COMPARISON OF MAIN EFFECT OF VEGETABLE OIL AND AL2O3 NANOFLUIDS USED WITH MQL ON SURFACE ROUGHNESS AND TEMPERATURE

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International Journal of Mechanical Engineering and Technology (IJMET)
Volume 7, Issue 1, Jan-Feb 2016, pp. 203-213, Article ID: IJMET_07_01_021
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COMPARISON OF MAIN EFFECT OF
VEGETABLE OIL AND AL2O3 NANOFLUIDS
USED WITH MQL ON SURFACE
ROUGHNESS AND TEMPERATURE
Shrikant Borade
Master of Manufacturing Engineering Student,
Jawaharlal Nehru Engineering College,
Aurangabad, Maharashtra, 431001, India
Dr.M.S.Kadam
Professor and Head of Department, Mechanical Engineering,
Jawaharlal Nehru Engineering College,
Aurangabad, Maharashtra, 431001, India
ABSTRACT
The growing demands for high productivity of machining need use of high
cutting velocity and feed rate. Such machining inherently produces high
cutting temperature, which not only reduces tool life but also impairs the
product quality. Application of cutting fluids changes the performance of
machining operations because of their lubrication, cooling, and chip flushing
functions. But the conventional cutting fluids are not that effective in such high
production machining, particularly in continuous cutting of materials likes
steels. So Nanofluids have novel properties that make them potentially useful
in heat transfer medium in cutting zone and Minimum quantity lubrication
(MQL) presents itself as a viable alternative for turning with respect to tool
wear, heat dissipation, and machined surface quality. In this study Vegetable
oil with MQL is compared with Al2O3 Nanfluids and MQL for the turning of
EN353 steel based on experimental measurement of surface roughness and
temperature. This study prepares the effect of MQL and Nano fluids with 3%
volume Al2O3 on the machinability characteristics of EN353 steel mainly with
respect to surface roughness and temperature dissipation. Experimental
analysis for two different conditions – MQL + Vegetable Oil and MQL +
Al2O3 Nanofluids were carried out.
Key words: Al2O3 Nanofluids, MQL, CNC Machining, EN353 steel, Surface
Roughness, Temperature.

Shrikant Borade and Dr.M.S.Kadam
Cite this Article: Shrikant Borade and Dr.M.S.Kadam. Comparison of Main
Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on Surface
Roughness and Temperature. Dynamical Analysis of Silo Surface Cleaning
Robot Using Finite Element Method, International Journal of Mechanical
Engineering and Technology, 7(1), 2016, pp. 203-213.
http://www.iaeme.com/currentissue.asp?JType=IJMET&VType=7&IType=1
1. INTRODUCTION
Cutting fluids are employed in machining operations to improve the tribological
processes, which occur when two surfaces, the tool and work-piece make contact. The
cutting fluid improves the tool life, surface conditions of the work-piece and the
process as a whole. It also helps in carrying away the heat and debris produced during
machining. On the other hand, the cutting fluids have many detrimental effects.
Machining experiences high temperatures due to friction between the tool and work
piece, thus influencing the work piece dimensional accuracy and surface quality.
Machining temperatures can be controlled by reducing the friction between tool–work
piece and tool–chip interface with the help of effective lubrication. Cutting fluids are
the conventional choice to act as both lubricants and coolants. But, their application
has several adverse effects such as environmental pollution , dermatitis to operators,
water pollution and oil contamination during disposal [1,2]. Further, the cutting fluids
also incur a major portion of the total manufacturing cost. All these factors prompted
investigations into the use of biodegradable coolants or coolant free machining.
Hence, as an alternative to cutting fluids, researchers experimented with dry
machining, coated tools, cryogenic cooling, minimum quantity lubrication (MQL) and
solid lubricants. Dry machining, has been reported to be capable of eliminating
cutting fluids with the advancement of the cutting tool materials [3]. Dry machining
requires less power and produces smoother surface than wet machining at specified
cutting conditions [4]. In tribological applications of hard protective coatings the
toughness is as important as their hardness, since both properties have great influence
on the wear resistance of coated tools [5]. Itakuraetal. [6] conducted dry turning
experiments to identify the tool wear mechanisms with coated cemented carbide tool
while machining in conel 718. During continuous cutting almost no rake wear but
only flank wear appeared.
The primary function of a cutting fluid in wet machining operations is to cool, to
lubricate, and to remove the chips. Emulsions or straight oils are generally used
depending on the manufacturing operation and machining task involved. Straight oil
is a cutting fluid that is composed of mineral oil or vegetable oil and is mainly used as
a lubricant. Research by Rao DN, Srikant RR (2006) showed straight oil is not
intended to be mixed with water. Emulsions possess excellent heat transfer
characteristics because of their high water content. Straight oils excel when a high
degree of lubricity is required. Both media guarantee efficient chip transport, when
compressed air is used instead of a cooling lubricant, the lubrication benefit of the
fluid is lost. The coolant effect is much less pronounced than with water or oil. Water
and oil are also superior to air in terms of chip transport characteristics.

Comparison of Main Effect of Vegetable Oil and Al2o3 Nanofluids Used with MQL on
Surface Roughness and Temperature
2. EXPERIMENTAL SETUP
2.1 Selection of work material
The workpiece material used is EN353 steel in the form of round bars of 30 mm
diameter and length of 80 mm.
The composition of material is
Table 1 Composition of EN353 Steel
chemical composition of EN 353 Steel
C Mn Cr Si Mo Ni
0.18% 0.93% 1.11% 0.26% 0.11% 1.34%
EN353 steel is widely used for Machining components in various industries. This
material has significant application in automotive industry. Typical applications of
this material are crown wheel, crown pinion, bevel pinion, bevel wheel, timing gears,
king pin, pinion shaft, differential turn ion etc. The gears especially crown wheel and
pinion are one of the most stress prone parts of a vehicle, etc, which are made of
EN353 steel. This material EN353 steel is used in heavy duty chains, conveyor
chains. The workpiece of EN353 Steel was firstly hardened followed by oil
quenching at a temperature of 850°C to achieve a hardness of 60 HRC throughout. A
rough turning pass was conducted initially to eliminate the run out of the work piece,
after that diameter obtained for experimentation is 60mm.
2.2. Selection of Insert
Based on Literature survey, the Tool selected for this process was CBN CNMG
(Cubic Boron Nitride) 7025 Manufactured by Sandvik Specifications as CNMG
120408.
2.3. Selection of lubricant
Selection of cutting fluid is important in order to maintain better tool life, less cutting
forces, lower power consumption, high machining accuracy and better surface
integrity etc. Here Vegetable Oil {Max mix ST-2020}, is used as cutting fluid in
MQL. ST-2020 is an environmentally acceptable vegetables oil based lubricant. ST-
2020 will yield the lowest net manufacturing costs of any fluid as found by JungSoo,
et al. (2011.)
2.4. Preparation of Nano fluids
For preparation of Al2O3 nanoparticles the Precipitation Method is used. Alumina
prepared by this method is used for preparation of Nanofluids. In this work the
alumina (Al2O3) Nanoparticles are mixed with vegetable oil, as a base fluid to make
Al2O3 Nanofluid. Nano Al2O3 particles are selected due to their superior tribological
and antitoxic properties based on study of Pil-Ho Lee, et al. (2012). The method used
to make Nano fluid is given below. Take one gram of Al2O3 Nano particles and
directly mix with 100 ml vegetable oil as a base fluid and prepare the sample. 1 Vol%
of Al2O3 Nano fluid = 100 ml of vegetable oil + 1gm of Al2O3 Nano particles. 3Vol%
of Al2O3 Nano fluid = 100 ml of vegetable oil + 3gm of Al2O3 Nano particles. The
above composition has to be mixed continuously about 8 to 9 hours using Magnetic
stirrer. The size of Al2O3 particles is about 5 Nm. Here after, Nanofluid means a mix
of MQL and Al2O3 particles.

2.5. Selection of process parameter
Input process parameters were selected after studying the literature on minimum
quantity lubrication/near dry machining. Cutting speed (Vc), cutting feed (f) and
Depth of cut (dc) were the input parameters chosen for present work. To investigate
the effect of input parameters under the application of MQL on EN353 Steel
machining technique. The output parameters chosen were Surface Roughness (Ra)
and Temperature (T) at cutting zone to investigate the effect of input parameters
under the application of MQL for turning on case hardened EN353 steel. The cutting
insert used was CNMG7075 insert.
2.5.1. Machine
Figure 1 CNC Lathe (HAAS – Make)
A high speed precision CNC Lathe (HASS - MAKE) having maximum turning
diameter 406mm, Height of centers over carriage 60 mm Maximum turning length:
762mm, Headstock A2-5 / CAMLOCK D1-3’’ Spindle speed 2,000 rev/min, AC
motor drive: Power consumption 9kW, Transverse stroke X-axis 209mm, AC motor
drive: intermittent torque 4 to 14 Nm Longitudinal stroke, Z-axis 762mm, Number of
fixed tool stations/rotating tool stations. Weight of CNC Lathe 1860kgPower
Requirement: 9KVA, 1-Phase:240V@40A, 3-Phase: 208V
Table 2 Selected Parameter
Sr. no. Parameter Selected Parameter
1 Machine tool:
High speed precision CNC Lath (HASS –
MAKE)
2 Work pieces EN353 Case Hardening material
3 Cutting tool inserts CBN CNMG 7075
4 Cutting speed 800 – 1200 rpm
5 Feed rate, f: 0.05 - 0.09 mm/rev
6 Depth of cut,(d) 0.05 - 0.09 mm
7 MQL supply: Air:4 bar, Lubricant: 50 ml/h
8 Environment:
MQL with Vegetable oil and MQL with
Nanofluids.
9 Measurement of surface roughness
Surf Tester
Least Count - 0.001
10
Measurement of cutting
temperature
Infrared thermometer
Temperature Range-
-40°C to 400°C
Least Count - 1°C

2.6. Experimental procedure
The experiments were performed on the basis of Taguchi’s L9 array method. By
performing One variable at time (OVAT) analysis and from graph it is found that
cutting speed, feed rate, depth of cut are influencing parameters on Surface
Roughness and Cutting temperature. According to OVAT analysis following input
parameters namely cutting speed, feed rate and depth of cut are selected by keeping
other process parameters constant at minimum level which is less influencing on
Surface roughness and Cutting temperature [15]. On the basis of surface finish, the
selected levels of input parameters which are as follows.
Table 3 Selection of Levels from OVAT
3. RESULTS AND DISCUSSION
3.1. For Vegetable Oil with MQL-
3.1.1 For Surface Roughness
Table no.5 and Figure no.2 indicates the signal to noise ratio. In this table highest
signal to noise ratio is indicated in bold format. This higher signal to noise ratio
indicates that, this particular input is favourable on the output. From the graph it is
observed that for best surface roughness the optimum value levels are Speed 800 rpm,
Feed 0.05 mm/rev, Depth of cut 0.05 mm. It is observed that the most influencing
parameter from these three is Depth of cut then followed by feed and then speed.
Analysis of variance (ANOVA)
Table 4 Analysis of Variance for Signal to Noise Ratios
Level Speed Feed Depth of Cut
1 8.593 9.356 9.327
2 7.122 6.944 7.343
3 6.683 6.097 5.728
Delta 1.910 3.259 3.599
Rank 3 2 1
Response Table for Signal to Noise Ratios (Smaller is better)
Table 5 Signal to Noise Ratios - Smaller is better
Parameters Units Level 1 Level 2 Level 3
Speed rpm 800 1000 1200
Feed mm/rev 0.05 0.07 0.09
DOC mm 0.05 0.07 0.09
Source DF Seg SS Adj SS Adj MS F P
S 2 6.007 6.007 3.003 7.30 0.121
F 2 17.158 17.158 8.579 20.85 0.046
D 2 19.495 19.495 9.747 23.68 0.041
Residual Error 2 0.823 0.823 0.411
Total 8 43.484
S 2 6.007 6.007 3.003 7.30 0.121
F 2 17.158 17.158 8.579 20.85 0.046
D 2 19.495 19.495 9.747 23.68 0.041
Residual Error 2 0.823 0.823 0.411
Total 8 43.484

Main Effect Plot for Signal to Noise Ratios
MeanofSNratios 12001000800
9
8
7
6
0.090.070.05
0.090.070.05
9
8
7
6
S F
D
Main Effects Plot (data means) for SN ratios
Signal-to-noise: Smaller is better
Figure 2 Main Effect Plot for SIGNAL TO NOISE ratios
4.1.2 For Temperature (o
C)
Table no.7 and Figure no.2 indicates the signal to noise ratio. In this both the table
highest signal to noise ratio is indicated in bold format. This higher signal to noise
ratio indicates that, this particular input is favourable on the output. From the graph it
is observed that for best temperature the optimum value levels are Speed 800 rpm,
Feed 0.05 mm/rev, Depth of cut 0.05 mm. It is observed that the most influencing
parameter from these three is Depth of cut then followed by feed and at last speed.
Analysis of variance (ANOVA) –
Response Table for Signal to Noise Ratios (Smaller is better)
1 -32.45 -32.71 -32.04
2 -32.73 -32.66 -32.74
3 -32.85 -32.66 -33.25
Delta 0.39 0.05 1.21
Rank 2 3 1
S 2 0.242 0.242 0.121 1.51 0.398
F 2 0.004 0.004 0.002 0.03 0.974
D 2 2.213 2.213 1.106 13.83 0.067
Residual Error 2 0.160 0.160 0.080
Total 8 2.620

MeanofSNratios
12001000800
-32.0
-32.4
-32.8
-33.2
0.090.070.05
0.090.070.05
-32.0
-32.4
-32.8
-33.2
Speed Feed
Depth of Cut
Figure 3 Main Effect Plot for Signal to Noise Ratios
4.2. For Nanofluids with MQL
4.2.1. For Surface Roughness –
is observed that for best surface roughness the optimum value levels are Speed 800
rpm, Feed 0.07 mm/rev, Depth of cut 0.05 mm. It is observed that the most
influencing parameter from these three is Depth of cut then followed by feed and at
last speed.
Response Table for signal to Noise Ratios (smaller is better)
1 9.242 9.284 1.0763
2 7.832 7.906 7.702
3 7.442 7.327 6.052
Delta 1.800 1.957 4.711
Rank 3 2 1
S 2 5.380 5.380 2.690 9.690 0.094
F 2 6.061 6.061 3.030 10.92 0.084
D 2 34.289 34.289 17.144 61.75 0.016
Residual Error 2 0.555 0.555 0.277
Total 8 46.285

MeanofSNratios
12001000800
10.8
9.6
8.4
7.2
6.0
0.090.070.05
0.090.070.05
10.8
9.6
8.4
7.2
6.0
Speed Feed
Depth of Cut
Figure 5 Main Effect Plot
4.2.2. For Temperature
is observed that for best surface roughness the optimum value levels are Speed 800
rpm, Feed 0.07 mm/rev, Depth of cut 0.05 mm. It is observed that the most
influencing parameter from these three is Depth of cut then followed by feed and at
last speed.
Response Table for Signal to Noise Ratios (Smaller is better) –
S 2 0.893 0.893 0.446 12.73 0.073
F 2 0.064 0.064 0.032 0.92 0.521
D 2 1.294 1.294 0.647 18.46 0.051
Residual Error 2 0.070 0.070 0.035
Total 8 2.322
1 -31.04 -31.56 -30.96
2 -31.51 -31.36 -31.51
3 -31.80 -31.44 -31.88
Delta 0.76 0.21 0.92
Rank 2 3 1

4.2. Comparison of Main Effect of Vegetable oil and Al2O3 Nanofluids
used with MQL on Surface Roughness and Temperature –
4.2.1. Surface Roughness (µm)
Figure 7 Comparison graph for surface roughness
From the figure 7 it is observed that at each experiment the value of surface
roughness when used with Al2O3 Nanofluids is comparatively low as compared with
Vegetable oil. The smooth surface finish were obtained while using Al2O3 nanofluids
with MQL.
4.2.2. Temperature (C)
Figure 8 Comparison graph for surface roughness
From the figure 8 it is observed that at each experiment the value of temperature
when used with Al2O3 Nanofluids is comparative low as compared with Vegetable
oil. The main problem with machining any component is heat generated while
machining. To cool down the work piece litres of coolant were wasted. Hence the
Al2O3 nanofluid with MQL is better option for flooded cooling system.

6. ACKNOWLEDGEMENTS
The authors would like to thank Principal Dr. Sudhir Deshmukh MGM’s JNEC
Aurangabad, for continuously assessing my work providing great guidance by timely
suggestion and discussion at ever stages of this work. We would like to thanks all
Mechanical Engineering Department MGM’s JNEC Aurangabad. The authors would
also like to thank Indo German Tool Room, Aurangabad and Swajit Engineering
Pvt.Ltd, Aurangabad for significant information and kind support for experimentation
and making available their testing facility for Research Work.
6. CONCLUSIONS
The present work has successfully demonstrated Al2 O3 Nano fluid applications in
CNC turning on EN353 steel using Minimum Quantity Lubrication. The surface
roughness (Ra) and Temperature (T) were measured under different cutting conditions
for diverse combinations of machining parameters. The final conclusions arrived, at
the end of this work are as follows:
 These experiments show that Surface Roughness and Temperature reduced
significantly by machining EN353 steel using Nanofluid (i.e. 3 Vol. % of Al2O3 Nano
fluid) as compared to MQL Vegetable oil.
 The percentage of error between the predicted and experimental values of the
multiple performance characteristics during the confirmation experiments is less than
5 % as it is within limit.
 The value of multiple performance characteristics obtained from confirmation
experiment is within the 91% to 91.30% confidence interval of the predicted optimum
condition.
Hence it is concluded that Depth of Cut has significant effect on surface
roughness and temperature when used with MQL + Nanofluids. Followed by Feed,
Speed in Surface Roughness and Speed, Feed in Temperature.
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COMPARISON OF MAIN EFFECT OF VEGETABLE OIL AND AL2O3 NANOFLUIDS USED WITH MQL ON SURFACE ROUGHNESS AND TEMPERATURE

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