experimental Investigation of Vegetable Oil as Dielectric Fluid in
Electric Discharge Machining of Ti-6al-4V. Ain Shams Eng. J. 2020,
11(1), 143–147. DOI: 10.1016/j.asej.2019.07.010.
[17] Valaki, J. B.; Rathod, P. P. Assessment of Operational Feasibility of
Waste Vegetable Oil Based Bio-Dielectric Fluid for Sustainable
Electric Discharge Machining (EDM). Int. J. Adv. Manuf. Technol.
2016, 87(5–8), 1509–1518. DOI: 10.1007/s00170-015-7169-0.
[18] Dincer, K. Lower Emissions from Biodiesel Combustion. Energy
Sources, Part a Recover. Util. Environ. Eff. 2008, 30(10), 963–968.
DOI: 10.1080/15567030601082753.
[19] Sadagopan, P.; Mouliprasanth, B. Investigation on the Influence of
Different Types of Dielectrics in Electrical Discharge Machining.
Int. J. Adv. Manuf. Technol. 2017, 92(1–4), 277–291. DOI: 10.1007/
s00170-017-0039-1.
[20] Shah, Z. H.; Tahir, Q. A. Dielectric Properties of Vegetable Oils.
J. Sci. Res. 2011, 3(3), 481–492. DOI: 10.3329/jsr.v3i3.7049.
[21] Martin, D.; Khan, I.; Dai, J. An Overview of the Suitability of Vegetable
Oil Dielectrics for Use in Large Power Transformers. 2006.
[22] Abdullahi, U. U.; Bashi, S. M.; Yunus, R.; Mohibullah;
Nurdin, H. A. The Potentials of Palm Oil as a Dielectric
Fluid. Nat Power Energy Conf. PECon 2004 - Proc. 2004,
2004, 224–228.
13
[23] Valaki, J. B.; Rathod, P. P.; Sankhavara, C. D. Investigations on
Technical Feasibility of Jatropha Curcas Oil Based Bio Dielectric
Fluid for Sustainable Electric Discharge Machining (EDM).
J. Manuf. Process. 2016, 22, 151–160. DOI: 10.1016/j.
jmapro.2016.03.004.
[24] Ng, P. S.; Kong, S. A.; Yeo, S. H. Investigation of Biodiesel
Dielectric in Sustainable Electrical Discharge Machining. Int.
J. Adv. Manuf. Technol. 2017, 90(9–12), 2549–2556. DOI:
10.1007/s00170-016-9572-6.
[25] Das, S.; Paul, S.; Doloi, B. Investigation of the Machining
Performance of Neem Oil as a Dielectric Medium of EDM:
A Sustainable Approach. IOP Conf. Ser Mater. Sci. Eng. 2019, 653
(1), 1. DOI: 10.1088/1757-899X/653/1/012017.
[26] Ji, R.; Liu, Y.; Zhang, Y.; Cai, B.; Ma, J.; Li, X. Influence of Dielectric
and Machining Parameters on the Process Performance for Electric
Discharge Milling of SiC Ceramic. Int. J. Adv. Manuf. Technol.
2012, 59(1–4), 127–136. DOI: 10.1007/s00170-011-3493-1.
[27] Valaki, J. B.; Rathod, P. P. Investigating Feasibility Through
Performance Analysis of Green Dielectrics for Sustainable
Electric Discharge Machining. Mater. Manuf. Process. 2016, 31
(4), 541–549. DOI: 10.1080/10426914.2015.1070430.
[28] Schubert, A.; Zeidler, H.; Kühn, R.; Hackert-Oschätzchen, M.
Microelectrical Discharge Machining: A Suitable Process for
Machining Ceramics. J. Ceram. 2015, 2015, 1–9. DOI: 10.1155/
2015/470801.
[29] Pundir, R.; Chary, G. H. V. C.; Dastidar, M. G. Application of
Taguchi Method for Optimizing the Process Parameters for the
Removal of Copper and Nickel by Growing Aspergillus Sp.
Water Resour. Ind. 2018, 20, 83–92. DOI: 10.1016/j.
wri.2016.05.001.
[30] Davis, R.; John, P. Application of Taguchi-Based Design of
Experiments for Industrial Chemical Processes. Stat. Approaches
Emphas. Des. Exp. Appl. Chem. Process. 2018. DOI: 10.5772/
intechopen.69501.
[31] Pantula, P. D.; Miriyala, S. S.; Mitra, K. KERNEL: Enabler to Build
Smart Surrogates for Online Optimization and Knowledge
Discovery. Mater. Manuf. Process. 2017, 32(10), 1162–1171. DOI:
10.1080/10426914.2016.1269918.
[32] Vanaja, K.; Rani, R. H. S. Design of Experiments: Concept and
Applications of Plackett Burman Design. Clin. Res. Regul. Aff. 2007,
24(1), 1–23. DOI: 10.1080/10601330701220520.
[33] Karimifard, S.; Alavi Moghaddam, M. R. Application of Response
Surface Methodology in Physicochemical Removal of Dyes from
Wastewater: A Critical Review. Sci. Total Environ. 2018, 640–641,
772–797. DOI: 10.1016/j.scitotenv.2018.05.355.
[34] Kumar, A.; Mandal, A.; Dixit, A. R.; Das, A. K. Performance
Evaluation of Al2O3 Nano Powder Mixed Dielectric for Electric
Discharge Machining of Inconel 825. Mater. Manuf. Process. 2018,
33(9), 986–995. DOI: 10.1080/10426914.2017.1376081.
[35] Inapakurthi, R. K.; Pantula, P. D.; Miriyala, S. S.; Mitra, K. Data
Driven Robust Optimization of Grinding Process Under
Uncertainty. Mater. Manuf. Process. 2020, 35(16), 1870–1876.
DOI: 10.1080/10426914.2020.1802042.
[36] Sharma, S.; Pantula, P. D.; Miriyala, S. S.; Mitra, K. A Novel
Data-Driven Sampling Strategy for Optimizing Industrial
Grinding Operation Under Uncertainty Using Chance
Constrained Programming. Powder Technol. 2021, 377, 913–923.
DOI: 10.1016/j.powtec.2020.09.024.
[37] Somashekhar, K. P.; Ramachandran, N.; Mathew, J. Optimization
of Material Removal Rate in Micro-EDM Using Artificial Neural
Network and Genetic Algorithms. Mater. Manuf. Process. 2010, 25
(6), 467–475. DOI: 10.1080/10426910903365760.
[38] Inapakurthi, R. K.; Mitra, K. Optimal Surrogate Building Using
SVR for an Industrial Grinding Process. Mater. Manuf. Process.
2022, 00(00), 1–7. DOI: 10.1080/10426914.2022.2039699.
[39] Simpson, T. W.; Mauery, T. M.; Korte, J. J.; Mistree, F. Kriging
Models for Global Approximation in Simulation-Based
Multidisciplinary Design Optimization. Aiaa J. 2001, 39(12),
2233–2241. DOI: 10.2514/2.1234.
14
T. CHAKRABORTY ET AL.
[40] Mogilicharla, A.; Mittal, P.; Majumdar, S.; Mitra, K. Kriging
Surrogate Based Multi-Objective Optimization of Bulk Vinyl
Acetate Polymerization with Branching. Mater. Manuf. Process.
2015, 30(4), 394–402. DOI: 10.1080/10426914.2014.921709.
[41] Wang, X.; Liu, Z.; Xue, R.; Tian, Z.; Huang, Y. Research on the
Influence of Dielectric Characteristics on the EDM of Titanium
Alloy. Int. J. Adv. Manuf. Technol. 2014, 72(5–8), 979–987. DOI:
10.1007/s00170-014-5716-8.
[42] Kiyak, M.; Aldemir, B. E.; Altan, E. Effects of Discharge Energy
Density on Wear Rate and Surface Roughness in EDM. Int. J. Adv.
Manuf. Technol. 2015, 79(1–4), 513–518. DOI: 10.1007/s00170-0156840-9.
[43] Sahu, D. R.; Kumar, A.; Roy, B. K.; Mandal, A. Parametric
Investigation into Alumina Nanopowder Mixed EDM of Inconel 825
Alloy Using RSM. 2019; pp 175–184. doi:10.1007/978-981-13-64129_16
[44] Li, M.; Yang, Z.; Dong, H.; Zhou, Y.; Liu, Y. Machining
Performance of High Energy Die-Sinking Electrical Discharge
Machining on GH2132. Mater. Manuf. Process. 2020, 35(9),
1024–1031. DOI: 10.1080/10426914.2020.1758328.
[45] Das, S.; Paul, S.; Doloi, B. Feasibility Investigation of Neem Oil as
a Dielectric for Electrical Discharge Machining. Int. J. Adv. Manuf.
Technol. 2020, 106(3–4), 1179–1189. DOI: 10.1007/s00170-01904736-5.
[46] Mishra, B. P.; Routara, B. C. Evaluation of Technical
Feasibility and Environmental Impact of Calophyllum
Inophyllum (Polanga) Oil Based Bio-Dielectric Fluid for
Green EDM. Measurement. 2020, 159, 107744. DOI: 10.1016/
j.measurement.2020.107744.
[47] Mazarbhuiya, R. M.; Rahang, M. Reverse EDM Process for Pattern
Generation Using Powder Metallurgical Green Compact Tool.
Mater. Manuf. Process. 2020, 35(15), 1741–1748. DOI: 10.1080/
10426914.2020.1802036.
[48] Torres, A.; Luis, C. J.; Puertas, I. Analysis of the Influence of
EDM Parameters on Surface Finish, Material Removal Rate,
and Electrode Wear of an INCONEL 600 Alloy. Int. J. Adv.
Manuf. Technol. 2015, 80(1–4), 123–140. DOI: 10.1007/s00170015-6974-9.
[49] Kumar, A.; Kumar, S.; Mandal, A.; Rai Dixit, A. Investigation of
Powder Mixed EDM Process Parameters for Machining Inconel
Alloy Using Response Surface Methodology. Mater. Today Proc.
2018, 5(2), 6183–6188. DOI: 10.1016/j.matpr.2017.12.225.
[50] Roy, B. K.; Mandal, A. Surface Integrity Analysis of Nitinol-60
Shape Memory Alloy in WEDM. Mater. Manuf. Process. 2019, 34
(10), 1091–1102. DOI: 10.1080/10426914.2019.1628256.
[51] Castillo, E. D. Process Optimization a Statistical Approach; New
York: Springer Science, 2007.