Multi-Response Optimization of Thermal Conductivity and Rheological Behavior in Nanoparticle-Enhanced Vegetable Oil Emulsions

Abstract

In metal cutting industries, optimizing the thermal conductivity and viscosity of vegetable oil-based cutting fluids is critical for ensuring efficient heat dissipation, effective lubrication, and sustainability, directly influencing tool life and machining performance. This study presents a comprehensive experimental analysis employing statistical methods, particularly Taguchi’s Design of Experiments, to evaluate the thermal conductivity and viscosity of Pongamia pinnata, sunflower, and coconut oil incorporated with Silicon Dioxide (SiO₂), Hexagonal Boron Nitride (hBN), and Cupric Oxide (CuO) nanoparticles across different emulsion ratios and nanoparticle volume fractions. The results revealed that Pongamia pinnata oil containing 0.5 (Vol.%) SiO₂ nanoparticles at an emulsion ratio of 1:7 achieved the maximum thermal conductivity, measured at 0.637 W/mK. Additionally, the results revealed that Pongamia pinnata oil at an emulsion ratio of 1:13 exhibited the highest viscosity of 1.33 mPa·S, confirming that both the type of cutting oil and the emulsion ratio are the primary factors influencing viscosity. Further, the ANOVA analysis for thermal conductivity and viscosity highlights that the type of cutting fluid is the dominant factor, accounting for 90.58% of the total variance in thermal conductivity and 70.47% in viscosity, each with a highly significant p-value of 0.00, underscoring its decisive impact on the stability of both properties. Overall, this research offers important guidance for the selection and formulation of vegetable oil-based emulsions with nanoparticle additives. The results support the development of advanced nano lubricants with enhanced performance, catering to the increasing requirements of diverse industrial applications.

1. Introduction

Vegetable oil-derived lubricants have gained increasing interest in recent years because of their superior boundary lubrication characteristics, along with their environmentally friendly and biodegradable nature. Pongamia pinnata, a plant-based lubricant, exhibits outstanding lubricating characteristics, offering a reduced coefficient of friction along with enhanced resistance to oxidative degradation. Synthetic- and fossil-derived lubricants are widely used in the market; however, the continued exploitation of mineral-based resources has rendered them finite in nature. In addition, their production and usage adversely impact the environment by contributing to greenhouse gas emissions, thereby intensifying environmental concerns [1]. Furthermore, Manikanta et al. [2] suggested that chemically synthesized lubricants exhibit a higher biodegradability rate; however, they are also associated with increased cost and potential toxicity. Advancements in technical and scientific solutions, such as the adoption of materials that generate minimal exhaust emissions, the use of cleaner and safer fuels, and the promotion of stable and controlled combustion, have been proposed to mitigate environmental issues arising from automotive applications.

Lubrication is a process aimed at reducing friction and wear between one or more surfaces in close contact and relative motion by introducing a lubricant between the interacting surfaces, which facilitates load transfer and supports the moving components. Lubricants are employed to reduce wear and heat generation between contacting surfaces, protect components from degradation and oxidative damage, and serve as protective media in applications such as transformers. Additionally, they function as barriers that prevent the ingress of contaminants such as dirt, dust, and moisture. Although wear and heat cannot be completely eliminated from contacting surfaces since they originate from friction, their effects can be reduced to acceptable levels through the effective use of lubricants. Therefore, effective lubrication is essential for minimizing heat generation and wear, particularly in automotive and locomotive applications. Mineral-based oils have been widely used as friction-reducing agents; however, they are finite natural resources, and their degradation adversely affects both marine and terrestrial ecosystems. It has been predicted that the ongoing consumption of mineral oil will eventually result in a shortage of this resource in the future [3,4].

To address these challenges, many researchers worldwide have focused on replacing synthetic- and fossil-based lubricants with biodegradable, cost-effective, and environmentally friendly alternatives. For sustainable development, it is essential to use lubricants that have minimal negative impact on the environment. Bio-lubricants can serve as alternatives to mineral oil-based lubricants due to their inherent functional properties and biodegradability. Research indicates that vegetable-based bio-oils are beneficial as primary raw materials in lubricant production [5,6].

Researchers have compared the properties of vegetable oil-based lubricants with those derived from mineral oils and have clearly demonstrated that bio-lubricants exhibit higher lubricity, higher flash points, lower volatility, and greater viscosity indices. Lubricants serve as the primary agents for reducing friction between moving surfaces, but their performance can be significantly enhanced through the use of additives. These additives improve oxidation stability, enhance anti-friction and wear-reducing properties, and provide effective anti-corrosion protection [7,8,9,10].

Barbosa [11] and Kosasih [12] have suggested that lubricants are substances applied between moving surfaces to reduce friction, prevent wear, inhibit corrosion, and provide cooling, playing a crucial role in enhancing operational efficiency and reliability across various industrial applications. Based on their source, lubricants can be categorized into mineral oils, synthetic oils, and bio-based lubricants. Mineral oil-based lubricants dominate global consumption due to their affordability and availability. These oils are derived from refined petroleum fractions through crude oil distillation. However, improper disposal and incomplete combustion of mineral oils lead to ecological toxicity, soil and water pollution, and the release of trace metals, raising significant environmental concerns. Additionally, the finite nature of crude oil and price volatility drive the search for sustainable alternatives [13,14]. Synthetic oils, produced through chemical engineering of modified petroleum or synthetic feedstocks, offer advantages such as excellent low-temperature fluidity, low volatility, and superior thermal and oxidative stability, making them suitable for demanding industrial processes like high-temperature forming and metal stamping. Despite their longer lifespan, challenges related to storage, additive stability, and high production energy remain, contributing to environmental concerns [15,16].

Bio-lubricants, primarily derived from vegetable oils such as castor, soybean, rapeseed, and palm, provide high lubricity, low toxicity, and renewable characteristics. Their composition of triglycerides and unsaturated fatty acids makes them excellent candidates for lubricant formulations, though their oxidative and thermal stability often require chemical or physical modification. Growing environmental awareness, legislation, and rapid industrialization, especially in the Asia–Pacific region, are driving demand for environmentally acceptable lubricants. Modern bio-lubricants enhanced with nanoparticle additives offer promising performance aligned with green chemistry and circular economy principles, with the potential to replace conventional lubricants in automotive, industrial, and agricultural sectors [16,17,18,19].

This study addresses the critical need for high-performance sustainable lubricants by conducting a comprehensive experimental analysis of vegetable oil-based emulsions. Specifically, it systematically evaluates the synergistic effects of dispersing Silicon Dioxide (SiO₂), Hexagonal Boron Nitride (hBN), and Cupric Oxide (CuO) nanoparticles into Pongamia pinnata, sunflower, and coconut base oils. A key novelty of this work lies in the application of Taguchi’s Design of Experiments (DoE) to rigorously optimize the emulsion ratio and nanoparticle volume fraction for maximum thermal conductivity and viscosity. Readers can expect a detailed statistical assessment, including ANOVA, which isolates the dominant factors influencing fluid stability and heat transfer, ultimately providing a validated framework for formulating advanced, eco-friendly cutting fluids for industrial machining applications.

2. Methodology

This study evaluates the thermal conductivity and viscosity of vegetable oil-based cutting fluid emulsions formulated from Pongamia pinnata oil, sunflower oil, and coconut oil, reinforced with Silicon Dioxide (SiO₂), hexagonal boron nitride (hBN), and Cupric Oxide (CuO) nanoparticles. Analytical-grade SiO₂, hBN, and CuO nanoparticles with an average particle size of 50 nm were procured from SRL Chemicals, India, and used as reinforcement additives. coconut oil, Pongamia pinnata oil, and sunflower oil obtained from Ganesh Oil Mills, Udupi, India, served as the base fluids. Nanoparticle-reinforced cutting fluid emulsions were prepared using ultrasonication for 60 min to ensure uniform dispersion and stability. The thermal conductivity of the formulated emulsions was measured using a KD2 Pro thermal property analyzer, while viscosity measurements were carried out using an Anton Paar Modular Compact Rheometer under controlled conditions. Figure 1 illustrates the schematic process of dispersing Pongamia pinnata (PP) oil with SiO₂ nanoparticles. Figure 2 presents the flowchart outlining the preparation and experimental procedures for evaluating the thermal conductivity and viscosity of nanoparticle-reinforced vegetable oil emulsions.

Table 1. Levels and parameters.

Parameters	Levels
	1	2	3
Cutting Fluids	PP	CC	SF
Nanoparticles	SiO2	hBN	CuO
Emulsion Ratio	1:7	1:10	1:13
NanopParticle (Vol.%)	0	0.5	1.0

summarizes the control factors and their corresponding levels considered in this study.

The base fluids utilized in this study, i.e., Pongamia pinnata, sunflower, and coconut oils, were procured from Ganesh Oil Mills, Udupi, India, and used without further purification to simulate industrial applicability. Analytical-grade nanoparticles (SiO₂, hBN, and CuO) with an average crystallite size of 50 nm and a purity of >99.5% were sourced from SRL Chemicals, Mumbai, India. The formulation of the nanofluids was carried out using a programmable ultrasonic probe homogenizer (model: UP400S, Hielscher Ultrasonics; 24 kHz, 400 W) operating at 60% amplitude with a pulse cycle of 0.5 s to prevent localized overheating. Ultrasonication was performed for a duration of 60 min to break down agglomerates and ensure a homogeneous, stable dispersion. Thermal conductivity measurements were acquired using a KD2 Pro Thermal Properties Analyzer (Decagon Devices, Pullman, WA, USA), equipped with a KS-1 single-needle sensor (1.3 mm diameter, 60 mm length) specifically calibrated for liquid fluids with an accuracy of ±5%. Rheological behavior and viscosity were characterized using an Anton Paar Modular Compact Rheometer (MCR 302), utilizing a cone-and-plate geometry (CP50-1) to ensure precise shear rate control and uniform sample shearing during measurement.

To ensure the reliability and reproducibility of the experimental data, each formulation condition (defined by the Taguchi orthogonal array) was prepared and tested in independent triplicates. The thermal conductivity and viscosity values reported in this study represent the arithmetic mean of these three distinct measurements. All measurements were conducted under controlled ambient conditions (25 °C ± 1 °C) to negate environmental thermal fluctuations, and the equipment was calibrated against standard glycerin and distilled water references prior to data acquisition.

3. Results

The findings reveal that the addition of SiO₂ nanoparticles to Pongamia pinnata-based cutting fluid significantly increased its thermal conductivity, leading to improved energy efficiency of the fluid during operation. Additionally, the study indicated that variations in the emulsion ratio of vegetable oil-based cutting fluid markedly enhanced its viscosity, which in turn provided superior lubrication, minimized wear, and improved the resulting surface finish.

Table 2. Results of thermal conductivity and viscosity under nanoparticle-enhanced vegetable oil emulsions.

Sl. No.	Cutting Fluids	Nanoparticles	Emulsion Ratio	Nanoparticle (Vol.%)	Thermal Conductivity (W/mK)	Viscosity (mPa·S)
1	PP	SiO2	1:7	0.0	0.622	0.89
2	PP	SiO2	1:7	0.5	0.637	0.91
3	PP	SiO2	1:7	1.0	0.629	0.96
4	PP	hBN	1:10	0.0	0.604	1.05
5	PP	hBN	1:10	0.5	0.621	1.10
6	PP	hBN	1:10	1.0	0.615	1.15
7	PP	CuO	1:13	0.0	0.601	1.20
8	PP	CuO	1:13	0.5	0.612	1.28
9	PP	CuO	1:13	1.0	0.608	1.33
10	CC	SiO2	1:10	0.0	0.581	0.73
11	CC	SiO2	1:10	0.5	0.603	0.77
12	CC	SiO2	1:10	1.0	0.591	0.79
13	CC	hBN	1:13	0.0	0.567	0.84
14	CC	hBN	1:13	0.5	0.579	0.86
15	CC	hBN	1:13	1.0	0.572	0.89
16	CC	CuO	1:7	0.0	0.541	0.62
17	CC	CuO	1:7	0.5	0.558	0.66
18	CC	CuO	1:7	1.0	0.549	0.69
19	SF	SiO2	1:13	0.0	0.518	0.77
20	SF	SiO2	1:13	0.5	0.529	0.80
21	SF	SiO2	1:13	1.0	0.524	0.83
22	SF	hBN	1:7	0.0	0.511	0.54
23	SF	hBN	1:7	0.5	0.522	0.57
24	SF	hBN	1:7	1.0	0.517	0.59
25	SF	CuO	1:10	0.0	0.501	0.63
26	SF	CuO	1:10	0.5	0.512	0.65
27	SF	CuO	1:10	1.0	0.505	0.69

presents the results of thermal conductivity and viscosity of various cutting fluids containing nanoparticles, evaluated under different emulsion ratios and nanoparticle concentrations.

3.1. Thermal Conductivity

Figure 3 shows the influence of cutting fluids on thermal conductivity under varying nanoparticles and constant emulsion ratios and nanoparticles (Vol.%). This suggests that Pongamia pinnata cutting fluid incorporated with SiO₂ nanoparticles significantly enhanced the thermal conductivity. Pongamia pinnata cutting fluid consistently exhibited the highest thermal conductivity across all nanoparticle types, followed by coconut oil-based cutting fluids with moderate values, while sunflower cutting fluids show the lowest thermal conductivity. This behavior is attributed to the superior inherent thermal transport properties of Pongamia pinnata oil and its stronger interaction with nanoparticles, which collectively enhance heat transfer more effectively than coconut and sunflower oils [20]. Additionally, SiO₂ nanoparticles provided the maximum improvement due to their superior dispersion stability and ability to form continuous thermal conduction pathways within the fluid, thereby reducing thermal resistance [21]. In comparison, hBN offers moderate enhancement, while CuO exhibits the least improvement, primarily due to relatively lower dispersion efficiency under identical operating conditions. Higher thermal conductivity of the cutting fluid facilitates rapid heat removal from the cutting zone, thereby minimizing thermal accumulation during machining. Consequently, the Pongamia pinnata fluid incorporated with SiO₂ cutting fluid is expected to deliver superior cooling efficiency, reduced tool temperature, enhanced tool life, and improved surface finish [22].

Figure 4 illustrated influence of nanoparticles on thermal conductivity under varying cutting fluids and constant emulsion ratio and nanoparticle (Vol.%). A substantial enhancement of thermal conductivity is observed when SiO₂ nanoparticles are dispersed in Pongamia pinnata cutting fluid. SiO₂ nanoparticles demonstrate the enhancement in thermal conductivity across all cutting fluid formulations, followed by hBN nanoparticles with intermediate improvement, whereas CuO nanoparticles contribute the least to thermal conductivity enhancement. For all the investigated cutting fluids, thermal conductivity enhancement follows the same order, SiO₂ > hBN > CuO, highlighting the governing role of nanoparticle properties in heat transfer behavior. The superior performance of SiO₂ is attributed to its excellent dispersion stability and its capability to establish effective heat transfer networks within the base fluid, which minimizes thermal resistance [23]. The Pongamia pinnata cutting fluid incorporated with SiO₂ nanoparticles exhibited the highest thermal conductivity, whereas the sunflower oil incorporated with CuO combination shows the lowest thermal conductivity among the tested fluids. Accordingly, the Pongamia pinnata cutting fluid incorporated with SiO₂ nanoparticles is anticipated to offer improved cooling performance, lower cutting tool temperatures, extended tool life, and superior surface quality during machining operations [20,23].

Figure 5 demonstrated the influence of nanoparticles (Vol.%) on thermal conductivity under varying cutting fluids, a constant emulsion ratio and nanoparticles. This suggested that nnanoparticles(Vol.%) slightly influence the thermal conductivity of the cutting fluid. Across all cutting fluids, thermal conductivity exhibits a progressive increase as the nanoparticle volume fraction rises from 0 to 0.5 and further to 1.0 (Vol.%), which is attributed to the increased population of solid nanoparticles that enhance heat transfer via intensified particle-to-particle and particle-to-fluid interactions and promote the development of efficient thermal conduction networks within the fluid. The findings demonstrated that a slight increase in nanoparticle concentration leads to a consistent improvement in thermal conductivity across all cutting fluids; however, the extent of enhancement is highly dependent on the base fluid, with Pongamia pinnata cutting fluids exhibiting superior performance compared to coconut and sunflower oil under identical conditions.

Figure 6 demonstrated the influence of the emulsion ratio on thermal conductivity under varying nanoparticles (Vol.%), constant cutting fluids and nanoparticles. The graph indicates that the emulsion ratio has a negligible effect on the thermal conductivity of the cutting fluid, exerting only a marginal influence on heat transfer characteristics. For all cutting fluids, thermal conductivity steadily increases with the rise in the nanoparticle volume fraction from 0 to 0.5 and up to 1.0 (Vol.%). This enhancement is due to the greater number of solid nanoparticles, which improve heat transfer through strengthened particle–fluid interactions, and facilitate the formation of effective thermal conduction pathways within the fluid [24].

The inclusion of SiO₂ nanoparticles in Pongamia pinnata-based cutting fluid markedly improves its thermal conductivity, resulting in enhanced overall energy efficiency of the fluid [22,25]. The distinct hierarchy in thermal conductivity observed among the base oils, with Pongamia pinnata consistently outperforming coconut and sunflower oils, can be fundamentally attributed to the variations in their fatty acid profiles and molecular polarity. Pongamia pinnata is rich in unsaturated fatty acids, specifically oleic and linoleic acids, which possess polar double bonds. This inherent polarity facilitates a stronger interfacial interaction between the fluid molecules and the surface of the dispersed nanoparticles. A stronger fluid–particle interface reduces the Kapitza resistance (thermal boundary resistance), thereby allowing for more efficient phonon transport from the solid nanoparticle lattice to the surrounding liquid medium. In contrast, the saturated fatty acid chains predominant in coconut oil (primarily lauric acid) and the specific unsaturation of sunflower oil exhibit lower polarity, resulting in a relatively higher interfacial thermal resistance and, consequently, lower bulk thermal conductivity.

Table 3. ANOVA of thermal conductivity (W/mK).

Source	DF	Seq SS	Adj SS	Adj MS	F	P	P%
A	2	10.96	0.78	0.39	23.44	0.00	90.58
B	2	0.78	10.9	5.48	325.5	0.00	6.52
C	2	0.03	0.03	0.01	0.89	0.45	0.24
D	2	0.208	0.208	0.10	6.18	0.03	1.71
A × D	4	0.001	0.001	0.00	0.02	0.99	0.01
B × D	4	0.005	0.005	0.001	0.08	0.98	0.04
C × D	4	0.004	0.004	0.001	0.07	0.98	0.037
RSE	6	0.10	0.10	0.01			0.83
Total	26	12.1

where A = cutting fluids, B = nanoparticles, C = emulsion ratio, D= nanoparticle (Vol.%).

demonstrates the ANOVA for the thermal conductivity. The ANOVA results indicate that the type of cutting fluid is the primary factor influencing thermal conductivity, accounting for 90.58% of the total variance with a highly significant p-value of 0.00, while nanoparticles contributed 6.52% to the variance. The emulsion ratio and nanoparticle volume (Vol.%) contributed only marginally to the thermal conductivity.

The main effect plot of means and SN ratio (Figure 7 and Figure 8) indicates that the type of cutting fluid and the choice of nanoparticles are the primary factors influencing thermal conductivity, whereas the nanoparticle volume fraction (Vol.%) and emulsion ratio exerts a comparatively minor effect.

Moreover, the interaction plot (Figure 9) further demonstrated that the type of cutting fluid plays a decisive role in governing thermal conductivity, with Pongamia pinnata cutting fluid exhibiting superior thermal conductivity.

Figure 10, Figure 11, Figure 12 and Figure 13 illustrate the contour plots of thermal conductivity. The contour plot (Figure 10) revealed that the optimal combination of Pongamia pinnata cutting fluid incorporated with SiO₂ nanoparticles resulted in the highest thermal conductivity. This confirms that the Pongamia pinnata cutting fluid exhibits the highest thermal conductivity. Moreover, Figure 11 illustrates that nanoparticle concentrations in the range of 0.1 to 0.9 (Vol.%) yield superior thermal conductivity. Furthermore, Figure 12 indicated that a nanoparticle concentration in the range of 0.45 to 0.9 (Vol.%) combined with Pongamia pinnata cutting fluid yields optimal thermal conductivity. Additionally, Figure 13 demonstrated that the cutting fluid incorporated with SiO₂ nanoparticles at an emulsion ratio of 1:7 exhibits optimum thermal conductivity.

Figure 14 illustrates the residual plot (mean) of the thermal conductivity. The residuals align closely with the reference line in the normal probability plot, indicating that the normality assumption is well-satisfied and confirming the statistical adequacy of the thermal conductivity model. Additionally, Figure 15 represents the residual plot of the signal-to-noise (S/N) ratio for thermal conductivity. The normal probability plot demonstrates that the residuals closely follow the reference line, confirming that the normality assumption of the thermal conductivity model is adequately satisfied, with only slight deviations observed at the distribution tails.

3.2. Viscosity

Figure 16 illustrates the influence of cutting fluids on viscosity under varying nanoparticles and constant emulsion ratios and nanoparticle (Vol. %). Among the cutting base fluids, Pongamia pinnata consistently demonstrates the highest viscosity irrespective of nanoparticle type, followed by coconut oil and sunflower oil, owing to the higher molecular weight and distinct fatty acid profile of Pongamia pinnata oil, which enhances intermolecular interactions and results in increased resistance to flow [26,27].

Figure 17 presents the influence of nanoparticles on viscosity under varying cutting fluids and constant emulsion ratios and nanoparticles (Vol. %). The graph indicates that the nanoparticle type has a minimal influence on viscosity. The Pongamia pinnata oil incorporating all nanoparticles exhibits the highest viscosity among all tested combinations, indicating enhanced thickening from the synergistic interaction between the base oil properties and nanoparticle characteristics and advantageous potential for machining applications requiring improved lubrication, enhanced load-carrying capacity, and reduced tool and workpiece friction [28].

Figure 18 presents the influence of nanoparticles (Vol. %) on viscosity under varying cutting fluids and constant emulsion ratios and nanoparticles. For all cutting fluids, viscosity exhibits a systematic increase in nanoparticle volume percentage from 0 to 0.5 and further to 1 (Vol.%), attributed to the increased concentration of suspended nanoparticles that intensifies particle-to-fluid interactions, thereby elevating internal flow resistance. Among the investigated cutting fluids, Pongamia pinnata consistently exhibits the highest viscosity across all nanoparticle concentrations, followed by sunflower oil, whereas coconut oil shows the lowest viscosity, a trend predominantly governed by the intrinsic rheological behavior and fatty acid composition of the respective base oils [29].

Figure 19 illustrates the influence of the emulsion ratio on viscosity under varying nanoparticles, constant cutting fluids and nanoparticles (Vol.%). This suggest that viscosity increases steadily with an increase in the emulsion ratio from 1:7 to 1:13 for all nanoparticles (Vol.%), indicating that a higher proportion of base oil in the emulsion enhances fluid thickening; additionally, at each emulsion ratio, viscosity rises marginally as the nanoparticle concentration increases from 0 to 1.0 Vol.%, due to intensified particle–fluid interactions that elevate resistance to flow.

Regarding rheological behavior, the dominance of the base oil type in determining emulsion viscosity (70.47% contribution) is governed by the intermolecular forces and molecular weight of the triglycerides. Pongamia pinnata oil is characterized by longer carbon chain lengths and a higher average molecular weight compared to the lighter fractions found in coconut and sunflower oils. These longer chains result in increased intermolecular entanglement and van der Waals forces, which naturally manifest as higher internal resistance to flow. Furthermore, the specific triglyceride structure of Pongamia pinnata facilitates the formation of a denser lubricating film, which explains the shear-thickening tendency observed when reinforced with nanoparticles. This intrinsic high viscosity is advantageous for heavy-duty machining applications where maintaining a hydrodynamic wedge under high load is critical for tool protection.

The analysis of variance (ANOVA) for the S/N ratios of viscosity, presented in

Table 4. ANOVA table for viscosity.

Source	DF	Seq SS	Adj SS	Adj MS	F	P	P%
A	87.085	87.08	43.542	51.64	0.000	70.47	87.085
B	2.903	2.903	1.4516	1.72	0.257	2.34	2.903
C	26.007	26.00	13.003	15.42	0.004	21.04	26.007
D	2.472	2.472	1.2361	1.47	0.303	2.00	2.472
A × D	0.009	0.009	0.0022	0.00	1.000	0.007	0.009
B × D	0.011	0.011	0.0027	0.00	1.000	0.008	0.011
C × D	0.023	0.023	0.0056	0.01	1.000	0.018	0.023
RSE	5.059	5.059	0.8432			4.094	5.059
Total	123.56						123.56

, indicates that the cutting fluid is the dominant factor influencing viscosity, accounting for 70.47% of the total variation. The emulsion ratio exerts a significant influence on viscosity, contributing 21.04% to the overall variation, whereas the effects of the nanoparticle type and the nanoparticle volume fraction are comparatively minor, accounting for only 2.34% and 2.00%, respectively.

Furthermore, Figure 20 and Figure 21 illustrateates the main effects plot of means and SN ratio for viscosity, revealing that the cutting fluid type and emulsion ratio exert the most significant influence on viscosity, followed by the nanoparticle type. As the emulsion ratio increases from 1:7 to 1:13, a pronounced rise in viscosity is observed, indicating that higher emulsion ratios lead to greater viscosity.

Moreover, Figure 22 presents the interaction plot for viscosity, illustrating that the combined effect of cutting fluid type and emulsion ratio plays a significant role in governing viscosity, with Pongamia pinnata cutting fluid exhibiting the most pronounced influence. The nanoparticle (Vol.%) illustrated a slight increase in viscosity, indicating that nanoparticle addition enhances viscosity.

Figure 23, Figure 24, Figure 25 and Figure 26 illustrate the contour plots of viscosity under varying parameters. Figure 23 represents the variation in viscosity with different nanoparticles and cutting fluids. The graph indicates that the Pongamia pinnata oil CuO nanofluid exhibits the highest viscosity among the tested combinations.

Further, Figure 24 demonstrates the contour plot of viscosity for varying nanoparticle (Vol.%) and emulsion ratios. This reveals that the emulsion ratio significantly enhances viscosity, with the 1:13 emulsion ratio exhibiting the maximum viscosity.

Moreover, the contour plot in Figure 25 illustrates the combined influence of cutting fluid type and nanoparticle volume fraction on viscosity. From the plot, Pongamia pinnata consistently exhibits higher viscosity across the entire nanoparticle concentration range, as reflected by the green and dark green contours, highlighting its inherently superior thickening behavior. Coconut oil shows moderate viscosity values, transitioning from light green to pale shades as nanoparticle concentration increases. In contrast, sunflower oil indicates the lowest viscosity, with minimal sensitivity to nanoparticle addition.

Additionally, the contour plot in Figure 26 illustrates that the combined effect of a 1:13 emulsion ratio and CuO nanoparticles yields the highest viscosity among the investigated conditions.

Figure 27 illustrates the residual plot for mean viscosity, in which the residuals closely follow the reference line in the normal probability plot, signifying that the normality assumption is well-satisfied and validating the statistical adequacy of the model.

Furthermore, Figure 28 presents the residual analysis of the signal-to-noise (S/N) ratio for viscosity. The normal probability plot shows that the residuals are well-aligned with the reference line, with only minor deviations at the extremes, confirming that the normality assumption of the viscosity model is reasonably met.

4. Discussion

The experimental results demonstrate a clear hierarchy in thermal performance, with Pongamia pinnata (PP) emulsions consistently outperforming sunflower (SF) and coconut (CC) oil formulations. This behavior is fundamentally attributed to the intermolecular compatibility between the triglyceride structure of the base oil and the nanoparticle surface [20]. Pongamia pinnata, characterized by its specific fatty acid profile (rich in oleic and linoleic acids), exhibits a higher molecular density and polarity compared to coconut and sunflower oils. This polarity facilitates a more stable interfacial layer around the nanoparticles, reducing the thermal boundary resistance (Kapitza resistance) at the solid–liquid interface [26,27].

A critical finding of this study is the superior performance of Silicon Dioxide (SiO₂) nanoparticles (0.637 W/mK) compared to Hexagonal Boron Nitride (hBN) and Cupric Oxide (CuO) [14]. While metallic oxides like CuO theoretically possess high intrinsic thermal conductivity, their effectiveness in a fluid medium is strictly governed by dispersion stability. SiO₂ nanoparticles, due to their surface hydroxyl groups, are more likely to form stable steric barriers that prevent agglomeration [22]. This superior dispersion allows for the formation of extended particle-to-particle thermal percolation networks, which serve as high-speed conduits for phonon transport through the fluid [21,23]. In contrast, the relatively lower enhancement observed with CuO suggests partial agglomeration, which disrupts these conductive pathways and effectively increases the mean free path for heat transfer.

Rheological analysis confirms that viscosity is predominantly controlled by the base fluid’s molecular weight and the emulsion ratio, with nanoparticle concentration playing a secondary yet statistically measurable role [16,29]. The statistically significant impact of the emulsion ratio (21.04% contribution to variance) aligns with the Einstein viscosity equation for dilute suspensions, where the volume fraction of the dispersed phase (oil droplets) directly increases flow resistance. The application of the Einstein viscosity equation in this analysis is predicated on the relatively low concentration of nanoparticles utilized in this study (maximum 1.0 Vol.%). In this low-concentration range, the nanofluid can be approximated as a dilute suspension where the inter-particle distance is sufficiently large to minimize complex hydrodynamic interactions between particles. Consequently, the increase in viscosity can be primarily attributed to the volume fraction of the dispersed solid phase distorting the flow field of the base fluid, aligning with the fundamental principles of the Einstein model for dilute rigid sphere suspensions.

The data indicates that the addition of nanoparticles to Pongamia pinnata oil significantly enhances its bulk viscosity, particularly at higher concentrations (1.0 Vol.%). This substantial increase in viscosity is advantageous for establishing hydrodynamic lubrication regimes. Under the high-shear conditions typical of machining, a higher viscosity index helps maintain a robust fluid film between the tool and the workpiece, thereby preventing direct metal-to-metal contact and minimizing adhesive wear [28]. Furthermore, the presence of SiO₂ nanoparticles is posited to facilitate a rolling mechanism, acting as ‘nano-ball bearings’, which converts sliding friction into rolling friction, further reducing the coefficient of friction and heat generation at the cutting zone [25].

The Taguchi-based ANOVA analysis provides robust validation of the experimental design. The dominance of the cutting fluid type (90.58% contribution for thermal conductivity) underscores that while nanoparticles are effective enhancers, the baseline thermal properties of the carrier fluid remain the limiting factor [16]. The residuals for both thermal conductivity and viscosity follow a normal distribution without systematic error, confirming that the regression models developed are statistically adequate for predictive modeling within the tested range.

5. Conclusions

The current investigation conclusively demonstrates that the thermal conductivity and viscosity of vegetable oil-based cutting fluids is predominantly governed by the combined influence of cutting fluid type and nanoparticle characteristics.

• Among the evaluated formulations, Pongamia pinnata cutting fluid incorporated with 0.5 (Vol.%) SiO₂ nanoparticles consistently exhibited the highest thermal conductivity (0.637), clearly outperforming coconut and sunflower oil cutting fluids under identical operating conditions. This superior performance is primarily attributed to the inherently better thermal transport properties of Pongamia pinnata oil and its stronger interaction with nanoparticles, which together facilitate more efficient heat transfer.
• Across all cutting fluids, nanoparticle effectiveness followed the order SiO₂ > hBN > CuO, highlighting the dominant role of nanoparticle dispersion stability and the ability to form continuous thermal conduction networks. SiO₂ nanoparticles provided the maximum enhancement due to their excellent dispersion and effective heat transfer pathways, whereas CuO showed the least improvement, owing to comparatively lower dispersion efficiency.
• The viscosity analysis clearly indicates that the base cutting fluid plays a dominant role in governing the rheological behavior of the developed nanofluids, while nanoparticle type and concentration exert a comparatively secondary influence.
• Among all cutting fluids, Pongamia pinnata and the emulsion ratio consistently exhibits the highest viscosity, irrespective of nanoparticle type and nanoparticles (Vol.%). This behavior is primarily attributed to its higher molecular weight, unique fatty acid composition, and stronger intermolecular interactions, which collectively increase resistance to flow.
• Additionally, the emulsion ratio plays a crucial role in governing viscosity; an increase in oil concentration (1:13) results in a notable rise in viscosity, reaching approximately 1.33.
• Overall, the results confirm that Pongamia pinnata cutting fluids offer superior thermal conductivity and viscosity characteristics, making them highly suitable for machining applications requiring enhanced lubrication performance, reduced tool wear, and improved tribological efficiency.
• ANOVA results for thermal conductivity and viscosity indicate that the cutting fluid type is the most influential factor, contributing 90.58% of the total variance in thermal conductivity and 70.47% in viscosity, both with highly significant p-values of 0.00, emphasizing its significant role in determining the stability of these properties.

While this study establishes Pongamia pinnata with 0.5% SiO₂ as an optimal formulation, several limitations warrant future investigation to fully validate industrial applicability:

• The current study utilized ultrasonication for short-term dispersion. Future work should investigate long-term sedimentation rates (Zeta potential analysis) and the potential need for surfactant optimization to ensure shelf-life stability.
• Although thermal and rheological properties suggest superior lubrication, direct tribological testing (e.g., Pin-on-Disk or Four-Ball tests) is required to quantify the wear scar diameter and coefficient of friction reduction under varying loads.
• In situ machining trials (turning or milling) are necessary to correlate these static properties with dynamic outputs such as tool flank wear, surface roughness and cutting forces.