*3.5. Nanofluids*

When the fluids are suspended with nano-sized particles, they are referred to as nanofluids. Nanofluids are the colloidal dispersion of the nanometer-sized particles, which are referred to as the nanoparticles in the base fluids [104]. The base fluids may include water, ethylene glycol, engine oil, or any other cutting fluid. The current recent advances in nanotechnology allows us to use nanofluids as conventional MWFs in conjunction with the minimum quantity lubrication technique in machining processes [105]. The inherent properties that nanofluids offer, such as enhanced heat transfer and improved tribological properties, allow them to be used in applications where better cooling and lubrication are required during the machining process, thus making the machining process more viable. Therefore, using nanofluids as an alternative to the conventional MWFs is one of the novel technological approaches in machining. Based on their heat transfer and tribological characteristics, nanoparticles that comprise MoS2, CuO, ZnO, diamond, Ag, and titanium have been investigated for their use in machining operation. A considerable amount of research is being conducted to investigate the feasibility of nanofluids prepared from the colloidal dispersion of nanoparticles in the base fluid for machining operation. The decision to use nanofluids as a coolant is solely due to the enhanced thermal conductivity characteristics of the nanoparticles that are suspended in the base fluids [106]. It has been reported that the size of the nanoparticles also play an important role in determining the thermal conductivity of the nanofluids [107–110]. Furthermore, it has been reported that the nanofluids with smaller-sized nanoparticles have more enhanced thermal conductivity due to their extended specific surface area. Other factors which affect the thermal conductivity of nanofluids include the temperature of the nanofluid and the concentration of nanoparticles in the base fluid [111–115]. The thermal conductivity of nanoparticles and the base fluid also considerably affect the thermal conductivity of the nanofluid. The higher the thermal conductivity of the nanoparticles and thermal conductivity ratio i.e., the higher the ratio of the thermal conductivity of the nanoparticles and thermal conductivity of the base fluid, the higher the thermal conductivity of the resulting nanofluid will be [116,117]. Adding to the thermal conductivity, the pH of the base fluids and additives also play an important role. It has been observed that an increase in the pH of the base fluids and additives increase the thermal conductivity of the nanofluids. This is because of the fact that an increase in the pH value of the base fluids and additives results in the prevention of agglomeration and the improvement of the nanoparticle suspension [118–122].

In addition to thermal conductivity, other factors which affect the performance of nanofluids include the stability and viscosity of the nanofluids. The stability of the nanofluid is very important for improved heat transfer and thus the stability depends on various factors, such as the characteristics of the nanoparticles themselves, the methods of preparation, ultrasonication, stirring, etc. [123] Moreover, the viscosity of the nanofluids plays an important role in the performance of the nanofluids. Viscosity is defined as the internal resistance of the fluid to flow, i.e., the fluid's internal friction to flow, expressed as the force per unit area, which resists the flow; this property is widely affected by external physical parameters, such as temperature. Therefore, viscosity is an important parameter to be considered in thermal and fluid flow applications. Several investigations have been carried out to investigate the viscosity of nanofluids, and these investigations have reported an increase in the viscosity of the nanofluids with an increase in the volume fraction. Additionally, the size of the nanoparticles was seen to have a minimal effect on the viscosity of the nanofluids [124–127].

Nanofluids are widely used in the MQL technique to minimize the amount of lubricant used, and numerous attempts have been made to perform machining operation using nanofluids under the MQL method. Various studies have reported better surface finish, lower cutting forces, lower power consumption, and a higher tool life wen nanofluids were used, compared with dry machining and machining using flooded cooling methods. Prasad and Srikant [128] performed an experimental investigation on the turning of AISI 1040 using nanographite particles mixed with cutting fluid using the MQL method. They reported that as the concentration of the nanoparticles was increased, there was a spike in the values of the pH, viscosity, and thermal conductivity, in addition to lower tool wear, surface roughness, nodal temperatures, and cutting forces. They also observed a better machining performance at 0.3% nanoparticle concentration and a flowrate of 15 mL/min. Rahmati et al. [129] performed the slot milling of Al6061-T6, allowing the use of nanoparticles under the MQL approach. They reported that at 1% nanoparticle concentration in the mineral oil, the lowest cutting forces were observed; the lowest cutting temperature was observed at 0.5% nanoparticle concentration. Similarly, Sarhan et al. [130] reported a considerable reduction in the coefficient of friction at the tool–chip interface and consequently a decrease in the cutting forces, specific energy, and power when using SiO2 in tandem with mineral oil under the MQL method. Yücel et al. [131] performed a turning operation on the AA 2024 T3 aluminum alloy, using the MoS2-based nanofluid and the MQL technique, in order to investigate the tribological and machining characteristics. They reported that significant improvements were achieved in the surface roughness, surface topography, and maximum temperature. They also added that by using the nanofluidbased MQL, the built-up edges were eliminated, and they obtained less damaged edges compared with dry machining.

Recently, ¸Sirin and Kivak [132] performed a milling operation on the Inconel X-750 superalloy to see the effects of hybrid nanofluids using the MQL technique. They investigated the combination of different nanofluids, cutting speeds, and feed rates, and reported that using the hexagonal boron nitride (hBN)/graphite nanofluids resulted in a better performance compared to their counterparts under all criteria. They also added that hBN/graphite nanofluids achieved 36.17% and 6.08% improvements in tool life, respectively, compared to the graphite/MoS2 and hBN/MoS2 nanofluids. Junankar et al. [133] conducted a performance evaluation of a Cu nanofluid in a turning operation of bearing steel using the MQL approach. They analyzed the effect of the cutting sped, feed rate, and depth of the cut to perform a multi-objective optimization; this analysis was conducted using the grey relational analysis technique, and it was performed to obtain the optimum conditions of operation and their impact on the surface roughness and the cutting zone temperature. They reported that Cu nanofluid in conjunction with MQL resulted in the most significant cooling environment compared with vegetable oil MWFs. They also reported that the surface roughness and the cutting zone temperature were considerably reduced when the machining operation was performed using a Cu nanofluid under the MQL method. Haq et al. [134] evaluated the effects of a nanofluid-based MQL technique while performing a milling operation on the Inconel 718 superalloy, and compared the results of the simple MQL and nanofluid-based MQL approaches. They investigated the effect of feed rate, speed, flow rate, depth of the cut on the material removal rate, and the surface roughness, and conducted the optimization using the response surface methodology. They reported that the nanofluid-based MQL approach was better as compared with the simple MQL method, and resulted in decreased surface roughness, temperature, and power. Barewar et al. [135] investigated the sustainable machining of the Inconel 718 superalloy using an Ag/ZnO-based hybrid nanofluid and the MQL method, and performed the optimization using the Taguchi method with the grey relational analysis. They reported that the nanofluid-based MQL method resulted in an improved surface finish, minimum tool wear, and lower cutting temperature when compared with the simple MQL method and dry machining. Tiwari et al. [136] performed a computational analysis to see the characteristics of the surfaces of different concentrations of different nanofluids in conjunction with the MQL technique. They analyzed different nanofluids such as Al2O3, CuO, and TiO2 at different concentrations (1% to 6%, at an interval of 1%) through the MATLAB

software. From their analysis, they reported that the nanofluid-based MQLs resulted in intermittent chips which were easy to remove in contrast to the normal MQLs, which resulted in continuous chips. They also added that by using the nanofluid-based MQL method, cutting power was reduced, and a better surface finish was obtained. Mohana Rao et al. [137] performed an experimental investigation to observe the effects of cutting parameters on the tuning of EN-36 steel using both dry MQL and nanofluid-based MQL methods. They performed the investigations at 6% and 8% volume concentration of Al2O3 nanofluid and used the Taguchi analysis to optimize the process. They reported that at the 8% volume concentration, the surface roughness, temperature, cutting forces, and tool wear was lower compared with the 6% volume concentration and compared with dry machining.

Khanafer et al. [138] investigated the micro-drilling of the Inconel®718 superalloy using a MQL-Al2O3 nanofluid, and reported that the thrust forces were lower in the case of MQL-based nanofluid cooling compared with simple MQL cooling and flood cooling. They also reported that burr formation, tool wear, and cooling rates were improved in the case of the MQL-Al2O3 nanofluid. Sharma et al. [139] compared three different types of nanofluids, namely Al2O3, TiO2, and SiO2, with varying volume fractions to be utilized in metal cutting fluids. They concluded that the Al2O3 nanofluid exhibited better thermal properties compared with SiO2 and TiO2. Sharma et al. [140] experimentally investigated the turning operation of AISI 1040 steel using Al2O3 nanoparticle-based cutting fluids and the MQL approach. They reported that the performance of Al2O3 nanofluids were better in terms of the surface roughness, tool wear, cutting force, and chip morphology when compared with dry machining and wet machining with conventional cutting fluid. Minh et al. [141] investigated the performance of 0.5% (by volume concentration) Al2O3 nanofluids in MQL in the hard milling of 60Si2Mn steel using cemented carbide tools. They reported that the tool life was considerably improved, and they observed a reduction in the roughness and cutting forces in the range of 35–60% under the MQL conditions. They added that it could be attributed to the improved tribological behavior as well as the cooling and lubricating effect of the nanoparticles.

The above analyses indicate that cutting fluid applications, as well as cooling and lubrication media, can be customized by using properly selected nanofluids in varying amounts. For an enhanced cooling effect, i.e., for an enhanced heat removal rate, nanofluids can be tailored to meet the requirements. When the objective is to obtain more lubrication, nanofluids can be used as a cutting medium in the form of droplets with the MQL technique. From the above analyses, we inferred that the Al2O3 nanoparticles have shown promising results compared with their counterparts. However, nanofluids are still in the developmental phase, but the applications of nanofluids in machining have promising prospects compared to nano-coolants.
