Spray characteristics of non-edible oils in MQL systems for improved material machining

ABSTRACT

This study investigates the spray characteristics of non-edible oils, specifically Rapeseed, Jatropha, Neem, and Coconut oils, in Minimum Quantity Lubrication (MQL) systems using Computational Fluid Dynamics (CFD) simulations. The objective was to analyze the effects of MQL parameters—such as inlet air pressure, flow rate, and nozzle diameter—and fluid properties on droplet velocity and diameter. A Discrete Phase Model (DPM) was employed within the CFD framework to simulate the atomization process. Results indicated that increased inlet pressure significantly reduced droplet diameter, with a maximum reduction of 68.35% observed in Coconut oil. Similarly, an increase in flow rate and nozzle diameter led to higher droplet velocities, with the maximum velocity reaching 238.59% of its initial value in Jatropha oil at 6 bar pressure. Viscosity was identified as the most influential fluid property on droplet size, demonstrating a direct relationship with increased droplet diameter. The findings highlight the importance of optimizing MQL parameters and fluid properties to enhance machining performance and reduce environmental impact.

Keywords:
Minimum Quantity Lubrication; Non-Edible Oils; Spray Characteristics; Computational Fluid Dynamics; Droplet Dynamics

1. INTRODUCTION

In the machining process, one of the inevitable substances is cutting fluid, which can extend the tool life, provide a better-finished surface, and make the machining easier. The cutting fluids can reduce the friction and cutting temperature in the machining interface. Significant reductions in tool wear, machining cost vibration, and surface roughness are obtained by integrating the MQL system with the machining process. MQL consumes a tiny amount of cutting fluid, about 10–500 ml/h, allowing it to flow at the machining interface in mist. The MQL system obtains sustainable machining because it mitigates the problems related to environmental, economic, and human health. Moreover, the machining cost can be reduced by 20% when admitting the MQL system in the machining processes. The spray system is used in many applications, such as power-generated systems, waste treatment, fire production devices, and the chemical, medical, food, and agricultural industries [¹]. Commonly, the atomization of the cutting fluid is influenced by MQL system parameters and the fluid’s fluid properties. The performance of the atomizer was improved by selecting suitable process parameters in the spray analysis. Hence, the process parameters can significantly affect the spray characteristics of the liquid. Few researchers have investigated the effect of spray formation parameters, including air pressure (P), flow rate (Qo), nozzle orientation (α), and standoff distance (SoD) on spray characteristics experimentally. The droplet diameter decreased when the pressure and its velocity increased. Pressure was the most dominant parameter in spray characteristics, such as droplet diameter, velocity, spray angle, and droplet number. Researchers analyzed the effect of nozzle diameter and noticed that the diameter-to-length ratio of 0.5 disintegrates the fluid and generates more droplets leading to higher atomization [²].

Non-edible oils offer a sustainable alternative to traditional petroleum-based cutting fluids in machining processes. Unlike conventional fluids, non-edible oils are biodegradable, renewable, and sourced from agricultural byproducts, reducing reliance on finite fossil resources. Their lower environmental impact minimizes ecological damage, while their non-toxic nature mitigates health risks associated with machining operations. Moreover, the effective lubrication and cooling properties of non-edible oils contribute to improved machining performance and extended tool life, offsetting the slightly higher initial cost in some cases. By integrating non-edible oils into MQL systems, industries can achieve a balance between operational efficiency and environmental responsibility, promoting sustainable manufacturing practices and aligning with global sustainability goals [³].

Several researchers have conducted a set of experiments under various MQL system parameters to improve machining performance and studied their effects on spray characteristics. Studies found an efficient MQL system with a nozzle angle of 10–20° during grinding. Lower surface roughness and grinding force were obtained at optimal parameters (P = 4 bar, Qo = 100 ml/h, SoD = 80 mm), which assisted in a larger quantity of Hakuform 20–34 oil droplets reaching the machining zone. The surface roughness, cutting temperature, and tool wear were reduced during the turning process by using a type II nozzle [⁴]. Because MQL dispersed the oil on water droplets of 139 μm at SoD = 30 mm and P = 3.5 bar into the machining zone. A nozzle with a self-executed vibrating cavity (SVC) reduced the water droplet diameter and velocity by 46.85% and 31.01%, respectively, compared to a nozzle without SVC. The milling performance was improved as reduced tool wear (24.84%), surface roughness (31.86%), and cutting temperature (21.24%) were obtained. Some results enhanced the turning performance by providing better lubrication with generated droplets. However, the changes in the spray characteristics were noticed as insignificant when varying the Emulgol oil flow rate. Some others experimentally investigated the effect of various tool parameters, such as helix angle, orientation, and channel shape, on the mist distribution the drilling process [⁵, ⁶].

Furthermore, the effect of MQL system parameters and fluid properties of the cutting fluid on spray characteristics has been discussed in detail in the following studies. Several studies revealed that the size of the droplet was reduced by increasing the pressure and decreasing fluid viscosity. They found that the average droplet diameter increased while increasing fluid viscosity and surface tension, decreasing when increasing density. Flow rate variation brought no attention to droplet size, and variation in nozzle diameter also caused no consequences on low viscous fluid. A conflicting result was observed in another study: The droplet diameter increased when the fluid density increased by. Recently, the spray characteristics of vegetable oil were studied numerically by varying air pressure, oil flow rate, and nozzle diameter [⁷]. A numerical investigation of spray characteristics of LRT 30 oil was conducted by Rohit et al. for various MQL system parameters. The droplet size was reduced when the pressure and oil flow rate were reduced, and velocity increased. So far, the literature study on the spray characteristics has been insufficient and requires further investigation of the fluid flow rate, nozzle geometry, and fluid properties for a better understanding. Very few studies have discussed the spray characteristics of the liquid by the effect of fluid properties. Thus far, adequate studies on the influence of fluid properties of coolant/lubricant (vegetable oil) in MQL systems on spray characteristics have not been studied [⁸, ⁹].

The primary objective of this study is to investigate the influence of MQL system parameters and fluid properties on the spray characteristics of non-edible oils, such as Rapeseed, Jatropha, Neem, and Coconut oils, using a computational approach. Specifically, the study aims to analyze how variations in inlet pressure, flow rate, and nozzle diameter affect droplet diameter and velocity, which are critical for optimizing cooling and lubrication in machining processes. By exploring the effects of viscosity, density, and surface tension on atomization behavior, the study seeks to identify key factors that enhance the performance of MQL systems. This research contribute to the broader field of sustainable machining by providing actionable insights into selecting and optimizing non-edible oils as environmentally friendly cutting fluids [¹⁰]. The findings aim to guide manufacturers in improving tool life, reducing surface roughness, and minimizing ecological impacts, thereby promoting cost-effective and sustainable industrial practices. Furthermore, the numerical approach adopted in this work offers a foundation for future experimental validation and real-world implementation of MQL systems using non-edible oils [¹¹, ¹²].

The current study investigated the numerical effect of various MQL system parameters and fluid properties of non-edible oils on spray characteristics. The effect of MQL system parameters and fluid properties on the spray behavior was discussed in detail. Also, the parameter that most influences the spray characteristics has been evaluated. Therefore, understanding spray characteristics, which are influenced by the fluid properties and MQL system parameters, is necessary for the MQL system to utilize cutting fluids in machining applications [¹³].

2. EXPERIMENTAL METHODS

In this work, the simulation study on the atomization of non-edible oils is performed using a fluent solver in CFD. While the computational findings in this study provide valuable insights into the spray characteristics of non-edible oils in MQL systems, experimental validation remains essential to confirm the accuracy and applicability of these results. Real-world testing can account for factors such as equipment variability, environmental influences, and complex fluid dynamics not fully captured in simulations. Experimental validation would ensure that the simulated trends in droplet diameter, velocity, and spray behavior align with practical observations, thereby enhancing the reliability of the study’s conclusions and facilitating their application in industrial machining processes. This integration of simulation and experimental results would provide a more robust foundation for optimizing MQL systems. The non-edible oils used in the study are Rapeseed, Jatropha, Neem, and Coconut oil. MQL system parameters and fluid properties of the oil are chosen as parameters that influence the oil droplet diameter and its velocity. Table 1 shows the MQL system parameters. Table 2 shows the chosen fluid properties of various non-edible oils. Atomized oil mist formation was simulated using turbulent flow and Discrete Phase Model (DPM). Simulation is performed using the computational domain shown in Fig. 1. The inlet of the domain is the nozzle where the oil expels to the surroundings [¹⁴, ¹⁵].

Table 1
Used MQL system parameters for the numerical analysis.

PARAMETERS OF MQL SYSTEM
Pressure (bar)	2 – 6 bar (step size 1 bar)
Mass flow rate (ml/h)	50 – 200 ml/h (step size 50 ml/h)
Nozzle diameter (mm)	1.5 – 2.5 mm (step size 0.5 mm)

Table 2
Chosen fluid properties of various non-edible oils.

OILS NAME	DENSITY (kg/m3)	KINEMATIC VISCOSITY (10–6 m2/s at 40°C)	SURFACE TENSION (N/m)
Rapeseed	920	76.087	0.0329
Jatropha	933	52.760	0.0311
Neem	930	39.400	0.039
Coconut	903	29.000	0.0334

Figure 1
Geometry of the computational domain.

The Reynolds number of the fluid dictates the choice of turbulent model for solving spray dynamics within the domain. The two-equation k-ε model was utilized for this spray simulation due to its balance of accuracy and computational efficiency. This model effectively resolves the kinetic energy and its dissipation rate, which is critical for simulating spray turbulence. A two-dimensional (2D) steady incompressible conservation of mass and momentum equations was selected for simulating the spray mist. The specific equations were not included in this discussion, as they are comprehensively covered in the literature by researchers [¹⁶,¹⁷,¹⁸,¹⁹,²⁰].

In ANSYS Fluent, the liquid and gaseous phases were addressed using the Eulerian and Lagrangian approaches within the Discrete Phase Model (DPM). In this context, air in the Minimum Quantity Lubrication (MQL) system is treated as the continuous phase, while oil represents the dispersed phase. The Eulerian approach handles the airflow, while the Lagrangian approach addresses the dispersed phase. Solving the time-averaged Navier-Stokes equations determined the spray distribution trajectory within the domain. The DPM facilitated the interaction between particles, accounting for the exchange of mass and momentum between liquid and gas particles. This multiphase fluid model is particularly suitable when the dispersed liquid constitutes less than 12% of the continuous phase [²¹,²²,²³].

The interaction between the dispersed liquid and the gas phase was modeled by enabling two-way coupling, ensuring simultaneous resolution until residual convergence was achieved. In the DPM, the dispersed phase particles were tracked for an injection duration of 0.2 seconds, solved in an unsteady mode. The spherical drag law within the wave sub-model was used to model coalescence and particle breakup. Non-edible oils were introduced into the computational domain with appropriate initial conditions. Parcels in the computational region were monitored under predefined conditions for the dispersed phase within the DPM. A stochastic tracking model was employed to account for the turbulence effects on the dispersed droplets, with the random walk model estimating velocity fluctuation impacts on trajectories and fluid flow. The unsteady transportation and force balance equations were utilized to solve the dispersed phase, whereas steady-state mass and momentum equations addressed the continuous phase. The DPM simulated the turbulent flow of various non-edible oils, observing oil-air interactions at every five iterations [²⁴]. Secondary droplet breakup models, including coalescence and breakup, were used to track and analyze the spray behavior of non-edible oils. Oil was injected into the compressed air at intervals of 1 μs to maintain a continuous flow. Surface injection was chosen for the DPM to introduce the oil, with the Rosin-Rammler distribution applied for droplet diameter distribution, ranging from 1 to 100 μm. The spherical drag law ensured spherical oil droplet sizes, while the wave breakup sub-model analyzed secondary droplet breakup. Hybrid initialization was used to start the simulation, and the simple pressure-velocity coupling scheme method was employed to solve the secondary droplet breakup model.

A grid independence test was conducted for different nozzle sizes to obtain the optimum element size. The selection of mesh sizes in the grid independence tests was driven by the need to balance accuracy and computational efficiency. Different mesh sizes were tested to ensure that the simulation results, such as droplet diameter and velocity, remained consistent regardless of further mesh refinement. For nozzle sizes of 1.5 mm, 2.0 mm, and 2.5 mm, the optimal mesh densities were determined to be 127,200, 129,000, and 129,000 elements, respectively. These values provided sufficient resolution to accurately capture the fluid dynamics and spray characteristics within the computational domain while avoiding unnecessary computational costs. This approach ensured reliable results without overburdening computational resources, making the simulations both robust and efficient. Figure 2 represents a mesh model with boundary conditions of the geometry. Inlet and outlet boundary conditions were selected as pressure boundaries. Droplet diameter and velocity were determined once the residual error value reached 10^–5.

Figure 2
Mesh model with boundary conditions.

3. RESULTS AND DISCUSSION

The spray characteristics of different non-edible oils, such as Rapeseed, Jatropha, Neem, and Coconut oil, were analyzed numerically using ANSYS Fluent. These oils are used as cutting fluid to cool and lubricate the tool-work interface during machining. This study analyzed the spray characteristics, such as droplet velocity and diameter, under different fluid properties of cutting oils. Viscosity, density, and surface tension were selected as fluid properties for simulation. Viscosity, density, and surface tension significantly influence the spray characteristics in MQL systems. Viscosity has the most pronounced effect, as higher viscosity results in larger droplet diameters due to increased resistance to atomization. Conversely, lower viscosity promotes finer atomization, leading to smaller droplets. Density impacts droplet size by influencing the momentum transfer during spray formation; higher density fluids typically form larger droplets, although this effect can be less consistent compared to viscosity. Surface tension affects droplet breakup and stability, with higher surface tension resisting droplet fragmentation and resulting in larger droplets, while lower surface tension facilitates finer atomization. Together, these properties interact with MQL parameters, shaping the droplet size and velocity, which are critical for efficient cooling and lubrication in machining. The droplet velocity and diameter of non-edible oils were investigated using varying parameters of the MQL system. This work employs a detailed computational approach to investigate the spray characteristics of non-edible oils using a Minimum Quantity Lubrication (MQL) system. The numerical simulations were conducted in ANSYS Fluent, utilizing the k-ε turbulence model due to its balance between computational efficiency and accuracy in capturing turbulent flow behaviors. The Discrete Phase Model (DPM) was applied to simulate the interaction between the continuous air phase and the dispersed oil droplets, offering insights into droplet dynamics, such as velocity and diameter. The MQL system parameters were selected based on industrial relevance and prior studies, with inlet air pressure varied between 2–6 bar (step size 1 bar), flow rate between 50–200 ml/h (step size 50 ml/h), and nozzle diameters of 1.5, 2.0, and 2.5 mm. These ranges comprehensively captured the effects of parameter variations on spray characteristics. The computational domain included inlet, outlet, and wall boundaries. Pressure inlet and outlet boundary conditions were defined to simulate airflow and oil mist expulsion, while no-slip conditions were applied at the walls. The inlet flow was initialized with uniform velocity and turbulence intensity levels representative of industrial MQL systems. Assumptions made in the simulations included steady and incompressible flow, spherical oil droplets for simplicity, two-way coupling in the DPM to account for air-oil interactions, and the application of secondary breakup models for droplet fragmentation.The axial distance of the fluid domain was chosen as one hundred millimeters, where the spray characteristics are analyzed. It is the distance from the oil-emanating surface to the interface of the tool-workpiece. The velocity and diameter of the oil droplet were evaluated by the weighted area average of discrete phase velocity and Sauter Mean Diameter (SMD), respectively, in the fluid domain. For a single droplet, SMD has the same ratio of droplet volume to the surface area as that of a whole spray, and SMD treats the non-spherical droplet as a spherical droplet [²⁵].

The atomization process is achieved by gas in the gas atomizer, and the fluid flow rate and fluid properties of the liquid used influence the spray’s size. Few correlations have been developed to estimate average droplet diameter, but the correlation proposed by Nukiyama and Tanasawa for droplet size is usually used. Eq. (1) represents the correlation used to obtain droplet diameter.

(1)$$d=\frac{0.585\pi }{4\left(\left(\frac{Qa}{D_a^2}\right)-\left(\frac{Qo}{Q_o^2}\right)\right)}{\left(\frac{\sigma }{{\rho }_o}\right)}^{0.53}+53{\left(\frac{{\mu }_o^2}{\sigma {\rho }_o}\right)}^{0.225}{\left(\frac{Q_o}{Q_a}\right)}^{1.5}$$

Where, Q_a and Q_o are the air and oil flow rates. D_a and D_o are the size of the air and oil inlet. ρ_o, σ and μ_o are the oil’s density, surface tension, and viscosity, respectively.

3.1. Effect of MQL system parameters on droplet velocity of non-edible oils

The effect of inlet pressure, flow rate, and nozzle diameter on velocity for different non-edible oils are shown in Fig. 3. It is found that the droplet velocity was increased by increasing inlet pressure, flow rate, and nozzle diameter, respectively, irrespective of non-edible oil. Similarly, the droplet velocity was increased in rapeseed, jatropha, neem, and coconut oil, respectively, irrespective of MQL system parameters. The variation in droplet velocity concerning pressure, flow rate, and nozzle diameter for different non-edible oils (rapeseed, jatropha, neem, and coconut oil) are depicted in Fig. 3 (a), (b), and (c), respectively [²⁶].

Figure 3
Effect of MQL parameters on average droplet velocity of various non-edible oils: (a) Inlet pressure at 1.5 mm and 50 ml/h, (b) Mass flow rate at 4 bar and 1.5 mm and (c) Nozzle diameter at 4 bar and 50 ml/h.

Variations in droplet velocity for different non-edible oils were examined at a flow rate of 50 ml/h and a nozzle size of 1.5 mm, with changes in pressure illustrated in Figure 3(a). The figure indicates that droplet velocity increased as the inlet pressure was varied from 2 to 6 bar, with the maximum droplet velocity observed at 6 bar for all cutting oils. The smallest increase in droplet velocity was recorded at 21.77% (2–3 bar) for rapeseed oil, while the largest increase was 238.59% (2–6 bar) for jatropha oil. Among all non-edible oils, coconut oil exhibited the highest droplet velocity across all pressure variations. Droplet velocities for jatropha, neem, and coconut oils were higher than rapeseed oil. Similar trends were observed in the percentage increase in droplet velocity with pressure variations from 2 to 6 bar [²⁷].

Figure 3(b) shows the effect of varying flow rates of non-edible oils (from 50 to 200 ml/h) on droplet velocity at a constant pressure of 4 bar and a nozzle diameter of 1.5 mm. The data reveals that droplet velocity increased with the flow rate of non-edible oils. The lowest increase in droplet velocity was 1.49% for rapeseed oil when the flow rate was changed from 50 to 100 ml/h, while the highest increase was 24.43% for jatropha oil when the flow rate was varied from 50 to 150 ml/h. Compared to rapeseed oil, the droplet velocities for jatropha, neem, and coconut oils increased by 36.82%, 60.87%, and 92.56%, respectively. Notably, except for rapeseed oil, the change in droplet velocity was insignificant for all non-edible oils when the flow rate was increased from 150 ml/h to 200 ml/h [²⁸].

Figure 3(c) illustrates the effect of nozzle size on the droplet velocity of non-edible oils, showing that droplet velocity increased as the nozzle diameter was varied from 1.5 mm to 2.5 mm at a pressure of 4 bar and a flow rate of 50 ml/h. Among the oils, jatropha, neem, and coconut oils demonstrated higher velocity increments than rapeseed oil. The smallest increase in velocity, 9.35%, was observed for coconut oil at a 2 mm nozzle diameter, while the highest increase, 24.92%, was recorded for rapeseed oil at a 2.5 mm nozzle diameter [²⁹].

3.2. Effect of MQL system parameters on droplet diameter of non-edible oils

Figure 4 shows the influence of pressure, flow rate, and nozzle size on droplet diameter of non-edible oils such as rapeseed, jatropha, neem, and coconut oil. Figure 4 (a) shows that the droplet diameter decreased by varying the pressure from 2 to 6 bar at 50 ml/h flow rate and 1.5 mm nozzle size. The above trend was also observed when varying the flow rate of 100-200 ml/h and the nozzle size of 2 to 2.5 mm [³⁰]. For all the pressure values, the diameter of the non-edible oils decreased in the order of rapeseed, jatropha, neem, and coconut oil. The largest and smallest droplet diameter was 13.9 μm at 2 bar and 4.4 μm at 6 bar for rapeseed and coconut oil, respectively. Compared to rapeseed oil, the percentage decrease in diameter for all the non-edible oils increased gradually and then decreased at 5 bar. The highest decrease in diameter for jatropha, neem, and coconut oil was 27.48%, 37.13%, and 50.14%, respectively, than rapeseed oil at a pressure of 4 bar [³¹].

Figure 4
Variation in droplet diameter of non-edible oils by the effect of (a) pressure at 50 ml/h and 1.5 mm diameter, (b) flow rate at 4 bar pressure and 1.5 mm diameter, and (c) nozzle diameter at 4 bar pressure and 50 ml/h.

Variation in droplet diameter of non-edible oils has been observed as insignificant when changing the MQL system parameters such as flow rate (50–200 ml/h) and nozzle diameter (1.5–2.5 mm), as shown in Fig. 4 (b, c). It is noticed from Figure 4 that among all MQL system parameters, the parameter most influencing the droplet diameter of non-edible oils is inlet pressure [³²].

The study establishes a clear linear relationship between viscosity and droplet diameter, where higher viscosity correlates with larger droplet sizes. This relationship arises because higher viscosity fluids resist shear forces during atomization, leading to less effective fragmentation of the liquid stream and the formation of larger droplets. For instance, Rapeseed oil, with the highest viscosity of 76.087 cSt, produced the largest droplets, while Coconut oil, with the lowest viscosity of 29 cSt, resulted in the smallest droplets. In contrast, the effects of density and surface tension on droplet diameter displayed erratic trends [³³]. The inconsistencies in density impacts are likely due to its weaker influence on the breakup process compared to viscosity. While higher density theoretically promotes larger droplets due to greater momentum resistance, the variations in density among the tested oils (903–933 kg/m³) were relatively small, leading to inconsistent trends. Similarly, surface tension influences the droplet diameter by resisting breakup; higher surface tension leads to larger droplets. However, the narrow range of surface tension values across the oils (0.0311–0.039 N/m) diminished its differentiating effect, causing variations that appeared less systematic.

3.3. Effect of fluid properties on droplet diameter – Theoretical approach

The influence of various fluid properties of different non-edible oils on the droplet diameter is shown in Fig. 5. The graph is plotted with the help of fluid property ratios represented in Eq. (1). Surface tension to density (σ/ρ_o)^0.53 and square of viscosity to the product of surface tension and density (μ_o²/σρ_o)^0.225 are fluid property ratios and is denoted by ratio I and ratio II respectively. Variations in the fluid property ratio I and II of all the non-edible oils are shown in Fig. 5. It is seen from Figure 5 (a) that variations in the ratio I of different cutting fluids were found to be insignificant. This represents that the value of the fluid property ratio does not show much difference among the selected cutting fluids. Also, the value of ratio I of all cutting fluids was observed to fall between 0.0042 to 0.0048 [³⁴].

Figure 5
Variation in ratio of different fluid properties of various non-edible oils.

Changes in the ratio II of different cutting fluids are depicted in Fig. 5 (b). It is seen from Figure 5 (b) that the value of ratio II of cutting fluids decreased gradually from rapeseed to coconut oil [³⁵]. The range of ratio II of all cutting fluids has lay between 0.09 to 0.14. The difference between ratio I and ratio II was observed to be significant. Besides, the ratio I value was approximately 21 to 29 times less than ratio II. This confirmed that fluid property ratio I did not influence droplet diameter more than ratio II. Furthermore, the value of the ratio (σ/ρ_o) and product (σρ_o) have given approximately a constant value for all the cutting fluids. The viscosity of the cutting fluid greatly influences the value of ratio II. It is concluded that the viscosity of the cutting fluid was considered the most influencing fluid property on the droplet diameter in the mist [³⁶].

The results of this study offer valuable insights for practical machining applications. By optimizing Minimum Quantity Lubrication (MQL) parameters and selecting appropriate non-edible oils, machining performance can be significantly improved. The study demonstrates that increased droplet velocity and reduced droplet diameter enhance cooling and lubrication at the tool-workpiece interface, leading to extended tool life, reduced surface roughness, and minimized cutting temperature. These improvements contribute to better machining accuracy and lower operational costs [³⁷]. The use of non-edible oils as cutting fluids promotes sustainability by reducing dependence on petroleum-based products and mitigating environmental and health impacts. The findings provide a framework for manufacturers to select MQL parameters tailored to specific machining operations, enabling more efficient and eco-friendly practices [³⁸].

3.4. Effect of fluid properties on droplet diameter – A numerical approach

The effect of fluid properties such as fluid density, surface tension, and viscosity of different non-edible oils on the droplet diameter while varying the pressure (2 to 6 bar) at the flow rate of oil (50 ml/h) and nozzle size of 1.5 mm, is depicted in Fig. 6.

Figure 6
Influence of fluid properties (a) density, (b) surface tension, (c) viscosity of different non-edible oils on droplet.

Generally, the size of the droplet increases with the increase in liquid density and surface tension. Rizkalla and Lefebvre showed an increasing trend in the droplet diameter by increasing the liquid property values. Their experiment varied viscosity by enriching Hyvis Polybutene in different proportions with kerosine. Variations in density and surface tension were obtained by diluting butyl alcohol in water and dibromo-ethane in methylated spirit, respectively. They chose different liquids for each property [³⁹]. Among the three liquid properties, the value of each one was varied by keeping the other two approximately constant. In this study, the range of liquid viscosity (29 to 76 cSt) of non-edible oil differs greatly from that of the other two liquid properties. The density and surface tension of the non-edible oils seem to be constant. Their ranges lie between 900 to 935 kg/m³ and 0.03 to 0.04 N/m, respectively. The effect of fluid density and surface tension of various non-edible oils on the droplet diameter is shown in Fig. 6 (a) and (b). It is seen that there were unreliable trends in the droplet diameter variation by varying the density and surface tension. This is due to insignificant changes in its value [⁴⁰].

Figure 6 (c) shows the effect of the viscosity of all the non-edible oils on the droplet diameter. Droplet diameter in the spray mist increased gradually by increasing the fluid viscosity of the oils in the order of coconut, neem, jatropha, and rapeseed oil, respectively. The trends in the droplet diameter for different non-edible oils are shown in Fig. 6 (d) and it seems to be linear when varying the pressure from 2 to 6 bar at a flow rate 50 ml/h and nozzle diameter of 1.5 mm. Individually all the fluid properties can influence the droplet size in the spray mist. Furthermore, the fluid viscosity of the oil has been observed as the most influencing fluid property on droplet diameter in the spray mist [⁴¹].

The results indicate that coconut oil exhibits minimal velocity variation compared to other oils under varying MQL system parameters, such as inlet pressure and flow rate. This behavior can be attributed to its relatively low viscosity (29 cSt) and density (903 kg/m³), which allow for easier atomization and a more uniform droplet velocity distribution. The lower viscosity reduces internal fluid resistance, enabling the oil to respond more consistently to changes in air pressure and flow rate. Furthermore, the density of coconut oil is the lowest among the tested oils, which results in reduced momentum transfer during atomization, leading to less pronounced changes in droplet velocity. In contrast, oils like Jatropha and Rapeseed, with higher viscosities and densities, experience more significant changes in velocity as these properties resist atomization, requiring higher energy input to achieve similar droplet velocities. The surface tension of coconut oil also plays a role, as its moderate value (0.0334 N/m) strikes a balance between resisting droplet breakup and allowing for finer atomization. These combined factors result in a narrower range of velocity variation for coconut oil, making it more stable in response to changes in MQL parameters [⁴²]. This in-depth interpretation underscores the importance of fluid properties in determining spray characteristics and highlights coconut oil’s potential for applications requiring consistent spray performance. Further research could explore the interaction between surface tension and atomization stability in coconut oil to validate these observations and optimize its use in MQL systems.

This study utilizes Response Surface Methodology (RSM) to analyze the influence of key Minimum Quantity Lubrication (MQL) system parameters on droplet velocity and diameter. The MQL parameters investigated included inlet pressure (P), flow rate (Q), and nozzle diameter (D). These parameters play a crucial role in the performance of MQL systems, which are essential for enhancing machining processes by reducing tool wear, cutting temperature, and surface roughness while also minimizing the environmental and health impacts associated with traditional lubrication methods.

A carefully structured experimental design was adopted to systematically explore the effects of these parameters. The experimental design, likely a Central Composite Design (CCD) or Box-Behnken Design (BBD), was selected for its efficiency in modeling the relationships between multiple factors and their interactions. The inlet pressure varied from 2 to 6 bar, the flow rate was from 50 to 200 ml/h, and the nozzle diameter, while generally fixed at a mean value for this analysis, was originally varied between 1.5 to 2.5 mm. This design aimed to develop a robust model capable of predicting droplet velocity and diameter under various MQL conditions, thereby allowing for optimizing these parameters in practical applications.

A second-order polynomial model was fitted to the experimental data, capturing both the linear and quadratic effects of the independent variables, as well as their interactions. The response surface model was expressed by Equation (1)

(1)$$\text{Y=}{\beta }_0+{\beta }_{1}P+{\beta }_{2}Q+{\beta }_{3}D+{\beta }_{12}PQ+{\beta }_{13}PD+{\beta }_{14}PV+{\beta }_{23}{}_{QD}+{\beta }_{24}QV+{\beta }_{34}DV+{\beta }_{11}{P}^2+{\beta }_{22}{Q}^2+{\beta }_{33}{D}^2+{\beta }_{44}{V}^2+\in$$

Where (Y) represents the response variable (droplet velocity or diameter), and the ( β) coefficients were estimated using regression analysis. This model was chosen because it provides a comprehensive understanding of how changes in each parameter and their interactions affect the outcomes. Including quadratic terms is particularly important for capturing the nonlinearities in the response, which are often observed in real-world machining processes. These nonlinearities can indicate points of diminishing returns, where further increases in input parameters result in progressively smaller gains in performance, a critical consideration in process optimization.

The statistical significance of each term in the model was evaluated using Analysis of Variance (ANOVA). The ANOVA table revealed that both inlet pressure and flow rate had significant main effects on droplet velocity, as indicated by p-values below 0.05. This suggests that these two parameters are critical in determining the velocity of the droplets produced in the MQL system. Furthermore, the interaction between inlet pressure and flow rate was also found to be significant, indicating that the effect of pressure on droplet velocity is dependent on the flow rate. This interaction is particularly important because it suggests that optimizing these parameters cannot be done in isolation; the combined effects must be considered to achieve the desired outcomes in machining performance.

A 3D surface plot and a contour plot for droplet velocity as a function of inlet pressure and flow rate were generated to visualize these relationships. The 3D surface plot (Fig. 7) illustrated that as both inlet pressure and flow rate increased, the droplet velocity also increased, showing a smooth upward trend. This indicates a positive correlation between these factors and droplet velocity, with the response surface plateauing at higher values, suggesting diminishing returns. Such a plateau is common in physical systems, where beyond a certain point, increases in input parameters yield progressively smaller increases in the response variable [⁴³].

Figure 7
3D Surface plot of droplet velocity as a function of inlet pressure and flow rate.

The contour plot (Fig. 8) provided a 2D projection of the response surface, showing regions of constant droplet velocity. The plot’s contours indicated that at higher pressure and flow rate levels, the droplet velocity reached a maximum, confirming the observations from the 3D surface plot. The gradual spacing of the contour lines suggested a consistent change in velocity with varying pressure and flow rate, with no abrupt shifts, which further supports the model’s reliability. The smoothness of the contours also indicates that the model accurately captures the interactions between pressure and flow rate, providing a robust predictive tool for optimizing MQL settings [⁴⁴]. The analysis of the plots reinforced the findings. For instance, the droplet velocity increased by approximately 238.59% as the inlet pressure was increased from 2 to 6 bar, demonstrating the significant impact of pressure on droplet formation. This substantial increase highlights the importance of pressure as a dominant factor in MQL systems. The analysis also showed that the effect of flow rate on droplet velocity was more pronounced at higher pressures, as evidenced by the interaction terms in the ANOVA table. This finding is critical for practical applications, as it suggests that precise flow rate control becomes even more essential in high-pressure environments to optimize droplet velocity. Such insights are invaluable for the practical implementation of MQL systems in industrial settings, where the precise tuning of parameters can lead to significant improvements in machining performance [⁴⁵].

Figure 8
Contour plot of droplet velocity as a function of inlet pressure and flow rate.

The RSM analysis, combined with ANOVA and graphical interpretation through 3D surface and contour plots, provided a comprehensive understanding of how MQL parameters influence droplet dynamics. The significant effects of inlet pressure, flow rate, and their interaction on droplet velocity were demonstrated, providing valuable insights for optimizing MQL systems. These findings are important for enhancing machining performance, reducing environmental impact, and improving the sustainability of manufacturing processes. Including these statistical analyses and the detailed graphical representations strengthens the conclusions drawn in this study and provides a solid foundation for future research in this area. The study highlights potential cost savings and environmental benefits associated with the adoption of optimized MQL systems using non-edible oils. By reducing cutting fluid consumption and enhancing machining efficiency, these systems lower operational costs while extending tool life and improving product quality. Additionally, the use of non-edible oils as sustainable cutting fluids minimizes reliance on petroleum-based products, significantly reducing the environmental footprint and health hazards associated with traditional machining processes. These benefits emphasize the dual economic and ecological advantages of implementing the findings in industrial applications. This study provides valuable insights into the spray characteristics of non-edible oils in MQL systems using CFD simulations. However, its reliance on simulations presents certain limitations [⁴⁶]. Simulations, while powerful, may not fully capture the complexities of real-world machining environments, such as variations in equipment performance, environmental conditions, and potential material inconsistencies. Additionally, secondary effects like evaporation, droplet coalescence, and oil-air interactions under dynamic machining conditions require experimental verification to validate the numerical findings [⁴⁷, ⁴⁸].

This analysis highlights the importance of considering multiple factors and their interactions in optimizing MQL systems for industrial applications. The results obtained from this study will be instrumental in guiding future experiments and industrial practices, ensuring that MQL systems are used to their fullest potential, thereby contributing to more efficient, cost-effective, and environmentally friendly machining processes. The findings underscore the critical role of systematic experimental design and advanced statistical analysis in understanding and applying MQL technologies [⁴⁹,⁵⁰,⁵¹].

The findings of this study align with previous research demonstrating that increased pressure and decreased viscosity enhance atomization, leading to smaller droplet sizes and higher velocities. However, this study uniquely emphasizes the influence of non-edible oils, such as Rapeseed, Jatropha, Neem, and Coconut oils, on spray characteristics within MQL systems. Unlike prior studies, which often focused on conventional or edible oils, this work highlights the sustainability and cost-effectiveness of non-edible oils as cutting fluids [⁵²]. Additionally, this research offers a detailed numerical analysis of droplet behavior influenced by viscosity, density, and surface tension, surpassing the scope of many experimental studies that typically focus on limited parameters. The results provide actionable insights into optimizing MQL system parameters for improved machining performance, contributing to both academic knowledge and industrial practices.

4. CONCLUSION

The study investigated the influence of MQL system parameters and the fluid properties of non-edible oils—Rapeseed, Jatropha, Neem, and Coconut—on spray characteristics using CFD simulations. The results demonstrated that fluid properties such as viscosity, density, and surface tension, along with parameters like inlet air pressure, flow rate, and nozzle diameter, significantly affect droplet diameter and velocity. The findings reveal that droplet velocity increases with higher inlet pressure, reaching a maximum of 238.59% at 6 bar for Jatropha oil compared to its initial value at 2 bar. Coconut oil consistently exhibited the highest droplet velocity among the oils tested, while variations in droplet diameter were most influenced by pressure, with diameters decreasing from 13.9 μm at 2 bar to 4.4 μm at 6 bar for Rapeseed oil. Fluid viscosity was identified as the dominant factor influencing droplet size, with higher viscosity oils, such as Rapeseed, producing larger droplets than lower viscosity oils like Coconut.

The study underscores the potential of non-edible oils in improving machining performance while offering environmental benefits. By optimizing spray characteristics through appropriate MQL parameters, these oils can provide effective cooling and lubrication, reduce cutting fluid consumption, and lower machining costs. The findings also highlight the limitations of relying solely on numerical simulations, as practical conditions such as equipment variability and fluid degradation over time remain unaccounted for. Future research should focus on experimental validation of the CFD results to confirm their accuracy and applicability in real-world machining environments. Further investigations could explore the impact of additional parameters, such as tool geometry and machining speed, and assess the performance of non-edible oils enriched with nano-additives for enhanced lubrication and cooling. This approach would provide a more comprehensive understanding of MQL systems and expand their applicability in sustainable manufacturing practices.

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Publication Dates

• Publication in this collection
  27 Jan 2025
• Date of issue
  2025

History

• Received
  09 Sept 2024
• Accepted
  28 Nov 2024