experimental study of the effect of mineral oil- based sio

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

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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



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.



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.



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



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).



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



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



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




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


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


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



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)

)



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


C.V. % 5.98 Predicted R² 0.7441


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


C.V. % 5.43 Predicted R² 0.7961




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



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



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



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



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



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.

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experimental study of the effect of mineral oil- based sio

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