experimental study of the effect of mineral oil- based sio
TRANSCRIPT
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 91
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
Experimental Study of the Effect of Mineral Oil-
Based SiO2 Nano-Lubricant on Surface Roughness
During Turning of AL6063 Alloy using Box-
Behnken Design
I.P. Okokpujie 1*, C.A. Bolu 1, O.S. Ohunakin 1, 3, S.O. Gbadegesin 1, I.O. Aladegbeye 1, E.T. Akinlabi 2
1Department of Mechanical Engineering, Covenant University, Ota, Ogun State, Nigeria 2Department of Mechanical Engineering Science, University of Johannesburg, Auckland Park Kingsway Campus,
Johannesburg, 2006, South Africa
3Senior Research Associate, Faculty of Engineering & the Built Environment, University of Johannesburg, South Africa.
Corresponding author: [email protected]
Abstract-- The machining of AL6063 alloy is a challenging
process because, when machined, it has adhesion problems,
which increases heat generation between the cutting tool and the
workpiece during machining. To achieve a minimum surface
roughness of the AL6063 alloy, the need to use an eco-friendly
lubricant with high pressure at the turning zone is essential.
Therefore, this study aid in carrying out experimental analysis
on the effect of mineral oil-based SiO2 nano-lubricant on
AL6063 alloy surface roughness during the turning operation.
The nano-lubricant was ultra-sonicated for five (5) hours to
properly homogenize the mineral oil and the SiO2 nanoparticles.
The mineral oil-based SiO2 nano-lubricant was engaged in the
turning operation, to study the effects and also compared the
performance with the dry, and mineral oil-lubricant (i.e., the
control based fluid) machining. This research applied Box-
Behnken experimental design for the turning operation. The
result shows that the mineral oil-based SiO2 nano-lubricant
reduces the surface roughness value with 17.14% and 9.57%
when compared with the dry and mineral oil-lubricant and the
mineral oil-lubricant reduces the surface roughness with 8.38%
when compared with the dry turning operation. The study
achieved the minimum surface roughness of 8.65 µm, 7.72 µm,
and 6.78µm for dry, mineral oil and mineral oil-based SiO2
nano-lubricant machining, at the optimized machining
parameter of spindle speed of 165 rev/min, depth of cut of 1.5
mm and feed rate of 0.5 mm/rev. Furthermore, the developed
models predicted the experimental result with 94.9%, 95.55%,
and 95.71%, respectively, which is workable and reasonable in
lathes machining. The finding from this study will assist
researchers and manufacturers in carrying out a turning
process on aluminum alloy with mineral oil-based SiO2 nano-
lubricant for greener machining.
IndexTerm-- Machining; Nano-lubricant; Surface roughness;
Aluminium alloy; Box-Behnken design; Optimization
1. INTRODUCTION
The machining procedure is a unique technique that helps to
transform solid raw material into a required part for an application with necessary exactness and excellent surface
quality. It comprises of lathes machining, milling, and
grinding machining. A portion of these procedures are
intricate because it represents a substantial level of the whole
volume expelled, and the production of mechanical parts
involves broad economic implications [1]. During the turning
operation, it generates a lot of heat, which has significant
effects on the workpiece and the cutting tool. Therefore, for
proper control of the high temperature during machining,
there is a need for using a technique to deliver the lubricant at the turning region. Conventional cooling, otherwise called
flood cooling, is one of the most established utilized systems
in the manufacturing industry [2].
The lubrication process plays a vital role in turning
operations, which helps to reduce the temperature and
prolonge the cutting tool life during operation. The utilization
of lubricant in machining offers an essential purpose mainly
to build efficiency and surface nature of the machined
workpiece [3-4]. This lubricant is promising because cutting
fluids enable the turning process at higher feed rates and high
spindle speed [5].
Implementing Lubricants with excellent cooling
characteristic is call cutting fluid, the lubricants assist in
protecting, diminishes surface severity, increase dimensional
accuracy and also reduce the rate of power consumption
during the turning process by the lathe machine. Besides,
cuttings fluids help to transport the extreme heat and chip
deposited at the turning zone during the cutting procedures
and also protect the cutting tool from frequently damaging [6-
10]. Conventional cutting fluids have made-up a great deal of
concern all over the world as regards the health of the public
and subsequently causing a high rate of failure of the workpiece and cutting tool during turning operation [11]. The
following limitations are:
Environmental contamination because of chemical
break-up/separation of the cutting liquid at a high
cutting temperature
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 92
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
Water contamination and soil sullying amid
transfer;
A prerequisite of additional floor space and new
frameworks for siphoning, stockpiling, filtration,
reusing, chilling, and so on.;
The cost of disposing of the cutting liquid is higher as environmental regulations are firmer.
Therefore, due to the limitations of the conventional cutting
fluid, researchers as introduce nano-lubricant as an
alternative.
Nano-lubricant, which is formed from nanoparticles and base
oil, has excellent thermal characteristics, free from corrosion,
and has unique tribological properties. Sharma et al. [12]
carried out an experimental study of alumina/graphene (GnP)
hybrid nano-lubricant and alumina nano-lubricant in lathe
turning operation of AISI 304 steel with different volumetric concentrations. The result shows that the hybrid nano-
lubricant reduces the friction coefficient and temperature
with 5.8% and 12.3% when compared with the alumina nan-
lubricant. The result also indicates that the addition of GnP to
the alumina improved the tribological properties of the water-
based alumina nano-lubricant. Jia et al. [13] performed a
study to find out the optimum concentration of MoS2
nanoparticles in mixed oil with a base castor oil, which was
utilized in the machining of a Nickel-based alloy. The
mixture of soybean and castor oil have optimum
performance. The nanoparticle, mixed with the based oil,
produces a thin film on the surface of the lubricant. These thin films contributed to the increase of the heat transfer capability
of the nanofluid. From the result, the optimum concentration
for improved cooling and lubrication was 8wt.%, after which
cooling and lubrication stopped being improved and slightly
reduced. However, there are a lot of ways to deliver nano-
lubricant in the machining operation, such as minimum
quantity lubrication (MQL), flood cooling, and high-pressure
lubricating process. This research focuses on the high-
pressure lubrication process.
High-pressure lubrication methods help deliver the lubricant at the turning zone between the cutting tool and the
workpiece with a specific pressure [14]. Under this cooling
method, precisely in the working region between the
workpiece and the cutting tool’s rake top, the coolant is
coordinated under high pressure. This active cooling and
lubrication process reduces tool wear when contrast to usual
cooling methods. This technique helps in reducing the
quantity of lubricant used and also increases the turning
efficiency of the lathe machine, mostly when machining
aluminum alloys [15]. Courbon et al. [16] carried out an
experimental study of high-pressure jet assistance turning
operation on Inconel 718 to surface finishing. The study applied response surface methodology with three turning
parameters such as nozzle angle, feed rate, and cutting speed.
The result from the experimental study shows that the
application of the high-pressure lubrication process assists in
reducing the surface roughness and the temperature at the
cutting region. However, the authors recommended that the
investigation of eco-friendly lubricant should be carried out
to avoid the failure of the workpiece during operation.
Courbon et al. [17] developed a numerical model using the
finite element method for the orthogonal turning operation to predict the experimental result and to analyze the
performance of the high-pressure jet on the turning process.
From the result, it shows that the high-pressure lubricating
system performance was able to reduce the tool rake face and
the chip formation, which also helps to reduce the surface
roughness of the Inconel 718 workpiece.
Machining of aluminum alloys needs an efficient system that
can deliver and optimize cutting lubricants with high pressure
to reduce the adhesion of the material during machining [18].
However, the manufacturer has already configured the
lubrication delivery systems to the lathes machines using flood cooling method and with specific types of cooling
pattern, which limits its use with other coolant/lubricants. In
machining, the cutting fluid applied varies according to the
material during the machining operation, machining of
AL6063 alloy needs excellent cutting fluid with high
tribological properties to enable the achievement of
minimum surface roughness during the turning process.
Aluminum alloys have profitable attributes (e.g., a high
strength-to-weight proportion and extraordinary corrosion
opposition), and this has prompted its extensive use in designing applications. Due to its excellent chemical,
mechanical, and thermal properties, the manufacturing
industry implements aluminum alloy in aerospace,
automobile, and structural form for human comfort and safety
in our day-to-day life activities. Okokpujie et al. [19]
performed an investigation of the impacts of spindle velocity,
axial cutting depth, radial cutting depth, and feed rate effects
on surface roughness during dry machining of aluminum
alloy 6061. The experimental design used was central
composite design, with 30 experimental runs for the end-
milling machining. At the end of the experiment, the authors
concluded that an increase in both feed rates and radial cutting depth translates to a significant increase in surface
roughness, respectively. The authors confirmed that an
increase in the spindle speed decrease in the surface
roughness while the axial cutting depth showed negligible
effects on the surface roughness.
According to Sasimurugan and Palanikumar [20], Okokpujie
et al. [21] and Eapen et al. [22], says that the implementation
of lubricant in turning operation increase the efficiency of the
turning process. Furthermore, studying the machining
parameters under the lubrication condition gives a better
understanding with excellent optimization of the parameters
when studied. [21] Carried out an experimental study of
surface roughness during machining of AL6061 alloy, and
developed a predictive model using central composite design
(CCD). The result shows that the CCD predicted the
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 93
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
experimental outcome with about 90%, and the most
influential machining parameters are spindle speed, followed
by feed rate. However, the authors recommend that more
experimental design methods should be studied under nano-
lubrication conditions when machining aluminum alloy.
Saravanakumar et al. [23] work on the optimization of the CNC machine’s cutting parameters with carbon nitride
inserts on aluminum 6063 alloy using the Taguchi Robust
design. The study used three-factor and three-level cutting
parameters, such as feed rate, spindle speed, and cutting
depth. The HANDYSURFE-35B tester was applied to
measure the surface roughness. This result showed that the
Taguchi technique was capable and could have achieved a
perfect outcome. The feed rate was the most prominent
parameter among the three controllable machining
parameters on the surface roughness value, and the study
obtained the minimal surface roughness of 7.1 (µm) at
a spindle speed of 1200 rpm, a feed rate of 0.15 mm/rev and cuttings depth of 0.5 mm. Therefore, a lot of studies as proven
the usefulness of lubricant in machining aluminum alloy for
better performance during its applications.
However, there are needs to carry out studies on eco-friendly lubricants to reduce or eliminate the occurrence of material
adhesion during machining aluminum alloys. Also, the
application of non-eco-friendly oil, such as flood lubrication
techniques, can affect the health of the machine operators and
causes environmental pollution when deposed. To address
this gap, this research aimed at studying the effect of the
synthesized mineral-based SiO2 nano-lubricant on surface
roughness during turning of AL6063 alloy using Box-
Behnken design for the sustainable manufacturing process.
The novelty of this work is that the study also develops a
mathematical model to predict the performance of the machining parameters in the three machining conditions
compared in this paper. These predictions will help the
manufacturing industry to be able to carry out machining
processing of aluminum alloys, why having in mind the
surface roughness of the workpiece to be achieved at the end
of the manufacturing process.
2. MATERIALS AND METHOD
The WARCO GH-1440A lathes machine in the department
of mechanical engineering, Covenant University, was used
for the experiment. Figure 1 shows the ultrasonic cleaner
machine used for synthesizing the nano-lubricant, and
Figures 3 and 4 present the experimental setup of the turning operations with a closer look at the cutting zone during one
of the machining process. The HPDLS delivered the mineral
oil-based SiO2 nano-lubricant with a constant pressure
between four (4) to six (6) bars. However, the machine can
provide lubricant up to 7 bars. The SRT-6210S surface tester
applied to measure the surface roughness after each turning
operation of the AL6063 alloy, as shown in Figure 4. The
authors measured the surface roughness in three different
machining surface of the AL6063 alloy, and the average of it
is the surface roughness (Ra) value for each turning with the
factors and their level used in this study. The manufacturer
extracted the mineral oil applied in this experiment from a biodegradable organic plant. The mineral oil is colorless,
odorless, has a density of 0.90 g/cm3, pH value of 5.76,
viscosity @ 40oC of 13.6 mm2/s.
The base fluid contains a high percentage of carbon atoms
with a chemical formula of CnH2n+2. Moreover, the 5g of
Silicon oxide (SiO2) was added to the base fluid. As an
additive in developing the nano-lubricant to improve the
thermal resistance of the lubricant. This study used SEM and
EDS machines to characterize the synthesized nano-lubricant to know the chemical composition, as shown in Figure 2,
before the application of the lubricants in the turning
operations. The dry machining of the AL6063 alloy was
machined first, and the authors measured the surface
roughness for each turning process. After the dry
machining, the authors also carried out the turning operation
using the mineral oil-lubricant (control) and mineral-based
SiO2 nano-lubricant to determine the effects of the lubricant
on the surface roughness of the AL6063 alloy (i.e., the
workpiece).
Fig. 1. The ultrasonic cleaning process used for synthesizing the mineral oil-based SiO2 nano-lubricant.
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 94
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
Fig. 2. The SEM and EDS characterization result of the mineral oil-based SiO2 nano-lubricant used for the turning operations.
Fig. 3. Experimental setup of the HPDLS and the Lathe machine.
Fig. 4. Cutting zone during the experiment and the surface roughness tester in use.
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 95
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
2.1 The Workpiece Material and Cutting Tool Used
for the Turning Operation
The dimension of the workpiece used are 39.2 mm diameter
by 246 mm length of the cylindrical bar of AL6063 alloy, for
ratio 1:6, to enable excellent rigidity and reduction of
vibration occurrence during the turning of the AL6063
alloy. Tables 1 and 2 presents the physical and chemical
properties of the AL6063 alloy, respectively.
Table I
Physical Properties of Aluminum 6063 Alloy [24]
Property Value
Melting Point 655 ℃
Density 2.70 g/cm3
Modulus of Elasticity 69.5 GPa
Thermal Expansion 23.5 × 10−6/K
Thermal Conductivity 201 W m. K⁄
Electrical Resistivity 0.033 × 10−6 Ω. m
Table II
Chemical Properties of Aluminium 6063 Alloy
Element Cr Ti Zn Si Cu Fe Mn Mg Other Aluminum
% Present 0.10 0.10 0.10 0.60 0.10 0.35 0.10 0.90 0.16 Balance
The M2 HSS cutting tool applied for this experiment has a dimension of 14×14×200 mm, used for the lathe machine
operations. Figure 5 shows the orthographic projection of the facing tool with its relevant angles.
Fig. 5. Diagram of the HSS Facing Tool.
2.2 The Experimental Design
The experimental design is a statistical method that allows a
researcher to perform practical tests information effectively
and derive significant findings from the study. Scientific
research aims are to help to demonstrate the statistical impact
of the independent variable on the dependent variable
(output/response) of the interest exerted by a specific factor
(input parameter) [19-21]. In particular, DOE aims to define the optimal configurations for the various variables affecting
the manufacturing method. The main reason researchers
should use statistical conceived tests is to achieve peak data
from minimum funds. This study applied Box-Behnken
experimental design to develop the innovative template, and
the total number of trial runs in this study is 17 runs;
according to the Box-Behnken plan of three (3) factors, three
levels. The software used for this experiment is a Design
expert, and the process parameters used are spindle speed,
feed rate, cutting depth, with their factors at three levels.
As shown in Table 3, and Figure 6 also shows the design flow chart used for the optimization process. This research carried
out the turning operation with a constant length of cut off 20
mm while varying all other machining factors, i.e., spindle
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 96
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
speed, feed rate, and cutting depth to determine the effects of
the three factors on the surface roughness of AL6063 alloy.
Table III
Selected Process Factors used and their Factors at Three Levels
Factors Unit -1 0 +1
Spindle speed rev/min 90 140 165
Feed rate mm/rev 0.5 1 1.5
Depth of cut mm 1 1.5 2
Define independent input variables
and desired response
Adopt an experimental design plan
for the surface roughness
experimental analysis
Perform regression analysis with the
Box-Behnken design
ANOVA test to check the significant
parameters and the fitness of the
models
Optimize and conduct confirmation test of the experiment
Validate the developed models for the prediction of the surface roughness of
the AL6063 alloy
Start
Is the model fit
and
significant for the prediction?
END
No
Yes
Fig. 6. the experimental design flow chart for the analysis of the turning process of AL6063 alloy.
2.3 Developing the Mathematical Expression
The connection between surface roughness and the input parameters given in this study is shown in equation (1) [25].
Ra = φ(N,f,d) (1)
With the transformation power process, equation (1) becomes equation (2).
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 97
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
Ra = φ(Nx,f y,dz) (2)
Where, Ra = the average surface roughness, φ = response function, N = Spindle speed, f = Feed rate, d = cutting depth, and x,
y, z are the power transformation. However, equation (2) becomes equation (3)
Log Ra= Log φ + x. logN + y.logf + z.logd (3)
Therefore, introducing the parameters and coefficient show the following expression.
Y=log Ra, β0 = log φ, X1 = log N , X2 = logf, X3 = log d, x = β1, y = β2, z = β3
Therefore, the overall shape of a quadratic polynomial, which provides an association between the responding surface y and
system variable x, is shown by the equation (4) [26]:
Y = βo+ ∑ β
ixi
k
i=1
+ ∑ βiixi
2
k
i=1
+ ∑ βijxij
i<j
+ε (4)
Where, y = is the response (that is surface roughness), xi = spindle speed, feed rate and cutting depth, βo = constant, βi = linear
term coefficient, βii = quadratic term coefficient, βij = interaction term coefficient, ε = random error. After obtaining the
experimental results for the three turning conditions, the study used the percentage reduction method to evaluate the
performance influence of these, as shown in equation (5).
% = [Radry − Ramineral oil
Radry
] ∗ 100 (5)
Where % is the percentage reduction, Ra is the various surface roughness from the three turning conditions.
2.4 Optimization Procedure
This research used the desirability function approach under the numerical optimization method to carry out the optimization
analysis of the turning parameters under the various turning conditions. The function desirability ranges from zero to one,
which can be explained as outside the limits to the set goal. This process enables the numerical optimization to discover a point
that assists in maximizing the desirability of the multi-response parameters. However, the target most of the time is affected by
the regulating of the importance or weight. Equation (6) show the mathematical expression used to determine the desirability function in this study [32].
D = (d1 . d2. … . . dn)1n = (∏ di
n
i=1
)
1n
(6)
Where n = number of responses measured during the experiment, 𝑑𝑖 = the responses and D= the desirability function.
3. RESULTS AND DISCUSSION
This study used the developed HPDLS to deliver the nano-lubricant at the machining region during the turning operation. The
experimental design helps to vary the three turning parameters according to the Box-Behnken plan under three conditions dry,
mineral oil-lubricant, and mineral-based SiO2 nano-lubricant. The surface roughness is the response parameter for the study. After each turning process, the Mitutoyo surface tester measures the value of the surface roughness, and Table 4 presents the
results for the three turning conditions.
Table IV
Results of Experiments for Surface Roughness in Dry Machining, Mineral Oil, and Mineral-based SiO2 nano-Lubricant
Run
Machining Parameters Surface Roughness (Ra) % Reduction for the three machining environment
Spindle
speed
(rev/min)
Feed rate
(mm/min)
Depth
of cut
(mm)
Dry Mineral
Oil
Mineral
oil-based
SiO2 Nano-
lubricant
Mineral
Oil and
Dry
SiO2 and
Mineral
oil
SiO2
and
Dry
1 165 1 1 10.36 9.36 8.35 9.65 10.79 19.4
2 140 0.5 2 12.5 11.27 10.03 9.84 11 19.76
3 165 1 2 11.08 10.07 9.05 9.12 10.13 18.32
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 98
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
Run
Machining Parameters Surface Roughness (Ra) % Reduction for the three
machining environment
Spindle
speed
(rev/min)
Feed rate
(mm/min)
Depth
of cut
(mm)
Dry Mineral
Oil
Mineral
oil-based
SiO2 Nano-lubricant
Mineral
Oil and
Dry
SiO2 and
Mineral
oil
SiO2
and
Dry
4 90 0.5 1.5 14.56 13.29 12.01 8.72 9.63 17.51
5 90 1 1 14.96 13.76 12.56 8.02 8.72 16.04
6 165 0.5 1.5 8.65 7.72 6.78 10.75 12.18 21.61
7 140 1 1.5 12.68 11.51 10.33 9.23 10.25 18.53
8 140 1 1.5 12.68 11.55 10.33 8.92 10.56 18.53
9 90 1.5 1.5 15.55 14.72 13.89 5.34 5.64 10.67
10 140 0.5 1 11.55 10.49 9.42 9.17 10.2 18.44
11 140 1 1.5 11.15 10.1 9.04 9.42 10.49 18.92
12 140 1 1.5 11.16 10.11 9.05 9.41 10.48 18.91
13 140 1 1.5 11.15 10.2 9.04 8.52 11.37 18.92
14 140 1.5 1 14.67 13.7 12.33 6.61 10 15.95
15 140 1.5 2 14.87 13.84 12.81 6.92 7.44 13.85
16 90 1 2 15.3 14.38 13.45 6.01 6.47 12.09
17 165 1.5 1.5 13.67 12.73 11.78 6.87 7.46 13.82
Fig. 7. The experiment results of the surface roughness for dry, mineral oil, and mineral oil-based SiO2 nano-lubricant machining conditions.
Figure 7 shows the experimental result of the dry, mineral oil-
lubricant, and mineral oil-based SiO2 nano-lubricant during
the turning process of AL6063 alloy. The result indicates that
the mineral oil-based SiO2 nano-lubricant could reduce the
surface roughness with 17.13% when compared with the dry
turning operations, 9.57% when compared with the mineral
oil-lubricant which served as the control. Moreover, the mineral oil-lubricant reduces the surface roughness with
8.38% when also compared with the dry turning operation, as
shown in Table 4. From the experimental analysis, the
authors observed that the AL6063 alloy has adhesion
problems during the dry turning process. However, this
adhesion of the material attaching the cutting tool reduced
during the application of the mineral oil-lubricant and
mineral oil-based SiO2 nano-lubricant, due to the
improvement of the mineral oil-lubricant thermal resistance and tribological property, as depicted in Figure 2. The SEM
and EDS results of the mineral oil-based SiO2 nano-lubricant
0
2
4
6
8
10
12
14
16
18
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Surf
ace
roughnes
s (R
a)
Experimental Runs
Dry Mineral Oil Mineral oil-based SiO2 Nano-lubricant
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 99
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
possess a reasonable amount of silicon (Si) of 45.2%, oxygen
of 25.1%, and carbon of 15.4%. This percentage of the three
key elements gives the reason while the cutting fluid assists
as a coolant and lubricant. Silicon is an anti-wear resistant
when added as an additive in cutting fluid.
The silicon and the carbon elements present in the nano-
lubricant help in the turning operation by depositing the nano-
thin film on the surface of the AL6063 alloy. Which assists
to protects the surface and increases the hardness of the
material surface during the turning operation. These results
show that the high presence of silicon in the control lubricant
increases the slipperiness of the mineral oil at the turning
region between the cutting tool and the workpiece. Also, the
addition of the silicon nanoparticle in the mineral oil reduces
the friction and chips discontinuity on the surface of the
workpiece and cutting tool, which led to a reduction of
unwanted vibration, as shown in Figure 8(a-b). This result is
in line with [13] and [14]. The presents of oxygen and carbon also assist in term of reducing the temperature in the cutting
region. During the turning operation, the carbon element
increases the surface hardness of the workpiece, which help
in reducing the impact of the cutting tool on the machined
surface. In this study, the authors applied the experimental
result to develop the predictive model using the Box-
Behnken design of the experiment.
Fig. 8. The mechanism of the turning operation with the mineral oil-based SiO2 nano-lubricant
(a) The heat generated region and the heat reduction, (b) Thin firm, and rolling effect.
3.1 Explanation of the Model and the Analysis of Variance for the Turning Operation The study of the analysis of variance (ANOVA) for both dry, mineral oil-lubricant, and mineral-based SiO2 nano-lubricant was
to determine the most significant process parameter. Moreover, to also determine the effects of the two turning operation
conditions on surface roughness of the machined face of AL6063 alloy. Tables 5 to 6 present the results of the statistical
analysis of the process parameters on the surface roughness result for the three turning conditions.
Table V
ANOVA Results for Dry Turning Operation
Source Sum of Squares df Mean Square F-value p-value
Model 62.68 9 6.96 14.54 0.0010 Significant
A-Spindle speed 34.49 1 34.49 72.00 < 0.0001
B-Feed rate 12.49 1 12.49 26.07 0.0014
C-Depth of cut 0.5164 1 0.5164 1.08 0.3337
AB 3.68 1 3.68 7.69 0.0276
AC 0.0370 1 0.0370 0.0771 0.7892
BC 0.1406 1 0.1406 0.2936 0.6047
A² 0.2598 1 0.2598 0.5425 0.4854 B² 3.48 1 3.48 7.26 0.0309
C² 2.22 1 2.22 4.64 0.0682
Residual 3.35 7 0.4790
a b
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 100
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
Source Sum of Squares df Mean Square F-value p-value
Lack of Fit 0.5439 3 0.1813 0.2582 0.8526 not significant
Pure Error 2.81 4 0.7023
Cor Total 66.03 16
Table VI
ANOVA Results for Mineral Oil-lubricant Turning Operation
Source Sum of Squares df Mean Square F-value p-value
Model 62.80 9 6.98 16.66 0.0006 Significant
A-Spindle speed 33.09 1 33.09 79.01 < 0.0001
B-Feed rate 14.70 1 14.70 35.09 0.0006
C-Depth of cut 0.5975 1 0.5975 1.43 0.2712
AB 2.85 1 2.85 6.81 0.0349
AC 0.0001 1 0.0001 0.0003 0.9875
BC 0.1024 1 0.1024 0.2445 0.6361
A² 0.1342 1 0.1342 0.3204 0.5890
B² 3.62 1 3.62 8.64 0.0218
C² 2.09 1 2.09 4.99 0.0607
Residual 2.93 7 0.4188
Lack of Fit 0.5950 3 0.1983 0.3395 0.7992 not significant
Pure Error 2.34 4 0.5841
Cor Total 65.73 16
Table VII
ANOVA Results for Mineral-based SiO2 Nano-lubricant Turning Operation
Source Sum of Squares df Mean Square F-value p-value
Model 62.46 9 6.94 17.29 0.0005 significant
A-Spindle speed 31.80 1 31.80 79.22 < 0.0001
B-Feed rate 16.11 1 16.11 40.13 0.0004
C-Depth of cut 0.9087 1 0.9087 2.26 0.1761
AB 2.02 1 2.02 5.04 0.0597
AC 0.0177 1 0.0177 0.0441 0.8397
BC 0.0042 1 0.0042 0.0105 0.9212
A² 0.0042 1 0.0042 0.0105 0.9212
B² 3.62 1 3.62 9.01 0.0199
C² 1.86 1 1.86 4.63 0.0684
Residual 2.81 7 0.4014
Lack of Fit 0.8131 3 0.2710 0.5429 0.6784 not
significant
Pure Error 2.00 4 0.4992
Cor Total 65.27 16
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 101
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
In this study, 95 percent was the selected confidence level
used. From Table 5, the F-value of 14.54 suggests that the
model is adequately significant. The p-values that are lower
than 0.05 are a sign that the terms of the model are vital. In
this wise, A, B, AB, B2, and C2 are very significant. Also, p-
values above 0.1 show that the terms of the model are not significant; that goes to show that the F-value of 0.2582 for
the “Lack of Fit” indicates that the “Lack of Fit” is
insignificant. Tables 6 and 7 show the F-value of 16.66 and
17.29, which also suggests that the model is adequately
significant. Also, their factors A, B, AB, B2, and C2 are
significant because their p-values are lower than 0.05.
However, the “Lack of Fit” is insignificant since both have
its F-value of 0.3395 and 0.5429, respectively, which is
higher than 0.1. The insignificant “Lack of Fit” is preferable
to enable the model to give a substantial accurate prediction.
Figure 9a and 9b also shows a standard residual plot for the
dry, mineral oil-lubricant, and mineral oil-based nano-
lubricant turning process. From the residual plots, it implies that the factors variables used for the experimental design
were distributed uniformly as the residuals fall between the
straight line. From this illustration in Figure 9, it also depicted
that the two models developed in the study are significant.
Also, the ANOVA confirms this in Tables 5 to 7 that the
models are significant and can predict the experimental
results.
Fig. 9. Normal residuals plots (a) dry machining (b) mineral oil-lubricant (c) mineral oil-based SiO2 nano-lubricant.
In Table 8a, the R2 was 0.9492, which shows that the
independent variables, spindle speed, feed rate, and depth of
cut, could define 94.92% of the observed irregularity of
surface roughness. While in Table 8b-8c, the R2 was 0.9554,
and 0.9571 shows that 95.54% and 95.71% of the
independent variables could identify the observed
variableness of the surface roughness. Table 78a to 8c also
shows the Adequate precision of 12.653, 13.798, and 14.198,
(a)
)
(c)
)
(b)
)
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 102
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
which shows that the coefficient of correlation for dry
machining between the values of the surface roughness
gotten from the experiments and the predicted values in line
with the regression model was standard. While from Table 8b
and 8c, the Adequate Precision also shows that the correlation
coefficient for mineral oil-lubricant and mineral oil-based SiO2 nano-lubricant was also reasonable. However, the
equation in terms of actual factors predicted the response for
a given level of each parameter. Where the specific level of
the turning operation is in the original units for each setting
in the experimental study; therefore, this equation cannot
determine the relative impact of each factor. Because the
coefficients accommodate the parameter units, and the location of the intercept is not at center design space.
Table 8a
Fit statistics for dry turning operation
Table 8b
Fit statistics for mineral oil-lubricant turning operation
Std. Dev. 0.6471 R² 0.9554
Mean 11.69 Adjusted R² 0.8981
C.V. % 5.53 Predicted R² 0.7920
Adequate Precision 13.7983
Table 8c
Fit Statistics for Mineral-based SiO2 nano-lubricant
Std. Dev. 0.6336 R² 0.9571
Mean 10.60 Adjusted R² 0.9016
C.V. % 5.98 Predicted R² 0.7441
Adequate Precision 14.1980
The developed mathematical models for dry turning, mineral oil-lubricant, and the mineral oil-based SiO2 nano-lubricant are
shown in equations (7) to (9), respectively.
Radry= +29.230 − 0.061N − 9.934f − 8.083d + 0.049Nf + 0.005Nd − 0.750fd − 0.0002N2 + 3.636 f 2
+ 2.906 d2 (7)
RaMineral oil= +27.025 − 0.0610N − 9.261f − 7.285d + 0.044Nf + 0.0003Nd − 0.640fd − 0.0002N2 + 3.707f 2
+ 2.817d2 (8)
RaSiO2= +26.169 − 0.078N − 9.015f − 6.712d + 0.037Nf − 0.003Nd − 0.130fd − 0.00003N2 + 3.708f 2
+ 2.658d2 (9)
Where, Ra = surface roughness, N = Spindle speed, f = feed rate, and d = depth of cut.
Std. Dev. 0.6921 R² 0.9492
Mean 12.74 Adjusted R² 0.8839
C.V. % 5.43 Predicted R² 0.7961
Adequate Precision 12.6526
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 103
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
Figure 10 (a-c) shows the model prediction vs. the value from the experiment for Dry, mineral oil-lubricant, and SiO2 nano-
lubricant. This result indicates that the model could predict the surface roughness for Dry turning with 94.9%, for mineral oil
with 95.54%, and 95.71% for mineral oil-based SiO2 nano-lubricant for the turning operations of the AL6063 alloy.
Fig. 10. The predicted value vs. experimental result value for (a) Dry (b) Mineral oil-lubricant (c) Mineral oil-based SiO2 nano-lubricant under HPL turning
operation.
3.2 The Influence of the Dry, Mineral Oil-lubricant
and Mineral Oil-Based SiO2 Nano-lubricants on
the Surface Roughness of AL6063 Alloy During the
Turning Operations
This section presents the experimental result of the various
turning conditions and the effects of the cutting parameters
on the surface roughness of AL6063 alloy during the turning
operations. In this analysis, two parameters at a time were
plotted against the response parameter, i.e., surface
roughness. Therefore, the contour plot is employed to
illustrate the influence of these three turning parameters
under the various machining conditions, which will assist in
given better understanding with the surface roughness values
showing on the graph with different degrees of color
variations. The two-dimensional contour plots graphically represented the relationship between the independent and
dependent variables in Figure 11 to 13. The result shows as
the feed rate increases, the surface roughness also increases,
as shown with the color variations from blue to green, green
to yellow, and from yellow to red in the figures respectively,
which explained the processes of the level of the influence on
the surface roughness. This result corresponds with the result
obtained from Duc and Chien [27] and Gupta et al. [28].
0
2
4
6
8
10
12
14
16
18
1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7
Suef
ace
Rougnhnes
s (R
a) D
ry M
achin
ing
Experimental Runs
Actual Value Predicted Valuea
0
2
4
6
8
10
12
14
16
1 2 3 4 5 6 7 8 9 1 0 1 11 21 3 1 41 51 61 7SUR
FAC
E R
OU
GH
NES
S (R
A) M
INER
AL
OIL
EXPERIMENTAL RUNS
Actual Value Predicted Value
0
2
4
6
8
10
12
14
16
1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7
Surf
ace
Ro
ugh
nes
s (R
a) S
iO2+m
inie
ral
Oil
Experimental Runs
Actual Value Predicted Valuec
b
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 104
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
However, as the spindle speed increased, the surface
roughness reduced, and the depth of cut had little or no effect
on the surface roughness. The study achieved a minimal
surface roughness value of 8.65 µm, 7.72 µm, and 6.68µm
for dry, mineral oil, and mineral oil-based SiO2 nano-lubricant at a spindle speed of 165 (rev/min.), a feed rate of
(0.5mm/rev.) and depth of cut of 1.5 (mm). The minimum
surface roughness value of 6.68µm during the application of
the mineral oil-based SiO2 nano-lubricant is due to the high
thermal resistance and the ability of the silicon in the mineral
oil to reduce the friction. Also, the implementation of the
high-pressure lubricating system assists in eliminating chips
discontinuity, by washing away the chips from the turning zone; this result is supported by [29 - 36].
Fig. 11. The Influence of feed rate and spindle speed on surface roughness under (a) Dry (b) Mineral oil-lubricant and (c) Mineral oil-based SiO2
nano-lubricant.
a b
c
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 105
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
Fig. 12. The Influence of depth of cut and spindle speed on surface roughness under (a) Dry (b) Mineral oil-lubricant (c) Mineral oil-based SiO2 nano-
lubricant.
a
c
b
a
b
c
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 106
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
Fig. 12. The Influence of cutting depth and feed rate on surface roughness under (a) Dry (b) Mineral oil-lubricant (c) Mineral oil-based SiO2 nano-lubricant.
3.3 The Optimisation of the Turning Parameters using
Desirability Approach The desirability optimization approach in the turning process
is to obtain the optimum value (i.e., the minimum surface
roughness value) for the AL6063 alloy. In this research, the
cutting parameter settings are different to enable the
achievement of the minimum surface roughness. The cutting
speed kept at maximum, the feed rate fixed at minimum, and
the depth of cut set in range. This research used design expert
11.0.3 software to analyzed and optimized the turning
parameters after the experiment. As soon as the desirability values are determined, the condition for the predicted output
is obtained [37-41]. Figure 13 shows the desirability plot for
the dry, mineral oil-lubricant, and mineral oil-based SiO2
nano-lubricant turning conditions. From the analysis, the
total desirability for the multi-response parameters is 0.991,
which is approximately 1.
Fig. 13. Bar plot for the desirability value for the cutting parameters and the surface roughness Dry, mineral oil, and mineral oil-based SiO2 nano-
lubricant turning environment.
However, the study achieved the optimisation by the ramps
values at a minimum surface roughness of 8.72 µm, 7.80 µm,
and 6.93 µm for dry, mineral oil, and mineral oil-based SiO2
nano-lubricant when compared to the experimental value
with a slight difference. These differences occurred due to the
effects of desirability weight during the simulation of
parameter optimization, as shown in the ramp plot in Figure
14. The desirability of 0.991 for the multi-response parameter
c
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 107
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
indicates that the prediction of the experimental result is
accurate and feasible for implementation in the
manufacturing industries.
Fig. 14. The ramps plot of the dry, mineral oil-lubricant and mineral oil-based SiO2 nano-lubricant turning conditions.
Therefore, the application of lubricant in turning operations as patterned surface roughness is very significant. From the
experimental result to the predicted outcome, the mineral oil-
based SiO2 nano-lubricant performs excellent well in
reducing the surface roughness of the AL6063 alloy.
4. CONCLUSION This study carried out a performance analysis of the mineral
oil-based SiO2 nano-lubricant delivered with the HPDLS to
determine its effects on the machined surface of the AL6063
alloy. Furthermore, compared the result with the dry and
mineral oil (control lubricant) using Box-Behnken design
with three factors, three levels of the experiment. This study used the SRT-6210S surface tester to determine the surface
roughness values after each operation and applied the
ANOVA to ensure that the experimental data was valid and
adequate. Moreover, the authors developed the mathematical
models to predict the performance of the experimental result
with the cutting parameters at optimized levels. This research
has the following conclusions:
The minimum surface roughness of 8.65 µm, 7.72 µm
and 6.78 µm for dry, mineral oil-lubricant, and
mineral oil-based SiO2 nano-lubricant, respectively,
was achieved. At the optimal machining parameters, of spindle speed of 165 rev/min., depth of cut of 1.5
mm, and feed rate of 0.5 mm/rev, for the experiments.
While the numerical optimisation from the
applied Box-Behnken experimental design minimum
surface roughness of 8.72 µm, 7.80 µm, and 6.93 µm
for dry, mineral oil, and mineral oil-based SiO2 nano-
lubricant at a spindle speed of 165 rev/min., a feed rate
of 0.5 mm/rev., and depth of cut of 1.4 mm.
Mineral oil-based SiO2 nano-lubricant delivered by
the HPDLS reduces the surface roughness with
17.13% and 9.57% when compared with the dry and
mineral oil-lubricant at the turning process, why the mineral oil-lubricant reduces the surface roughness
with 8.38 µm when compared with the dry turning
operations.
It was discovered that silicon nanoparticles added in
the mineral oil improves the thermal resistance of the
based fluid and provide minimum surface roughness.
The developed model could predict the experimental
result for the dry turning operation with 94.9%,
mineral oil-lubricant with 95.55%, and the mineral
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 108
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
oil-based SiO2 nano-lubricant with 95.71%, which is
suitable in machining in the manufacturing industry.
Authors Contributions
Imhade P. Okokpujie: Conceptualization, Formal analysis,
Methodology, and Supervised the research. Christian A. Bolu: Software and Supervision. Olayinka S. Ohunakin:
Funding Acquisition. Stephanie O. Gbadegesin and
Iyanuoluwa O. Aladegbeye: Investigation and Writing-
original draft. Esther T. Akinlabi: Writing-review and
editing.
Conflict of Interest
All the authors in the article declare no conflict of interest,
competing for financial benefit or personal relationships in
this paper.
Acknowledgment The authors sincerely appreciate the Covenant University
Management for the financial support for the publication of
this article.
REFERENCES [1] Dipeshkumar B. T. Machining of difficult-to-cut Super-alloys: A
Review. International Journal of Advanced Engineering and
Research Development. 2017,4(10), 2348-4470.
[2] Okokpujie IP, Ajayi OO, Afolalu SA, Abioye AA, Salawu EY, Udo
M. Modeling and optimization of surface roughness in end milling
of aluminum using least square approximation method and response
surface methodology. International Journal of Mechanical
Engineering and Technology (IJMET). 2018;9(1):587-600.
[3] Adler DP, Hii WS, Michalek DJ, Sutherland JW. Examining the
role of cutting fluids in machining and efforts to address associated
environmental/health concerns. Machining Science and technology.
2006 Jun 1;10(1):23-58.
[4] Cortes V, Ortega JA. Evaluating the Rheological and Tribological
Behaviors of Coconut Oil Modified with Nanoparticles as Lubricant
Additives. Lubricants. 2019 Sep;7(9):76.
[5] Okokpujie IP, Okonkwo UC. Effects of cutting parameters on
surface roughness during end milling of aluminum under minimum
quantity lubrication (MQL). International Journal of Science and
Research. 2015 May 31;4(5):2937-2942.
[6] Qu D, Zhang P, Xue J, Fan Y, Chen Z, Wang B. Experimental study
on the effects of coolants on surface quality and mechanical
properties of the micro-milled thin-walled alloy. Materials. 2018
Sep;11(9):1497.
[7] Rubio E, Villeta M, Valencia J, Sáenz de Pipaón J. Experimental
study for improving the repair of magnesium–aluminum hybrid
parts by turning processes. Metals. 2018 Jan 16;8(1):59.
[8] Okonkwo Ugochukwu C, Nwoke Obinna N, Okokpujie Imhade P.
Comparative Analysis of Chatter Vibration Frequency in CNC
Turning of AISI 4340 Alloy Steel with Different Boundary
Conditions. Journal of Covenant Engineering Technology (CJET).
Vol. 2018 Mar;1(1).
[9] Aggarwal A, Singh H, Kumar P, Singh M. Optimization of multiple
quality characteristics for CNC turning under cryogenic cutting
environment using desirability function. Journal of materials
processing technology. 2008 Aug 26;205(1-3):42-50.
[10] Morley C. High pressure grinding rolls: a technology review.
Advances in comminution. 2006:15-39.
[11] Anton S, Andreas S, Friedrich B. Heat dissipation in turning
operations are utilizing internal cooling. Procedia Engineering.
2015 Jan 1; 100:1116-1123.
[12] Sharma AK, Tiwari AK, Dixit AR, Singh RK, Singh M. Novel use
of alumina/graphene hybrid nanoparticle additives for improved
tribological properties of lubricant in turning operation. Tribology
International. 2018 Mar 1; 119:99-111.
[13] Jia D, Li C, Zhang Y, Yang M, Wang Y, Guo S, Cao H. Specific
energy and surface roughness of minimum quantity lubrication
grinding Ni-based alloy with mixed vegetable oil-based nanofluids.
Precision Engineering. 2017 Oct 1; 50:248-62.
[14] Debnath S, Reddy MM, Yi QS. Environmental friendly cutting
fluids and cooling techniques in machining: a review. Journal of
cleaner production. 2014 Nov 15; 83:33-47.
[15] Okokpujie I, Okonkwo U, Okwudibe C. Cutting parameters effects
on surface roughness during end milling of aluminum 6061 alloys
under dry machining operation. International Journal of Science and
Research. 2015 Jul 31;4(7):2030-2036.
[16] Courbon C, Kramar D, Krajnik P, Pusavec F, Rech J, Kopac J.
Investigation of machining performance in high-pressure jet
assisted turning of Inconel 718: an experimental study. International
Journal of Machine Tools and Manufacture. 2009 Nov
1;49(14):1114-25.
[17] Courbon C, Sajn V, Kramar D, Rech J, Kosel F, Kopac J.
Investigation of machining performance in high-pressure jet
assisted turning of Inconel 718: A numerical model. Journal of
Materials Processing Technology. 2011 Nov 1;211(11):1834-51.
[18] Neugebauer R, Drossel W, Wertheim R, Hochmuth C, Dix M.
Resource, and energy efficiency in machining using high-
performance and hybrid processes. Procedia CIRP. 2012 Jan 1;1:3-
16.
[19] Okokpujie IP, Ohunakin OS, Adelekan DS, Bolu CA, Gill J, Atiba
OE, Aghedo OA. Experimental Investigation of Nano-Lubricants
Effects on Temperature Distribution of Mild Steel Machining.
Procedia Manufacturing. 2019 Jan 1; 35:1061-6.
[20] Sasimurugan T, Palanikumar K. Analysis of the machining
characteristics on surface roughness of a hybrid aluminum metal
matrix composite (Al6061-SiC-Al2O3). Journal of Minerals and
Materials Characterization and Engineering. 2011 Nov
1;10(13):1213.
[21] Eapen J, Murugappan S, Arul S. A Study on Chip Morphology of
Aluminum Alloy 6063 during Turning under Pre Cooled Cryogenic
and Dry Environments. Materials Today: Proceedings. 2017 Jan
1;4(8):7686-93.
[22] Saravanakumar A, Karthikeyan SC, Dhamotharan B. Optimization
of CNC Turning Parameters on Aluminum Alloy 6063 using
TaguchiRobust Design. Materials Today: Proceedings. 2018 Jan
1;5(2):8290-8.
[23] Hair Jr JF, Wolfinbarger M, Money AH, Samouel P, Page MJ.
Essentials of business research methods. Routledge; 2015 Mar 4.
[24] Okokpujie IP, Bolu CA, Ohunakin OS, Akinlabi ET, Adelekan DS.
A Review of Recent Application of Machining Techniques, based
on the Phenomena of CNC Machining Operations. Procedia
Manufacturing. 2019 Jan 1; 35:1054-60.
[25] Ogundimu O, Lawal SA, Okokpujie IP. Experimental study and
Analysis of Variance of Material Removal Rate in High Speed
Turning of AISI 304L Alloy Steel. In IOP Conference Series:
Materials Science and Engineering 2018 Sep (Vol. 413, No. 1, p.
012030). IOP Publishing.
[26] Okokpujie IP, Ikumapayi OM, Okonkwo UC, Salawu EY, Afolalu
SA, Dirisu JO, Nwoke ON, Ajayi OO. Experimental and
mathematical modeling for prediction of tool wear on the
machining of aluminum 6061 alloys by high-speed steel tools. Open
Engineering. 2017 Jan 1;7(1):461-9.
[27] Okokpujie IP, Ohunakin OS, Bolu CA, Okokpujie KO.
Experimental data-set for prediction of tool wear during turning of
Al-1061 alloy by high-speed steel cutting tools. Data in brief. 2018
Jun 1; 18:1196-203.
[28] Duc TM, Chien TQ. Performance Evaluation of MQL Parameters
Using Al2O3 and MoS2 Nanofluids in Hard Turning 90CrSi Steel.
Lubricants. 2019 May;7(5):40.
[29] Gupta MK, Jamil M, Wang X, Song Q, Liu Z, Mia M, Hegab H,
Khan AM, Collado AG, Pruncu CI, Imran GM. Performance
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 109
200303-8181-IJMME-IJENS © June 2020 IJENS I J E N S
Evaluation of Vegetable Oil-Based Nano-Cutting Fluids in
Environmentally Friendly Machining of Inconel-800 Alloy.
Materials. 2019 Jan;12(17):2792.
[30] Gupta MK, Sood PK. Surface roughness measurements in NFMQL
assisted turning of titanium alloys: An optimization approach.
Friction. 2017 Jun 1;5(2):155-70.
[31] Świercz R, Oniszczuk-Świercz D, Chmielewski T. Multi-Response
Optimization of Electrical Discharge Machining Using the
Desirability Function. Micromachines. 2019 Jan;10(1):72.
[32] Okokpujie, I.P., Bolu, C.A. & Ohunakin, O.S. Comparative
performance evaluation of TiO2, and MWCNTs nano-lubricant
effects on surface roughness of AA8112 alloy during end-milling
machining for sustainable manufacturing process. Int J Adv Manuf
Technol 108, 1473–1497 (2020). https://doi.org/10.1007/s00170-
020-05397-5
[33] Okokpujie KO, Odusami M, Okokpujie IP, Abayomi-Alli O. A
model for automatic control of home appliances using DTMF
technique. International Journal of Scientific & Engineering
Research. 2017 Jan 26;8(1):266-72.
[34] Ohunakin OS, Adelekan DS, Gill J, Atayero AA, Atiba OE,
Okokpujie IP, Abam FI. Performance of a hydrocarbon driven
domestic refrigerator based on varying concentration of SiO2 nano-
lubricant. International Journal of Refrigeration. 2018 Oct 1;94:59-
70.
[35] Yıldırım ÇV, Kıvak T, Sarıkaya M, Şirin Ş. Evaluation of tool wear,
surface roughness/topography, and chip morphology when
machining of Ni-based alloy 625 under MQL, cryogenic cooling,
and CryoMQL. Journal of Materials Research and Technology.
Volume 9, Issue 2, March–April 2020, Pages 2079-2092.
[36] Okonkwo UC, Okokpujie IP, Sinebe JE, Ezugwu CA. Comparative
analysis of aluminium surface roughness in end-milling under dry
and minimum quantity lubrication (MQL) conditions.
Manufacturing Review. 2015 Dec 30;2(30):1-11.
[37] Onoroh F, Ogbonnaya M, Echeta CB. Experimental Investigation
of Cutting Parameters on a Turning Tool Flank Wear (Industrial and
Production Engineering). Covenant Journal of Engineering
Technology (Special Edition). 2018 Mar 5;1(1).
[38] Stat-Ease I. Design-Expert software, Educational Version 7.0. 3.
Wiley Publishing; 2007 Mar 30.
[39] Manohar M, Joseph J, Selvaraj T, Sivakumar D. Application of
desirability-function and RSM to optimize the multi-objective
while turning Inconel 718 using coated carbide tools. International
Journal of Manufacturing Technology and Management. 2013 Jan
1;27(4-6):218-37.
[40] Saidi R, Fathallah BB, Mabrouki T, Belhadi S, Yallese MA.
Modeling and optimization of the tuning parameters of cobalt alloy
(Stellite 6) based on RSM and desirability function. The
International Journal of Advanced Manufacturing Technology.
2019 Feb 25;100(9-12):2945-68.
[41] Gupta MK, Sood PK, Singh G, Sharma VS. Sustainable machining
of aerospace material–Ti (grade-2) alloy: modeling and
optimization. Journal of cleaner production. 2017 Mar 20; 147:614-
27.