# Performance Evaluation ofJatropha andPongamia Oil Based Environmentally Friendly Cutting Fluids for Turning AA 6061

Authors: T. P. Jeevan; S. R. Jayaram
Journal: Advances in Tribology
 (2018)

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## Abstract

Owing to the desirable properties of vegetable oils as cutting fluids, an attempt is made to explore the potentiality of plentifully available vegetable oils as a cutting fluid for turning AA 6061. Two nonedible vegetable oils,Jatropha andPongamia, in their chemically modified (epoxidized) versions are used as straight cutting fluids. Cutting fluids are introduced to the machining zone with the aid of Minimal Quantity Lubrication (MQL) method. Taguchi’s technique of orthogonal arrays is used to develop an effective design of experiments. The results obtained under epoxidized versions ofJatropha andPongamia oils are compared with the results of mineral oil in terms of cutting forces and surface roughness. Experimental observations and statistical analysis show that, compared to mineral oil, the modified versions of vegetable oil-based cutting fluids are more effective in reducing the cutting forces and increasing surface finish. It is also observed that the modifiedPongamia oil showed lesser flank wear compared to the other two tested oils.


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## Body

## 1. Introduction

The application of cutting fluids in machining was first reported by F. Taylor in 1894 who observed that up to 33% increase in cutting speed can be achieved without affecting the life of cutting tool by applying large amounts of water as cutting fluid to the machining zone. Cutting fluids are extensively used in the machining process to carry away heat generated at machining zone, lubricate and take away chips, and prevent corrosion during machining operations [1]. Cutting fluids results in improved tool life, work quality, effective chip management, and reduced process variability. Thus, utilization of cutting fluids is increasing in the metal cutting operations [2]. Major share of cutting fluids being used across the globe are petroleum-based oils. Enormous use of petroleum-based oils has had a lot of negative environmental and health-related consequences like skin diseases [3]. Petroleum-based lubricants in 2016 have increased tremendously on high global consumption, showing at least 1% annual increments with 13,726 million tons of oil equivalent [4]. This has therefore, made another viewpoint of bad impact on the environmental pollution and the danger of large loss proportion (13–50%) of the lubricants in the aquatic and terrestrial ecosystems, including continuous depletion of global energy and natural resources, prevail [5]. The increasing consciousness for green manufacturing globally and consumer focus on environmentally friendly products has put increased pressure on industries to minimize the use of petroleum-based cutting fluids [6]. The demand for biodegradable and eco-friendly cutting fluids has opened an avenue for using vegetable oils as a potential alternative to petroleum-based cutting fluids [7]. In this regard, vegetable oils are emerging as potential replacements to conventional cutting fluids [8].Vegetable oil-based cutting fluids are highly biodegradable, eco-friendly, renewable, less toxic, high flash point, low volatility, high viscosity index, wide production possibilities, and economical in the waste management [9–12]. Vegetable oils primarily consist of triglycerides; [13–15] the triglycerol structure of vegetable oil makes it a strong competitor as a base stock for lubricants and functional fluids [16]. Vegetable oils are triglycerides in which C18 carboxylic acids are dominant. Some of the fatty acids derived from these glycerides are unsaturated; those typically contain stearic, oleic, linoleic, and linolenic acids in varying amounts. Three of these are unsaturated acids, namely, oleic (18 : 1), linoleic (18 : 2), and linolenic (18 : 3).The use of edible vegetable oil to produce lubricants and cutting fluids is not feasible in view of the big gap in demand and supply of such oil. Hence, nonedible vegetable oils are finding importance due to their abundance and also, this would save large quantities of edible oils which are in great demand. The forecast for eco-friendly lubricants for next 10–15 years is a worldwide volume share of approximately 15% and in some regions up to 30% [17].Many metal cutting processes are tested by researchers to employ the vegetable oil-based fluids as metal cutting oils and observed a better performance. TheJatropha oil proved to be the best one for being used in the machining AA7050-T7451 [19]. The coconut oil is impressive in reducing surface roughness at higher values of feed and depth of cut while turning hardened AISI 52100 [20] and also showed an improved tool life for turning of AISI 304 [21]. The significant reduction in tool wear, cutting force, better dimensional accuracy, and surface roughness by MQL and a favourable change in chip tool and work tool interaction are observed while turning AISI 1060 [22] and AISI 4340 [23] steel using vegetable oils. The reduction in coefficient of friction is observed while machining mild steel, brass, and aluminum under different cutting conditions using Palm kernel oil as cutting fluid [24]. A formulated cutting fluid of Canola with 8% extreme pressure additive showed lower turning forces, tool wear, and surface roughness values for turning AISI 304L [25]. Sunflower and Canola oil based cutting fluids [26] generate better surface finish and produce lower cutting and feed forces while turning AISI 304 L. A novel cutting fluid was developed from nonedible Neem [27] performed better with respect to temperature rise at tip, cutting forces, tool wear, and surface roughness. The modifiedJatropha oil [11] showed the lowest values of cutting force, cutting temperature, and surface roughness, with a prolonged tool life and less tool wear. Coconut oil with 0.5% nanoparticle inclusions resulted in improved machining performance compared to Sesame oil and Canola oil [28]. The demand for a balance in meeting both the technological and environmental requirements of a new cutting fluid for machining process forms the basis of this research. In this regard, nonedible vegetable oil modifiedJatropha (MJO) and modifiedPongamia (MPO) are experimentally investigated and compared with mineral oil (MO) based on Taguchi’s design of experiments, for their sustainability as metal cutting fluids during turning AA6061, using minimum quantity lubrication technique (MQL). MQL has proven to be a better alternative to a flood coolant system in which a mixture of air and cutting fluid is applied to the cutting zone. It has a better reach ability as a result of the high pressure [29].


## 2. Materials and Methods

### 2.1. Formulation of the Vegetable Oil-Based Cutting Fluids

Biofuels have already been accepted around the world for their advantages over conventional petroleum fuels, including the opportunity for energy independency. Now, similar growth is expected for biolubricants, which are derived from renewable vegetable oils for different applications [30].Jatropha andPongamia based cutting fluids have a huge potential owing to their abundant availability, renewability, and biodegradability. However, the challenges with these oils in meeting lubricant performance is their low oxidative stability [31]; they cannot be used in their raw form. Hence, they are chemically modified by the process of epoxidation. Epoxidation of fatty acids is a reaction of a carbon–carbon double bond with active oxygen, which results in the addition of an oxygen atom, converting the original double bond into a three membered epoxide (oxirane) ring (Figure 1). In epoxidation process a known volume of oil is blended with formic/acetic acid and hydrogen peroxide. The reaction takes place for 14 hours, with 7 hours of titration in the presence of formic acid and hydrogen peroxide and 7 hours of constant agitation in the temperature range of 50°C to 60°C, thus removing double bonds in the fatty acid chain of the oil. Epoxidized vegetable oil produces flexibility and elasticity due to the presence of the epoxy ring in the backbone chain [32]. The changes in the properties of vegetable oil after chemical modification can be identified in Table 1. The iodine value of oil confirms the process of epoxidation.Table 1
Physicochemical properties of vegetable oils, their modified versions, and mineral oil.
Properties MJO MPO MO Kinematic viscosity @ 40°C (cSt) 11.22 20.15 33.00 Viscosity index 225.36 219.00 185.21 Saponification value (mg KOH g−1) 203.80 198.00 - Total acid value (mg KOH g−1) 00.05 0.13 01.75 Flash point (0°C) 180.00 190.00 160.00 Pour point (0°C) −03.00 −02.00 −7.00 Iodine value (mg l g−1) 22.00 21.41 6.50Figure 1
Mechanism of epoxidation reaction for vegetable oil [18].


### 2.2. Experimental Design by Taguchi Technique and Machining Conditions

In this study, AA 6061 having a Vickers hardness of 107 was used as a workpiece material, which had a dimension of Ø 45 mm × 200 mm. The measured values of chemical composition of AA 6061 in percentage of weight are given in Table2. The machining parameters as per the recommendations from the tool manufacturer are selected. Spindle speed, feed, depth of cut, and type of cutting fluid are considered as machining parameters. The type of cutting fluid used is considered as one of the demanding input parameters while designing the experiments. Accordingly, four input parameters (the type of cutting fluid, spindle speed, feed rate, and depth of cut), and for each parameter three levels, were assumed (Table 3). For four factors, three-level experiments, Taguchi had specified L27 orthogonal array for experimentation. The data obtained from the trials conducted as per L27 array experimentation was recorded and further analyzed. Table 3 shows the parameters and their levels considered for the experiments. The experimental setup is shown in Figure 2.Table 2
Compositions of AA6061.
Contents Cu Mg Si Fe Mn Ti Zn Cr Al % of weight 0.263 0.902 0.403 0.135 0.076 0.022 0.011 0.153 RemainingTable 3
Machining conditions.
Machine Engine lathe-LB-20 Work piece material AA 6061 Process parameters Spindle speed (rpm) 800, 1270, and 1600 Feed (mm/rev) 0.100, 0.175, and 0.250 Depth of cut (mm) 0.5, 1.0, and 1.5 Cutting tool Material Carbide cutting tool (uncoated) Tool holder High speed steel (HSS) Tool nose radius (mm) 0.4 Cutting tool edge length (mm) 9 Cutting fluids MJO, MPO, and MO Cutting fluid supply MQL method (3 to 4 bars)Figure 2
Experimental setup.


### 2.3. Cutting Force and Surface Roughness Measurement

The cutting force influences the deformation of the machined work piece, its dimensional accuracy, chip formation, tool wear, surface roughness, and stability of the machining system. Machining parameters affecting cutting forces include depth of cut, cutting speed, and type of cutting fluid. Hence, the study of cutting force as a quality characteristic and the influence of cutting fluid on the cutting force is important. The cutting force generated during machining is measured with a strain gauge type lathe tool dynamometer at different speeds and feeds under modified vegetable oil-based cutting fluids. Surface roughness is a significant design specification that is known to have considerable influence on properties such as wear resistance and fatigue strength. Surface roughness(Ra) of the machined surface is measured with a SURFCOM FLEX-50. The surface roughness values are measured by moving the stylus along the axis of turned workpiece over the machined surface as shown in Figure 3.Figure 3
Measuring surface roughnesses with stylus on workpiece.


### 2.4. Tool Wear Measurement

Tool wear was measured using educational optics transverse microscope which is modified into Tool Maker’s Microscope having least count of 1μm. Tool inserts were withdrawn after each continuous cut and were studied under Tool Maker’s Microscope for the wear pattern and width of the flank wear. Flank wear most commonly results from abrasive wear of the cutting edge against the machined surface. Flank wear is measured in terms of average wear land size VB.


## 2.1. Formulation of the Vegetable Oil-Based Cutting Fluids

Biofuels have already been accepted around the world for their advantages over conventional petroleum fuels, including the opportunity for energy independency. Now, similar growth is expected for biolubricants, which are derived from renewable vegetable oils for different applications [30].Jatropha andPongamia based cutting fluids have a huge potential owing to their abundant availability, renewability, and biodegradability. However, the challenges with these oils in meeting lubricant performance is their low oxidative stability [31]; they cannot be used in their raw form. Hence, they are chemically modified by the process of epoxidation. Epoxidation of fatty acids is a reaction of a carbon–carbon double bond with active oxygen, which results in the addition of an oxygen atom, converting the original double bond into a three membered epoxide (oxirane) ring (Figure 1). In epoxidation process a known volume of oil is blended with formic/acetic acid and hydrogen peroxide. The reaction takes place for 14 hours, with 7 hours of titration in the presence of formic acid and hydrogen peroxide and 7 hours of constant agitation in the temperature range of 50°C to 60°C, thus removing double bonds in the fatty acid chain of the oil. Epoxidized vegetable oil produces flexibility and elasticity due to the presence of the epoxy ring in the backbone chain [32]. The changes in the properties of vegetable oil after chemical modification can be identified in Table 1. The iodine value of oil confirms the process of epoxidation.Table 1
Physicochemical properties of vegetable oils, their modified versions, and mineral oil.
Properties MJO MPO MO Kinematic viscosity @ 40°C (cSt) 11.22 20.15 33.00 Viscosity index 225.36 219.00 185.21 Saponification value (mg KOH g−1) 203.80 198.00 - Total acid value (mg KOH g−1) 00.05 0.13 01.75 Flash point (0°C) 180.00 190.00 160.00 Pour point (0°C) −03.00 −02.00 −7.00 Iodine value (mg l g−1) 22.00 21.41 6.50Figure 1
Mechanism of epoxidation reaction for vegetable oil [18].


## 2.2. Experimental Design by Taguchi Technique and Machining Conditions

In this study, AA 6061 having a Vickers hardness of 107 was used as a workpiece material, which had a dimension of Ø 45 mm × 200 mm. The measured values of chemical composition of AA 6061 in percentage of weight are given in Table2. The machining parameters as per the recommendations from the tool manufacturer are selected. Spindle speed, feed, depth of cut, and type of cutting fluid are considered as machining parameters. The type of cutting fluid used is considered as one of the demanding input parameters while designing the experiments. Accordingly, four input parameters (the type of cutting fluid, spindle speed, feed rate, and depth of cut), and for each parameter three levels, were assumed (Table 3). For four factors, three-level experiments, Taguchi had specified L27 orthogonal array for experimentation. The data obtained from the trials conducted as per L27 array experimentation was recorded and further analyzed. Table 3 shows the parameters and their levels considered for the experiments. The experimental setup is shown in Figure 2.Table 2
Compositions of AA6061.
Contents Cu Mg Si Fe Mn Ti Zn Cr Al % of weight 0.263 0.902 0.403 0.135 0.076 0.022 0.011 0.153 RemainingTable 3
Machining conditions.
Machine Engine lathe-LB-20 Work piece material AA 6061 Process parameters Spindle speed (rpm) 800, 1270, and 1600 Feed (mm/rev) 0.100, 0.175, and 0.250 Depth of cut (mm) 0.5, 1.0, and 1.5 Cutting tool Material Carbide cutting tool (uncoated) Tool holder High speed steel (HSS) Tool nose radius (mm) 0.4 Cutting tool edge length (mm) 9 Cutting fluids MJO, MPO, and MO Cutting fluid supply MQL method (3 to 4 bars)Figure 2
Experimental setup.


## 2.3. Cutting Force and Surface Roughness Measurement

The cutting force influences the deformation of the machined work piece, its dimensional accuracy, chip formation, tool wear, surface roughness, and stability of the machining system. Machining parameters affecting cutting forces include depth of cut, cutting speed, and type of cutting fluid. Hence, the study of cutting force as a quality characteristic and the influence of cutting fluid on the cutting force is important. The cutting force generated during machining is measured with a strain gauge type lathe tool dynamometer at different speeds and feeds under modified vegetable oil-based cutting fluids. Surface roughness is a significant design specification that is known to have considerable influence on properties such as wear resistance and fatigue strength. Surface roughness(Ra) of the machined surface is measured with a SURFCOM FLEX-50. The surface roughness values are measured by moving the stylus along the axis of turned workpiece over the machined surface as shown in Figure 3.Figure 3
Measuring surface roughnesses with stylus on workpiece.


## 2.4. Tool Wear Measurement

Tool wear was measured using educational optics transverse microscope which is modified into Tool Maker’s Microscope having least count of 1μm. Tool inserts were withdrawn after each continuous cut and were studied under Tool Maker’s Microscope for the wear pattern and width of the flank wear. Flank wear most commonly results from abrasive wear of the cutting edge against the machined surface. Flank wear is measured in terms of average wear land size VB.


## 3. Results and Discussion

Experimentally determined values of cutting force and surface roughness values (Table4) are used to investigate the performance of vegetable based cutting fluids. Statistical analyses of experimental results obtained are carried out using MINITAB 17; statistical package established on Taguchi’s principle is used for analysis.Table 4
Cutting force and surface roughness for interfamily comparison.
Trialnumber Cutting fluids Depths of cut (mm) Feed rates(mm/rev) Spindle speeds (rpm) Cutting force (N) Surface roughness (µm) 1 MJO 0.5 0.100 0800 75 0.8 2 MJO 0.5 0.175 1270 105 0.9 3 MJO 0.5 0.250 1600 80 1.4 4 MJO 1.0 0.100 1270 145 0.7667 5 MJO 1.0 0.175 1600 215 0.8333 6 MJO 1.0 0.250 0800 345 1.3667 7 MJO 1.5 0.100 1600 200 0.7333 8 MJO 1.5 0.175 0800 350 1.2 9 MJO 1.5 0.250 1270 435 1 10 MPO 0.5 0.100 0800 70 0.8 11 MPO 0.5 0.175 1270 100 0.8 12 MPO 0.5 0.250 1600 145 1.6 13 MPO 1.0 0.100 1270 140 0.8 14 MPO 1.0 0.175 1600 235 1 15 MPO 1.0 0.250 0800 330 1.1 16 MPO 1.5 0.100 1600 215 0.8 17 MPO 1.5 0.175 0800 365 1 18 MPO 1.5 0.250 1270 420 1.4 19 MO 0.5 0.100 0800 75 1.033 20 MO 0.5 0.175 1270 110 1.1 21 MO 0.5 0.250 1600 145 1.6 22 MO 1.0 0.100 1270 155 0.7 23 MO 1.0 0.175 1600 230 1 24 MO 1.0 0.250 0800 340 1.5 25 MO 1.5 0.100 1600 210 1.1 26 MO 1.5 0.175 0800 345 1.2 27 MO 1.5 0.250 1270 315 1.4


### 3.1. Analysis of S/N and ANOVA

The statistical analysis is carried out to find the process parameters which extremely affect the quality characteristics (cutting force and surface roughness) and to identify the favourable level of each process parameter which results in the least cutting force and surface roughness.TheS/N ratio values for cutting force and surface roughness are obtained using the equation shown below and, for optimizing the process in this study, the smaller the S/N (dB) ratio, the better the characteristic.(1)SN=-10log10⁡1n∑i=1nyi2,where n is the number of measurements in a trial/row and yi is the ith measured value in a run. It includes the effect of noise as well.From the S/N ratio analysis (Figure4), optimal turning parameters for the cutting force are 0.5 mm of depth of cut, 0.1 mm/rev of feed rate, and 1600 rpm of spindle speed. MJO performed optimally followed by MPO and MO. The optimal turning parameters for surface roughness as obtained from main effect plot of the S/N ratio (Figure 5) are found to be 1.5 mm of depth of cut, 0.175 mm/rev of feed rate, and 800 rpm of spindle speed. MPO performed optimally in improving the machined surface quality followed by MJO and MO. Hence, both vegetable oil-based cutting fluids performed well compared to mineral oil with respect to cutting force and surface roughness. The lower cutting forces of vegetable oils can be attributed to better lubricity, higher viscosity index, and better thermal conductivity compared to mineral oils. This is because the modified version has more resistance to molecular breakdown, or a molecular rearrangement at a higher temperature, due to which the presence or absence of oxygen molecules was improved [7]. Previous studies also confirmed that, chemically modified vegetable oil exhibited better lubrication ability and stronger adsorption film onto metallic surface [33]. The better performance with respect to surface roughness is due to the fact that the longer carbon chains of vegetable oil corresponded to a stronger adsorption film which enhanced the surface quality [34].Figure 4
Main effects plot for cutting force.Figure 5
Main effects plot for surface roughness.The effect of viscosity, speed, and feed on response parameters, that is, cutting force and surface roughness, is measured using ANOVA. From Tables5 and 6 it is observed that cutting fluid, speed, depth of cut, and feed rate influence the cutting force by 0.1%, 5%, 63%, and 28%, respectively. Effects of cutting fluid, speed, depth of cut, and feed rate on surface roughness is found to be 10.65%, 5.73%, 4.13%, and 61.68%, respectively.Table 5
ANOVA interfamily cutting force.
Source of variance DOF Sum of squares % contribution Viscosity 2 363 0.098066 Depth 2 235524 63.62814 Feed 2 104080 28.1178 Speed 2 21446 5.793758 V i s c o s i t y ∗ D e p t h 4 837 02.999 V i s c o s i t y ∗ F e e d 4 131 0.22612 V i s c o s i t y ∗ S p e e d 4 1781 0.03539 Error 6 5994 Total 26 370157Table 6
ANOVA interfamily surface roughness.
Source of variance DOF Sum of squares % contribution Viscosity 2 0.23704 10.658 Depth 2 0.09188 04.131 Feed 2 1.37208 61.683 Speed 2 0.12748 05.732 V i s c o s i t y ∗ D e p t h 4 0.12314 05.537 V i s c o s i t y ∗ F e e d 4 0.00981 00.441 V i s c o s i t y ∗ S p e e d 4 0.04754 02.1375 Error 6 0.21509 Total 26 2.22405


### 3.2. Regression Analysis

Multiple regression analysis on the obtained data is done using Minitab 17. The independent variables are viscosity of cutting fluids, spindle speeds, feed rates, and depths of cut. The dependent variables are cutting force and surface roughness. Separate regression models are built for predicting cutting force and surface roughness. The regression equations obtained are(2)Fc=-62.2905+0.474026Viscosity+216.66Depth+940.741Feed-0.08620SpeedRa=0.12240+0.034549Viscosity-0.1036Depth+3.531Feed+5.844e-005Speed.R-Sq values for cutting force and surface roughness are 93% and 78%, respectively, whereR-Sq is correlation coefficient and should be between 0.8 and 1 in multiple linear regression analyses [35]. It provides a correlation between the experimental and predicted results. The comparisons of actual and predicted values of the observed parameters, that is, cutting force and surface roughness, from the regression models are depicted in Figures 6 and 7. And it is seen that the cutting force and surface roughness observed experimentally and the predicted values by the regression models built are close.Figure 6
Actual and predicted values of cutting force.Figure 7
Actual and predicted values of surface roughness.The confirmation tests are conducted for that particular run. Using the regression model, the values of cutting force and surface roughness are predicted for randomly chosen trials. The results of confirmation experiments in Tables7 and 8 show the difference between the predicted values and actual values. For reliable statistical analyses, error values must be smaller than 20%. The values of both the sets are compared and percentage errors are calculated. The maximum error of 11.39% is observed which is found within an acceptable range.Table 7
Confirmation tests for cutting forces.
Cutting fluids Depths of cut (mm) Feed rates (mm/rev) Speed(rpm) Predicted forceFc (N) Experimental force,Fc (N) % error MJO 1 0.175 800 253.39 235 7.821 MPO 0.5 0.175 1270 106.64 100 6.645 MO 1.5 0.25 800 434.47 465 6.563Table 8
Confirmation tests for surface roughness.
Cutting fluids Depths of cut (mm) Feed rates(mm/rev) Speed(rpm) PredictedRa (µm) ExperimentalRa (µm) % error MJO 0.5 0.1 1600 0.761 0.8 4.8186 MPO 1 0.25 800 1.345 1.3 3.5065 MO 1.5 0.175 1270 1.063296 1.2 11.392


### 3.3. Tool Wear Behaviour

It is observed that modifiedPongamia oil showed lesser tool wear (264.59 μm) for selected cutting parameters followed by mineral oil (558.15 μm) and modifiedJatropha oil (577.51 μm). Figure 8 shows SEM images of tool flank wear for different oil samples. From Figures 8(a) and 8(b) it is clearly visible that wear of cutting tool is due to abrasion between tool and workpiece in the presence of vegetable oil as cutting fluids. In Figure 8(c), attrition or peeling away of the carbide insert is observed for mineral oil based cutting fluid. The chemical modification processes significantly increase the polar functionality of vegetable oils. The interactions between ester chains in bothJatropha andPongamia molecules became stronger, hence increasing the adsorption potential on the metal surface. Thus, tool wear in modified versions of vegetable oils was reduced due to the polar structure of oil samples by forming a protective layer at the contact surfaces that reduced wear and friction [36].Figure 8
SEM images for flank wear.
(a)
Jatropha (b)
Pongamia (c)
Mineral oil


## 3.1. Analysis of S/N and ANOVA

The statistical analysis is carried out to find the process parameters which extremely affect the quality characteristics (cutting force and surface roughness) and to identify the favourable level of each process parameter which results in the least cutting force and surface roughness.TheS/N ratio values for cutting force and surface roughness are obtained using the equation shown below and, for optimizing the process in this study, the smaller the S/N (dB) ratio, the better the characteristic.(1)SN=-10log10⁡1n∑i=1nyi2,where n is the number of measurements in a trial/row and yi is the ith measured value in a run. It includes the effect of noise as well.From the S/N ratio analysis (Figure4), optimal turning parameters for the cutting force are 0.5 mm of depth of cut, 0.1 mm/rev of feed rate, and 1600 rpm of spindle speed. MJO performed optimally followed by MPO and MO. The optimal turning parameters for surface roughness as obtained from main effect plot of the S/N ratio (Figure 5) are found to be 1.5 mm of depth of cut, 0.175 mm/rev of feed rate, and 800 rpm of spindle speed. MPO performed optimally in improving the machined surface quality followed by MJO and MO. Hence, both vegetable oil-based cutting fluids performed well compared to mineral oil with respect to cutting force and surface roughness. The lower cutting forces of vegetable oils can be attributed to better lubricity, higher viscosity index, and better thermal conductivity compared to mineral oils. This is because the modified version has more resistance to molecular breakdown, or a molecular rearrangement at a higher temperature, due to which the presence or absence of oxygen molecules was improved [7]. Previous studies also confirmed that, chemically modified vegetable oil exhibited better lubrication ability and stronger adsorption film onto metallic surface [33]. The better performance with respect to surface roughness is due to the fact that the longer carbon chains of vegetable oil corresponded to a stronger adsorption film which enhanced the surface quality [34].Figure 4
Main effects plot for cutting force.Figure 5
Main effects plot for surface roughness.The effect of viscosity, speed, and feed on response parameters, that is, cutting force and surface roughness, is measured using ANOVA. From Tables5 and 6 it is observed that cutting fluid, speed, depth of cut, and feed rate influence the cutting force by 0.1%, 5%, 63%, and 28%, respectively. Effects of cutting fluid, speed, depth of cut, and feed rate on surface roughness is found to be 10.65%, 5.73%, 4.13%, and 61.68%, respectively.Table 5
ANOVA interfamily cutting force.
Source of variance DOF Sum of squares % contribution Viscosity 2 363 0.098066 Depth 2 235524 63.62814 Feed 2 104080 28.1178 Speed 2 21446 5.793758 V i s c o s i t y ∗ D e p t h 4 837 02.999 V i s c o s i t y ∗ F e e d 4 131 0.22612 V i s c o s i t y ∗ S p e e d 4 1781 0.03539 Error 6 5994 Total 26 370157Table 6
ANOVA interfamily surface roughness.
Source of variance DOF Sum of squares % contribution Viscosity 2 0.23704 10.658 Depth 2 0.09188 04.131 Feed 2 1.37208 61.683 Speed 2 0.12748 05.732 V i s c o s i t y ∗ D e p t h 4 0.12314 05.537 V i s c o s i t y ∗ F e e d 4 0.00981 00.441 V i s c o s i t y ∗ S p e e d 4 0.04754 02.1375 Error 6 0.21509 Total 26 2.22405


## 3.2. Regression Analysis

Multiple regression analysis on the obtained data is done using Minitab 17. The independent variables are viscosity of cutting fluids, spindle speeds, feed rates, and depths of cut. The dependent variables are cutting force and surface roughness. Separate regression models are built for predicting cutting force and surface roughness. The regression equations obtained are(2)Fc=-62.2905+0.474026Viscosity+216.66Depth+940.741Feed-0.08620SpeedRa=0.12240+0.034549Viscosity-0.1036Depth+3.531Feed+5.844e-005Speed.R-Sq values for cutting force and surface roughness are 93% and 78%, respectively, whereR-Sq is correlation coefficient and should be between 0.8 and 1 in multiple linear regression analyses [35]. It provides a correlation between the experimental and predicted results. The comparisons of actual and predicted values of the observed parameters, that is, cutting force and surface roughness, from the regression models are depicted in Figures 6 and 7. And it is seen that the cutting force and surface roughness observed experimentally and the predicted values by the regression models built are close.Figure 6
Actual and predicted values of cutting force.Figure 7
Actual and predicted values of surface roughness.The confirmation tests are conducted for that particular run. Using the regression model, the values of cutting force and surface roughness are predicted for randomly chosen trials. The results of confirmation experiments in Tables7 and 8 show the difference between the predicted values and actual values. For reliable statistical analyses, error values must be smaller than 20%. The values of both the sets are compared and percentage errors are calculated. The maximum error of 11.39% is observed which is found within an acceptable range.Table 7
Confirmation tests for cutting forces.
Cutting fluids Depths of cut (mm) Feed rates (mm/rev) Speed(rpm) Predicted forceFc (N) Experimental force,Fc (N) % error MJO 1 0.175 800 253.39 235 7.821 MPO 0.5 0.175 1270 106.64 100 6.645 MO 1.5 0.25 800 434.47 465 6.563Table 8
Confirmation tests for surface roughness.
Cutting fluids Depths of cut (mm) Feed rates(mm/rev) Speed(rpm) PredictedRa (µm) ExperimentalRa (µm) % error MJO 0.5 0.1 1600 0.761 0.8 4.8186 MPO 1 0.25 800 1.345 1.3 3.5065 MO 1.5 0.175 1270 1.063296 1.2 11.392


## 3.3. Tool Wear Behaviour

It is observed that modifiedPongamia oil showed lesser tool wear (264.59 μm) for selected cutting parameters followed by mineral oil (558.15 μm) and modifiedJatropha oil (577.51 μm). Figure 8 shows SEM images of tool flank wear for different oil samples. From Figures 8(a) and 8(b) it is clearly visible that wear of cutting tool is due to abrasion between tool and workpiece in the presence of vegetable oil as cutting fluids. In Figure 8(c), attrition or peeling away of the carbide insert is observed for mineral oil based cutting fluid. The chemical modification processes significantly increase the polar functionality of vegetable oils. The interactions between ester chains in bothJatropha andPongamia molecules became stronger, hence increasing the adsorption potential on the metal surface. Thus, tool wear in modified versions of vegetable oils was reduced due to the polar structure of oil samples by forming a protective layer at the contact surfaces that reduced wear and friction [36].Figure 8
SEM images for flank wear.
(a)
Jatropha (b)
Pongamia (c)
Mineral oil


## 4. Conclusion

The analysis of results by statistical approach based on Taguchi technique shows that the force developed during turning AA6061is said to be optimum for 0.1 mm/rev of feed rate, for 0.5 mm of depth of cut, and at 1600 rpm of speed in the presence of MJO as cutting fluid. A good surface finish is observed for MPO as cutting fluid at low speeds and MJO as cutting fluid at high speeds. Regression analysis showed that the percentage error for experimental and predicted values of cutting force and surface roughness are within an acceptable range. Thus, the modified versions ofPongamia andJatropha oil as straight cutting fluids have a great potential for use as environmentally friendly and biodegradable metal cutting fluids.



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