ORIGINAL ARTICLE

Tribological performance of nanographite-based metalworking fluidand parametric investigation using artificial neural network

W Rashmi1 & M Osama1 & M Khalid2& AK Rasheed2

& S Bhaumik3 & W. Y Wong4& S Datta3 & Gupta TCSM5

Received: 21 November 2018 /Accepted: 4 April 2019# Springer-Verlag London Ltd., part of Springer Nature 2019

AbstractThis paper investigates the impact of palm oil methyl ester (POME) and graphite nanoflakes as additives on the thermophysicaland tribological properties of naphthenic and groundnut oil blends. Different ratios of the two oil blends containing the selectedadditives were formulated and tested. The results show that both POME and graphite nanoflakes could be potential frictionmodifiers. Notably, the four-ball tribometer test reveals that the addition of 7 vol% of POME to naphthenic oil and 0.01 wt%graphite nanoflakes to naphthenic oil results in the reduction of wear scar diameter by 24% and 59%, respectively. Surfacemicrographs of aluminum workpiece subjected to simple turning operation in a CNCmachine corroborated the wear test results.POME in naphthenic oil resulted in better surface finish relative to POME-formulated groundnut oil. The experimental data wereanalyzed using artificial neural network (ANN) to gain a better understanding of the entire work. The sensitivity analysisdeveloped from the chosen ANN models exhibited the nature of influence of the input parameters (groundnut concentrations,POME, and naphthenic oil) on the outputs (Noack volatility, thermal conductivity, and wear).

Keywords Nanographite . Nanolubricant . Thermal conductivity . Tribology . Nanoscale heat transfer

1 Introduction

Metalworking is an important process in the manufacturingindustry comprising of cutting, milling, turning, facing, grind-ing, drilling, and so on. Such operations involve metal-metalcontacts at high speeds that increase temperature and pressureaffecting the workpiece finishing and tool wear. Coolantswhich are popularly known as metalworking fluids (MWFs)

are used to lubricate and cool the tool as well as the workpiece[1, 2]. MWFs separate the sliding surfaces by forming a liquidfilm and thereby reduce the frictional resistance and metalwear. From the total heat generated during metalworking,25% is due to the friction between tool and workpiece andthe rest is due to the metal deformation [1]. The cooling abilityof a MWF enables control over the temperature of workpiece,tool, and chip. In addition, coolants transport chips during themachining process, prevent re-welding, and reduce energyconsumption. Industrial revolution advanced the use ofMWFs all over the manufacturing which is reflected in im-proved tool life and machining speed [3]. Subsequent inven-tions of petroleum derivatives prompted the use of mineraloils and kerosene by-products as they are economical thanthe other alternatives [4]. Rizvi reported that naphthenic baseoils are preferred due to their lower viscosity index, greaterviscosity increase as a result of pressure rise, and better sol-vency properties towards additives [1]. Technological ad-vances in the manufacturing industry continue to demandmore efficient coolants than the existing petroleum-basedfluids which are relatively cheaper and accessible. Moreover,the rise in occupational health and environmental regulationsrequires eco-friendly coolants [4].

Disposal of oils extracted from animals and vegetables iseasy due to its biodegradability. Such bio-based oils possess

* W Rashmirashmi.walvekar@gmail.com; khalids@sunway.edu.my

1 Sustainable Energy and Green Technology Research Group, Facultyof Innovation and Technology, School of Engineering, Taylor’sUniversity Lakeside Campus, Subang Jaya, Malaysia

2 Graphene and Advanced 2D Materials Research Group (GAMRG),School of Science and Technology, Sunway University, No. 5, JalanUniversiti, Sunway City, 47500 Subang Jaya, Selangor, Malaysia

3 Tribology and Surface Interaction Research Laboratory, Departmentof Mechanical Engineering, SRM Institute of Science andTechnology, Kattankulathur, Tamil Nadu 603203, India

4 Fuel Cell Institute, Universiti Kebangsaan Malaysia,Bangi, Selangor, Malaysia

5 Research and Development, Apar Industries Limited, Mumbai, India

The International Journal of Advanced Manufacturing Technologyhttps://doi.org/10.1007/s00170-019-03701-6

excellent lubricity and viscosity-temperature characteristic,good anti-corrosion, and low volatility. Lawal et al. [5] studiedthe applications of vegetable oils in machining operations andreported that such oils improve cutting performance. For in-stance, the examination of various coolant emulsions obtainedfrom different vegetables at a concentration of 10% showedthat the contact temperature is significantly lowered by usinggroundnut oil emulsion [6]. Another report suggests thatamong various vegetable oils, groundnut oil helps in achiev-ing the least cutting force during cylindrical turning of copper,aluminum, andmild steel, implying lower power consumption[7]. On the other hand, palm oil methyl ester (POME) obtain-ed by transesterification of palm oil overcomes certain defi-ciencies of vegetable oils [1]. POME as an anti-wear additiveat a concentration of as low as 5% was able to improve thelubricating performance at low concentrations [8].Nevertheless, pure POME exhibited poor performance andresulted in the highest wear rate. Dayou et al. [9] found thatPOME, when added to paraffinic oil in mist lubrication at avolume fraction of 5 vol%, can delay wear and surface frac-ture of the tool during milling.

Interestingly, nanoparticles as coolant additives havegained the attention of researchers worldwide. Besides theadvantage of having better thermophysical properties, nano-particles penetrate the microscopic contacts, ridges, andgrooves. Mansot et al. [10] reported that without any chemicalreaction, nanoparticles can form tribofilms in a lower induc-tion period. Multilayered graphene, which may also be re-ferred to as nanographite added to base oils, has shown supe-rior tribological properties relative to the pure base oils.Furthermore, Berman et al. [11] reported that graphene nano-particles not only help lubricate but also offer protectionagainst oxidation and corrosion. Also, having exceptionallyhigh thermal conductivity > 5000 W m−1 K−1 [12, 13],graphene has the potential to improve the cooling perfor-mance of cutting fluids. In another study [14], it was observedthat an optimal concentration of modified graphene plateletsincreases the load-carrying capacity of the lubricant. The con-centration, being 0.075 wt%, is very low compared to that ofother nanoparticles used in metalworking application studies.Further, a reduction of > 20% in the coefficient of friction(COF) is reported to have been achieved using graphene oxide

nanoparticles [15]. Recently, Rashmi et al. [16] investigatedpalm oil–based trimethylolpropane containing 0.05 wt%graphene and observed a decrease in wear scar diameter andCOF by 7% and 16.2%, respectively.

Existing literature [17] on the performance of cutting fluidsfocuses mainly on tribological properties without a parallelinvestigation of the physical properties and thermo-oxidativecharacteristics. As a result, correlating the tribological perfor-mance with viscosity which is a physical property is not pos-sible. Similarly, thermo-oxidative stability is crucial in deter-mining the life service of the cutting fluids. Therefore, in thiswork, various blends of lubricants were prepared by varyingthe proportions of naphthenic oil, groundnut oil, POME, andgraphite nanoflakes. The nanolubricant stability and physicaland tribological properties are characterized. The preparedcutting fluid was tested for turning application using alumi-num as a workpiece and KGTN3 carbide tool. Furthermore, toestablish the role of the parameters behind the observationsmade, the artificial neural network (ANN) models [18] weredeveloped. The main advantage of using ANN is its capabilityof mapping relationships with small data sets, which helps in-depth analyses of the exact roles of the input parameters [19].

2 Materials

Table 1 shows the physical properties of the naphthenic baseoil and the POME oil as claimed by the respective suppliers.The groundnut oil and the graphite nanopowder were pur-chased from Waitrose (UK) and Graphene Supermarket(USA), respectively. The average thickness of nanoflakes is60 nm with a purity of about 98.5%. The average lateral di-mension of the flakes is 3–7 μm. The specific surface area ofthe nanoflakes is smaller than 15 m2/s.

3 Methodology

3.1 Sample formulation

In this work, 60-nm-thick graphite particles will be referred toas “nanoflakes.” The lubricant samples prepared by blending

Table 1 Physical properties ofmineral oil and POME Property Test method Units Naphthenic oil T22

(Nynas Pte. Ltd., Singapore)POME (ExcelVite Sdn.Bhd., Malaysia)

Physical form Visual NA Liquid Liquid

Appearance Visual NA Light yellow Clear colorless toslightly yellowish

Viscosity at 40 °C ASTM D445 mm2/s 22.43 4.5

Flash point PM ASTM D93A °C 169 174

Density at 15 °C ASTM D4052 kg/dm3 0.9015 0.8700

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different proportions of oils with graphite nanoflakes asshown in Table 2 will be called as “nanolubricants.”Naphthenic oil, groundnut oil, POME, and graphite nanoflakesare denoted as M, G, P, and Gr, respectively. The images of thesamples can be found in the supplement.

3.2 Stability of nanofluid samples

The nanolubricant stability of graphite nanoflakes was inves-tigated using the Genesys 10S UV-vis spectrophotometer(Thermo Scientific). Absorbance was measured at 225 nm,and the corresponding concentration of samples with respectto time was determined. In addition, the quality of the disper-sion and the size of the agglomeration were characterized andmeasured using the Swift M10D Series Digital Microscope.The microscopic images were taken at a magnification of ×40. The average cluster size of graphite nanoflakes was alsomeasured.

3.3 Physical and chemical characterization of samples

In this work, the dynamic viscosity was measured at 40 °C attwo shear rates (10 s−1 and 500 s−1) using a rheometer (HaakeMars, Thermo Scientific). Similarly, the thermal conductivityat three different temperatures (25 °C, 40 °C, and 55 °C) wasmeasured using the KD2 Pro thermal conductivity meter(Decagon Devices, USA). The oxidation stability was deter-mined using PerkinElmer TGA 8000 with oxygen as a purgegas at a flow rate of 20 ml/min. Likewise, based on the ASTMD6375 standard, Noack volatilities of the samples were deter-mined [20]. The detailed methodology of thermal conductiv-ity, rheology, and thermogravimetric measurements is de-scribed in our previous work [21].

The corrosion characteristics of the test sample wereassessed based on ASTM D130. Standard copper strips witha dimension of 12.7 mm × 76.2 mm × 2.38 mmwere polished

using P120 silicon carbide for coarse polishing and P600 sil-icon carbide for fine polishing. The copper strips were thenimmersed in a 35 ml volume of the sample. The samples werethen placed in an oven for 3 h at a temperature of 100 °C. After3 h, the copper strips were removed using forceps and imme-diately washed using acetone. The degree of corrosiveness foreach sample was interpreted following the ASTM CopperStrip Corrosion Standard.

3.4 Wear and friction analysis

Anti-wear properties of the test samples were studied based onASTM D4172 using the four-ball tester (Ducom TR 30 L).The test was performed at 75 °C at 1200 rpm with a load of40 kg for a duration of 60 min. Before using the 12.7-mm-diameter balls, they were wiped thoroughly with heptane toensure that polished shiny surface is obtained and to dissolveany oil residue that may affect the readings. Measurement ofwear scar diameters (WSDs) of the bottom balls was per-formed using an optical microscope. Subsequently, the wearscar surfaces of the three bottom balls were analyzed using theQuanta 400F SEM.

3.5 Turning test

The cutting test was performed using the LA430 × 1100 latheturning machine from Mastika with the KGTN3 carbide tooland aluminummetal rod as a workpiece. The feed rate was setto 0.11 mm/rev, and the spindle speed was set to 950 rev/min.The quality of the surface cut, the surface of the inner chip,and the chip thickness were analyzed qualitatively using theQuanta 400F SEM. The performance of the selected samplesis compared to the results of dry cutting and commercial cool-ant cutting as reported in Section 4.

Table 2 Composition of testedsamples Sample group Sample code M (wt%) G (wt%) P (vol%) Gr (wt%)

Naphthenic base oil ML 100 0 0 0

PML 7 0

GML 0 0.1

GPML 7 0.075

Naphthenic-groundnut oil blend BML 50 50 0 0

PBML 7 0

GBML 0 0.1

GPBML 5 0.1

Groundnut oil BL 0 100 0 0

PBL 7 0

GBL 0 0.1

GPBL 7 0.075

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3.6 Artificial neural network methodology

Artificial neural network is a powerful tool which helps inestablishing connections between the parameters by develop-ing complex relationships. In this work, a feedforward multi-layer perceptron-type architecture which is trained with scaleconjugate backpropagation algorithm has been adopted. Theexperimental data were used to train the networks. The param-eters (both input and output) were normalized within the rangeof − 1 to 1 using Eq. (1)

xN ¼ 2 x−xminð Þxmax−xmin

−1 ð1Þ

where xN is a normalized value of x, and xmax and xmin arethe maximum and minimum values of x. In order to operatethe weighted combination of the normalized input for everyhidden unit as such that each input contributes to every hiddenunit (j), a tangent hyperbolic transfer function (Eq. (2)) is used.The expression for computing the hidden node values is writ-ten as

hj ¼ tanh ∑wijxNi þ bj� � ð2Þ

where wij, b, i, and j are the weights, b is the bias, and i isthe input. The output y is calculated using Eq. (3)

y ¼ ∑W jhj þ b ð3Þ

The error of the predicted output is backpropagated usingscaled conjugate gradient algorithm to adjust the weights and

biases. To determine the influencing parameters, sensitivityanalysis connection weight method [22] was applied to thetrained networks. A schematic diagram of a perceptron-typeANN is shown in Fig. 1.

4 Results and discussion

4.1 Nanolubricant stability

The relative stability of the nanofluid samples is shown in Fig.2. With the increasing groundnut oil concentration, thenanolubricant stability increases. Samples without POMEwith the exception of naphthenic oil group have better stabilitycharacteristics than their corresponding POME-based sam-ples. Schuchardt et al. [23] reported that the transesterificationprocess lower the viscosity of vegetable oils, and therefore,POME has a lower viscosity relative to the naphthenic andgroundnut oils. Higher viscosity indicates higher resistance tosedimentation of nanoparticles. As such, lowering the viscos-ity because of POME addition results in lower stability as seenin Fig. 2.

In contrast, although POME reduces the viscosity of thenaphthenic base oil, the addition of POME resulted in a slight-ly better nanolubricant stability. As such, the improvednanolubricant stability as a result of POME addition in thenaphthenic base oil cannot be correlated with viscosity. Itcan be deduced that there are some surface interactional phe-nomena or functional group change that would encourage

Fig. 1 ANN structure with asingle hidden layer

Int J Adv Manuf Technol

higher solvency towards graphite nanoflake nanoparticles(better nanolubricant stability) despite the fact that the viscos-ity is reduced. This can be seen from Fig. 2 in which thesample GPML obtained slightly better nanolubricant stabilitythan the sample GML. The highest stability was achieved bythe sample GBL (0.1 wt%) which maintained roughly 60% ofinitial concentration for a period of 1 month. Relatively, Leeet al. [24] reported higher stability for fullerene-based mineraloil nanofluid which maintained roughly 80% concentrationfor 1 month. In comparison, the highest stability achieved bythe naphthenic base oil was for the sample GPML that main-tained roughly around 29% of initial concentration for a peri-od of 1 month. Rashmi et al. [25] reported stability of morethan 1 month for MWCNT in water using gum arabic as a

surfactant. However, applying surfactants in lubricants is notdesired because it may not satisfy the viscosity standards oflubricants as reported by Rasheed et al. [26]. An alternativesolution to improve the stability of the graphite nanoflakes inmineral oil and low groundnut composition samples is to con-sider functionalization of graphite nanoflakes’ surface. Theinvestigation of the aggregate size from Fig. 3 indicates thatthe samples GPML and GPBML have lower aggregate sizethan the samples GML and GBML. Thus, it can be inferredthat the addition of POME reduces the average aggregate sizerelative to the blend sample. Smaller aggregate size is moredesired. This is since the larger the aggregate size, the higherits tendency to favor sedimentation as reported by Ghadimiet al. [27].

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On the other hand, for groundnut oil, the addition of POMEresulted in larger aggregate size. Similar to many other prop-erties examined in this report, POME added to mineral oil isbetter than the groundnut oil based on aggregate size analysis.In addition, it can be seen that the aggregate size is in micronscale. This can explain the poor stability performance of thesamples. Overall, it can be noted that the observed stabilitycharacteristics do not correlate very well with the measuredaggregate size. For instance, despite having a lower aggregatesize, GPML samples have poorer stability relative to GPBL.Aggregate size can be a good indicator to roughly describe theexpected stability behavior of the nanolubricant. However,many other factors along with aggregate size must be consid-ered to draw an accurate conclusion regarding thenanodispersion stability. Thus, it can be deduced that the sta-bility of nanofluids is a complex phenomenon of the interac-tion between viscosity, aggregate size, and concentration ofnanoparticles.

4.2 Dynamic viscosity

From Fig. 4, samples containing a higher percentage ofgroundnut oil have higher dynamic viscosity. On the otherhand, the addition of POME resulted in lower dynamic vis-cosity relative to the corresponding POME-free samples.

The POME’s action could be ascribed to the lower viscos-ity of vegetable oils that are subjected to the transesterificationprocess as reported by Schuchardt et al. [23]. Relative to this,the addition of graphite nanoflakes did not result in big chang-es in the dynamic viscosity relative to the base fluids. Asconcluded by Stachowiak and Batchelor [28], dynamic vis-cosity is a function of the film thickness. Therefore, it is in-ferred that the POME alters the viscosity which subsequentlyaffects the film thickness.

4.3 Thermal conductivity

Figure 5 shows that the increase of the groundnut oil compo-sition results in increasing the thermal conductivity. As such,groundnut oil has a good capability for heat removal duringcutting. Besides, the addition of POME and graphitenanoflakes did not cause a pronounced change to the thermalconductivity of the pure base fluids.

4.4 Oxidative stability

The oxidation stability of the test samples was assessed usingthe onset temperature. Groundnut oil shows the highest onsettemperature as seen in Fig. 6. This can be attributed to thepresence of high proportions of saturated and monounsaturat-ed fatty acids in groundnut oil.

However, in the literature [1], it is reported that the loweroxidative stability of naphthenic base oil is due to the presenceof aromatic compounds. The addition of POME to the mineraloil increased the onset temperature slightly, but its addition toother oil blends resulted in a lower onset temperature. Theaddition of graphite nanoflakes leads to the highest onset tem-perature in all oil blends except groundnut oil. Among thegroundnut oil group, the addition of graphite nanoflakesachieved the second highest onset temperature. The additionof POME and graphite nanoflakes together resulted in loweroxidative stability. In the case of naphthenic base oil, graphitenanoflakes delayed oxidation by 10 °C, which is comparableto the results reported by Rasheed et al. [29]. In comparison tothe oxidative onset temperatures of cottonseed, corn, canola,safflower, sunflower, and soybean oils reported [30], ground-nut oil from this study with an onset temperature of 235 °C hasbetter oxidative resistance. The relatively higher onset

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Fig. 4 Dynamic viscosity of thetest samples at T = 40 °C

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temperature of groundnut oil observed here can be attributedto the higher content of monounsaturated fatty acids.

4.5 Evaporation losses

Figure 7 shows the Noack volatilities of the test samples. Theoil volatility is found to be suppressed with increasing ground-nut oil content. In comparison to mineral oil, the volatility ofgroundnut oil has significantly lowered. This change could bedue to the polarity of groundnut oil as described by Rizvi [1].

We observed that the Noack volatility of naphthenic oil isaround 95% which is quite similar to the study by Sharma

et al. [31] where they reported a volatility of 98.8% for thenaphthenic base oil. In contrast, POME in mineral oil de-creases the volatility to some extent. This can be due to thefact that the addition of POME results in the transfer of estermolecules to the naphthenic base oil, helping to decrease thevolatility as reported by Rizvi [1]. However, for the blend andthe groundnut samples, the addition of POME resulted inslightly higher volatility, a phenomenon that is ascribed to atransesterification process as suggested in previous studies[23]. As such, POME has a higher volatility than the ground-nut oil and, upon addition, it increases the volatility of themixture. This shows that POME as an additive is more

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Fig. 5 Average thermalconductivity of the test samples

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Fig. 6 Oxidation onsettemperature at 10 °C/min

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effective in mineral-based oil than in vegetable oil. The addi-tion of graphite nanoflakes resulted in slightly lower volatilityrelative to base fluids.

4.6 Copper corrosion

Table 3 shows the results of the copper corrosion test(ASTM D130) for the test samples. The results show thatbase fluids and POME-formulated samples result in onlyslight tarnish. The addition of POME in mineral oil result-ed in a lower corrosion rating. In contrast, the addition of

POME to the blend and groundnut oil did not show achange of corrosive behavior relative to the base fluid.Similar to many properties examined in this study,POME is found to be more effective in mineral than ingroundnut oil. Relative to graphite nanoflakes, the corro-sion tendency was found to be higher when graphitenanoflakes are added which resulted in a moderate tar-nish. This increase of corrosion can be due to the pHvalue of the graphite nanoflake–based nanofluid whichmay generate conditions that favor higher corrosion.

4.7 Tribological characteristics

Wear test shows that the addition of POME and graphitenanoflakes is more effective with mineral oil than ground-nut oil. With respect to the naphthenic oil-groundnut oilblend sample, the addition of graphite nanoflakes in-creased the WSD by 12% compared to the base fluid.On the other hand, POME at 7 vol% decreased theWSD of all the groups except for groundnut oil. Thehighest reduction in WSD was achieved for mineral oil,i.e., 24%. In comparison, the reported reduction of WSDby POME at 5 vol% as reported by Dayou et al. [9] wasno higher than 12%. The higher reduction of the WSDfrom this study can be due to the higher concentrationof POME. The interaction of graphite nanoflakes andPOME is more effective for groundnut oil in which theWSD decreased by 14%. The latter value is the highestreduction of WSD that was noted for the groundnut oilsamples. For mineral oil, the POME-graphite nanoflake–based mineral sample reduced the WSD, but at a lower

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Fig. 7 Noack volatility of the testsamples

Table 3 Corrosiveness of the test samples

Sample code Corrosion copper strip (3 h/100 °C)

Rating Designation

ML 1a Slight tarnish

PML 1a Slight tarnish

GML 2c Moderate tarnish

GPML 2b Moderate tarnish

BML 1a Slight tarnish

PBML 1a Slight tarnish

GBML 2c Moderate tarnish

GPBML 2c Moderate tarnish

BL 1a Slight tarnish

PBL 1a Slight tarnish

GBL 2c Moderate tarnish

GPBL 2c Moderate tarnish

Int J Adv Manuf Technol

percentage of reduction relative to other mineral oil sam-ples. The POME-graphite nanoflake–based blend samplewas found to increase the WSD by 7%. The highest re-duction (59%) of WSD was achieved for the sample GMLat 0.1 wt% of graphite nanoflakes as shown in Fig. 8.

Alves et al. [32] reported that when ZnO and CuO nano-particles are added tomineral and synthetic, theWSD is small-er and the surface is smoother, which is similar to the perfor-mance observed for the naphthenic base. From the same study,Alves et al. [32] found that when nanoparticles are added withsoybean and sunflower oils, the WSD was found to be higherrelative to the pure vegetable oils. From this study, graphitenanoflakes although reduced the WSD for pure groundnut oil,the percentage of reduction (7%) was significantly lower thanthat obtained by graphite nanoflakes when in mineral oil. Onthe other hand, Rashmi et al. [16] reported a reduction ofWSD by 16% when graphite nanoflakes are added to palmoil–based trimethylolpropane ester lubricant. In comparison tothe findings from this study, POME-graphite nanoflake–basedgroundnut oil achieved about a 14% reduction of WSD. It canbe concluded from WSD results that nanoparticles are lesseffective when added to vegetable and chemically modifiedvegetable oils.

The correlation of the viscosity with the WSD does notshow an obvious relation between the two parameters.However, when the viscosity is correlated to specific lubricat-ing film groups, a strong relationship is found as shown in Fig.9a–d.

Four different lubricating films which are the groundnut,POME, graphite nanoflakes, and POME-graphite nanoflakelubricating films were detected from this study. According toWilliamson and Bell [33], the major contribution to the oilfilm thickness is not due to viscosity, rather mainly due tothe existence of anti-wear films over the surfaces. When

applied to this study, it can be seen that liquids with higherviscosity do not provide better protection against wear. Thissuggests that the chemical composition lubricant ingredientsplay an important part in defining the characteristics of the oilthickness. From this study, it can be concluded that for eachlubricating film group, there is an optimum viscosity at whichthe WSD is minimum. For all the lubricating films, the min-imum viscosity was found to be the optimum. Analysis of theworn surfaces shows that groundnut oil has a relativelysmoother surface when compared to the mineral and blendsample as shown in Fig. 10.

This indicates that the WSD result cannot be directly cor-related to the roughness of the surface. In groundnut oil, thewear is more distributed and smoother. In contrast, the WSDofmineral although contained is rougher and contains groovesas shown in Fig. 10. The addition of POME shows significantimprovement for mineral oil. However, for the blend sample,no noticeable improvement can be seen. Conversely, the ad-dition of POME to groundnut oil degraded the quality of thesurface. In all the groups, graphite nanoflakes were found toresult in a smoother surface relative to the base fluids. POME-graphite nanoflake samples, although improved the surfacequality, are found to be inferior relative to the performanceof graphite nanoflake POME-free nanolubricants.

4.8 Turning performance

Observations on the micrographs of the machined surfaces, asshown in Fig. 11, show that cutting by mineral oil resulted in amachined surface with higher cavities and irregularities rela-tive to the blend and pure groundnut oil sample.

The blended sample obtained better surface finish com-pared to pure groundnut oil and mineral oil. POMEwas foundto interact more favorably with mineral oil, followed by the

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Fig. 8 Wear scar diameter of thetest samples

Int J Adv Manuf Technol

blend sample and the groundnut oil. The behavior of POMEcan be ascribed to its ester functional groups. In mineral,POME imparts ester functional group which helps create adurable lubricating film as reported by Rizvi [1]. In blendand groundnut oil, it is apparent that the lubricating films ofgroundnut and POME compete for the surface which leads toaugmented wear. The higher the composition of groundnutoil, the higher the competition and, eventually, the higher theresultant wear. Samples GML and GPML and cutting underdry and coolant conditions were found to have a comparablerough surface finish. The addition of graphite nanoflakes wasfound to improve surface finish quality significantly whenadded to the groundnut oil and blend sample. As it can beseen in Fig. 11, graphite nanoflake layers were formed bysamples GBML and GBL which helped to provide smoothcutting and better surface finish. The interaction of graphite

nanoflakes with POME in the blend sample was found toslightly lower the surface finish quality when compared tothe sample GBML. However, for the groundnut oil sample,the interaction of graphite nanoflakes with POME does notresult in favorable outcomes.

From this study, the chips formed are long and contin-uous. Figure 12 shows the morphological surface of thechips. All the chips were found to have rough features andsegmentation. Furthermore, lamellar patterns can be seenalong the chip width. According to Fernández-Abia et al.[34] when lamellas are small and more uniform, the cut-ting speeds are lower. In addition, Groover [2] reportedthat the cutting temperature is proportional to the cuttingspeed. As such, it can be deduced that the cutting temper-ature can be qualitatively assessed by visually describingthe shapes of the lamellar patterns observed along the chip

WSD = 0.022DVR² = 0.965

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(mm

)

DV (cP)

a b

c dFig. 9 aWear scar behavior for groundnut lubricating film. bWear scar behavior for POME lubricating film. cWear scar behavior for graphite nanoflakelubricating film. d Wear scar behavior for graphite nanoflake-POME lubricating film

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width. As it can be seen from Fig. 12, commercial coolantobtained smaller and more uniform chip segmentation.

This indicates that the commercial coolant is morelikely to have achieved the lowest cutting temperature.On the other hand, dry cutting although achieved smallchip segmentation, signs of wear are quite prevalent onthe surface. The wear observed for dry cutting is morelikely to be adhesive wear which resulted due to fric-tional heat as reported by Rizvi [1]. The chip formed bygroundnut oil had signs of wear, but at a relativelysmaller scale when compared to dry cutting. The addi-tion of graphite nanoflakes to groundnut achieved lowerchip segmentation and prevented damage to the surface.This can be due to the better heat dissipation by graph-ite nanoflake layer on the top of the chip. The chipsegmentation of POME-graphite nanoflakes when ingroundnut was found to be quite deep which indicatespossible high temperature, but with better protectionagainst heat due to interactional layers of POME andgraphite nanoflakes. Samples GML and GPML exhibit-ed rougher chip surface when compared to pure mineraland POME-mineral oils. The addition of POME to min-eral oil improved slightly the chip segmentation relativeto pure mineral oil. Chip segmentation of the blend

sample was found to be relatively inferior to that ofmineral and, at the same time, better than that ofgroundnut oil.

Although mineral oil had lower thermal conductivitywhen compared to groundnut oil, the chip segmentationof the latter was impacted by wear. This indicates thatthe cutting temperature is a phenomenon of the interac-tion between the frictional heat generated and the abilityof the cutting fluid to dissipate the heat generated.Fluids with higher viscosity may result in higher frictionbetween the tool and workpiece which increases thetemperature. If the thermal conductivity of the liquid isnot high enough, the high viscosity can be detrimentalto the process. Therefore, there must be a compromisebetween the lubricating performance and the coolingcapability of the liquid. Generally, POME was foundto be more effective as an additive when in mineraloil. In contrast, graphite nanoflakes are more effectivewhen dispersed in groundnut.

4.9 ANN models and data interpretation

Many multilayered perceptron models were trained usinga single hidden layer. The nodes were varied for

Fig. 10 SEM images of the worn surfaces

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developing separate models for Noack volatility, thermalconductivity, oxidation onset temperature, and wear scardiameter. Several ANN models with varied hidden nodeswere generated using the input parameters, and the selec-tions were made according to the highest regression coef-ficient value as shown in Fig. 13.

The overall effects of the variables were analyzed usingthe sensitivity analysis by using the weight and biases ofthe models developed [19]. From Fig. 14a, the Noackvolatility decreases with the increasing concentration ofgroundnut, POME, and graphite nanoflakes while theNoack volatility will increase with the increase in thecontent of naphthenic concentration. For thermal conduc-tivity, groundnut concentration, POME, and graphite

nanoflakes play an important role in enhancing it whilethermal conductivity will decrease with the increase innaphthenic content (Fig. 14b). Groundnut concentrationinfluences the oxidation onset temperature most as com-pared to other parameters. Figure 14c shows that, with ahigher concentration of groundnut, the oxidation onsettemperature will also be high. Furthermore, Fig. 14dshows that the increase in viscosity results in increasedwear scar diameter and groundnut concentration whilewith the increase in naphthenic concentration and POMEand graphite nanoflake contents, the wear scar diameterdecreases.

The simulated results showing the combined effect oftwo inputs on various outputs, keeping the other two

Fig. 11 SEM images of the machined surfaces

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inputs constant, are shown in Fig. 15. These plots exhibitthe insights on the combined effect of the inputs. Thesurface plots are shown in Fig. 15a, b, revealing that ahigher concentration of groundnut and POME reduces thevolatility of the oil samples while naphthenic concentra-tion increases the volatility of the samples. The thermalconductivity (Fig. 15c) increases with the increase in theconcentration of groundnut whereas it can be seen thatnaphthenic concentration will lower the thermal conduc-tivity. Graphite nanoflakes seem to increase the thermalconductivity, but POME seems to have less effect on ther-mal conductivity after a certain concentration (Fig. 15d).As seen from Fig. 15e, groundnut oil suppresses the oxi-dation and hence results in high oxidation onset tempera-ture while the presence of POME and naphthenic oil

favors the oxidation of the oil samples and hence tendsto lower the oxidation onset temperature (Fig. 15e, f).Therefore, a blend of higher concentration of groundnutoil and a lower concentration of POME and naphthenic oilwill result in having a better oxidation onset temperature.The wear scar formed during the test seems to have beeninfluenced by the presence of POME, groundnut concen-tration, and naphthenic concentration. The lowest wearscar diameter can be obtained with a small amount ofgroundnut and POME in naphthenic oils (Fig. 15g–i).From Fig. 15g–i, it can be seen that high viscosity andthe presence of high concentrations of groundnut andPOME favor the more wear which, in turn, increases thewear scar diameter while the presence of graphitenanoflakes helps in the reduction of wear scar diameter.

Fig. 12 SEM images of the chip morphology

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Fig. 13 Scatter plot for a Noack volatility, b thermal conductivity, c oxidation onset temperature, and d, e wear scar diameter

Fig. 14 Sensitivity analysis for a Noack volatility, b thermal conductivity, c oxidation onset temperature, and d, e wear scar diameter

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5 Conclusion

Both base oils and additives in various combinations yielddifferent results, indicating optimization of the concentrationof each element is vital based on specific application require-ments. From this work, four major conclusions are drawn:

1. Both POME and graphite nanoflakes are potential addi-tives to enhance the lubrication performance of groundnutand naphthenic oils.

2. Out of all the oil-additive combinations tested in the four-ball tribometer, naphthenic-groundnut-graphitenanoflakes and groundnut-graphite nanoflakes produce arelative smooth surface.

3. The addition of POME to naphthenic base oil results inthe best cutting and tribological performance among theentire naphthenic oil–based samples.

4. From the ANN results, it can be seen that high naphthenicconcentrations will favor high Noack volatility and highoxidation onset temperature, but low wear scar diameter.

Furthermore, with the increase in the concentration ofgroundnut and POME, the Noack volatility will decrease.Groundnut concentration will favor the thermal conduc-tivity and oxidation onset temperature. The viscosity ofthe lubricant also plays a major role as predicted by ANNmodels. An increase in viscosity will lead to high wearscar. Thus, the ANN-based data analyses provided an in-sight of the system, which helped in understanding theroles played by the parameters in controlling the desiredoutput which will be helpful in developing a lubricantwith better tribological properties.

Acknowledgments We acknowledge the ExcelVite Sdn. Bhd. and theNynas Pte. Ltd. for supplying the POME and naphthenic base oils,respectively.

Compliance with ethical standards

Conflict of interest The authors declare that they have no conflict ofinterest.

Fig. 15 Surface plots for a, b Noak volatility, c, d thermal conductivity, e, f oxidation onset temperature, g-i wear scar diameter

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References

1. Rizvi, S.Q.A. 2009 Comeprehensive review of lubricant chemistry,technology, selection, and design. : ASTM International.

2. Groover, M.P., Fundamentals of modern manufacturing: materials,processes, and systems. Fourth Edition ed. 2010, United States ofAmerica John Wiley & Sons, Inc.

3. Richard, G.B. and L.B. William 2005 Metalworking fluids. In:Synthetics, mineral oils, and bio-based lubricants. CRC.

4. Brinksmeier E, Meyer D, Huesmann-Cordes AG, Herrmann C(2015) Metalworking fluids—mechanisms and performance.CIRPAnn Manuf Technol 64(2):605–628

5. Lawal SA, Choudhury IA, Nukman Y (2012) Application of veg-etable oil-based metalworking fluids in machining ferrous metals—a review. Int J Mach Tools Manuf 52(1):1–12

6. Lawal, S.A., et al. 2007 Performance evaluation of cutting fluidsdeveloped from fixed oils. Leonardo Electronic Journal of Practicesand Technologies

7. Ojolo SJ, Amuda MOH, Ogunmola OY, Ononiwu CU (2008)Experimental determination of the effect of some straight biologicaloils on cutting force during cylindrical turning. Matéria (Rio deJaneiro) 13:650–663

8. Masjuki HH, Maleque MA (1996) The effect of palm oil diesel fuelcontaminated lubricant on sliding wear of cast irons against mildsteel. Wear 198(1):293–299

9. Dayou S, Liew WYH, Ismail MAB, Dayou J (2011) Evaluation ofpalm oil methyl ester as lubricant additive using milling and four-ball tests. International Journal of Mechanical and MaterialsEngineering 6(3):374–379

10. Mansot JL, Bercion Y, Romana L, Martin JM (2008)Nanolubrication. Braz J Phys 39:186–197

11. Berman D, Erdemir A, Sumant AV (2014) Graphene: a new emerg-ing lubricant. Mater Today 17(1):31–42

12. Pop E, Varshney V, Roy AK (2012) Thermal properties ofgraphene: fundamentals and applications. MRS Bull 37(12):1273–1281

13. Balandin AA, Ghosh S, Bao W, Calizo I, Teweldebrhan D, Miao F,Lau CN (2008) Superior thermal conductivity of single-layergraphene. Nano Lett 8(3):902–907

14. Lin J, Wang L, Chen G (2011) Modification of graphene plateletsand their tribological properties as a lubricant additive. Tribol Lett41(1):209–215

15. Vincenzo D’Agostino, Adolfo Senatore, Vincenzo Petrone, PaoloCiambelli, Maria Sarno 2012 Tribological behaviour of graphenenanosheets as lubricant additive. In 15th International Conferenceon Experimental Mechanics. : Porto/Portugal. pp. 22–27.

16. Rashmi W, Khalid M, Lim XY, Guepta TCSM, Arwin GZ (2017)Tribological studies on graphene/TMP based nanolubricant. J EnSci Technol 12(2):365–373

17. Selvam MD, Sivaram NM (2018) A comparative study on the sur-face finish achieved during turning operation of AISI 4340 steel inflooded, near-dry and dry conditions. Aust J Mech Eng:1–10

18. Datta S (2016) Materials design using computational intelligencetechniques. CRC, Boca Raton

19. Selvam MD, Senthil P, Sivaram NM (2017) Parametric optimisa-tion for surface roughness of AISI 4340 steel during turning undernear dry machining condition. Int J Mach Mach Mater 19(6):554

20. PerkinElmer 2011 The TGA Noack test for the assessment of engineoil volatility. In Application note—thermal analysis : United States

21. Osama M, Walvekar R, Khalid M, Rasheed AK, Wong WY, GuptaTCSM (2018) Physical properties optimization of POME-ground-nut-naphthenic based graphene nanolubricant using response sur-face methodology. J Clean Prod 193:277–289

22. Olden JD, Joy MK, Death RG (2004) An accurate comparison ofmethods for quantifying variable importance in artificial neural net-works using simulated data. Ecol Model 178(3):389–397

23. Schuchardt U, Sercheli R, Vargas RM (1998) Transesterification ofvegetable oils: a review. J Braz Chem Soc 9:199–210

24. Lee K, Hwang Y, Cheong S, Kwon L, Kim S, Lee J (2009)Performance evaluation of nano-lubricants of fullerene nanoparti-cles in refrigeration mineral oil. Curr Appl Phys 9(2, Supplement):e128–e131

25. Rashmi W, Ismail AF, Sopyan I, Jameel AT, Yusof F, Khalid M,Mubarak NM (2011) Stability and thermal conductivity enhance-ment of carbon nanotube nanofluid using gum arabic. J ExpNanosci 6(6):567–579

26. Rasheed AK, Khalid M, Rashmi W, Gupta TCSM, Chan A (2016)Graphene based nanofluids and nanolubricants—review of recentdevelopments. Renew Sust Energ Rev 63:346–362

27. Ghadimi A, Saidur R,Metselaar HSC (2011) A review of nanofluidstability properties and characterization in stationary conditions. IntJ Heat Mass Transf 54(17–18):4051–4068

28. Stachowiak, G.W. and A.W. Batchelor, Chapter 2. Physical proper-ties of lubricants, in engineering tribology (third edition). In:G.W.S.W. Batchelor, Editor. 2006, Butterworth-Heinemann:Burlington. pp. 11–50.

29. Rasheed AK, Khalid M, Walvekar R, Gupta TCS, Chan A (2016)Study of graphene nanolubricant using thermogravimetric analysis.J Mater Res 31(13):1939–1946

30. Adhvaryu A, Erhan SZ, Liu ZS, Perez JM (2000) Oxidation kineticstudies of oils derived from unmodified and genetically modifiedvegetables using pressurized differential scanning calorimetry andnuclear magnetic resonance spectroscopy. Thermochim Acta364(1–2):87–97

31. Sharma BK, Adhvaryu A, Perez JM, Erhan SZ (2008) Effects ofhydroprocessing on structure and properties of base oils usingNMR. Fuel Process Technol 89(10):984–991

32. Alves SM, Barros BS, Trajano MF, Ribeiro KSB, Moura E (2013)Tribological behavior of vegetable oil-based lubricants with nano-particles of oxides in boundary lubrication conditions. Tribol Int 65:28–36

33. Williamson, B.P. and J.C. Bell 1996 The effects of engine oil rhe-ology on the oil film thickness andwear between a Cam and Rockerfollower. SAE International.

34. Fernández-Abia AI, Barreiro J, Lacalle LNL, Martínez S (2011)Effect of very high cutting speeds on shearing, cutting forces androughness in dry turning of austenitic stainless steels. Int J AdvManuf Technol 57(1):61–71

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