1 Introduction

A contact of the workpiece and cutting tool will generate heat at the contact zone during machining [1]. The rising temperature at the contact zone will exert a negative effect on the machining performance such as high tool wear, high cutting force, and result in poor finishing quality. Therefore, cooling and lubrication are extremely important in this area. Cutting fluid, which is normally used as a coolant or lubricant, is essential in machining processes [2]. According to Samanta et al. [3], cutting fluid (water-based fluid) is used to cool the cutting area during machining and eliminate the unwanted effect of heat on both the workpiece and tools. In addition, cutting fluid (oil-based fluid) also works as a lubricant by penetrating in the chip–tool interface to reduce friction and prevent build-up edge formation. The cutting fluid also helps in the chip removal from the cutting zone and protects the machine tool against corrosion to improve the accuracy and ease of use during machining.

Figure 1 shows the various types of cooling–lubrication techniques, such as dry machining, wet machining, minimum quantity lubricant (MQL), cryogenic cooling, nanofluid and hybrid nanofluid. According to Goindi and Sarkar [4], dry machining is conducted without the assistance of any cutting fluids. Tool wear is high in this technique because of the effects of several wear mechanisms, such as abrasion, adhesion and diffusion, which reduce the tool life [5]. Furthermore, without the use of cutting fluid, chips formed during machining processes that cannot be washed away result in flaws on the machined surface [2].

Fig. 1
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Cooling–lubrication techniques in machining

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Wet machining/flood cooling is another type of cooling–lubrication technique that involves supplying a constant stream of fluid to the tool work or chip–tool interface during the machining operation [6].Wet machining requires more coolant fluid than MQL or any other cooling system. Gueli et al. [7] investigated the machining performance of slot milling of Inconel 718 under dry and flood cooling conditions by varying the depth of cut and feed rate. The results indicated that the average surface roughness of machined slots is slightly lower for flood coolant machining compared with that of dry machining at a lower depth of cut and feed rate settings. However, Razak et al. [8] analysed wet machining and revealed that nonuniform surface roughness is formed and the final surface presents an alternate shiny and dull area. Meanwhile, Najiha and Rahman [9] discovered that the cutting edge is completely damaged due to attrition and adhesion when wet machining is used with flooded conditions.

MQL is a lubricant sprayed in a fine mist over the workpiece surface and the cutting tool after being mixed with compressed air [10]. Jagatheesan et al. [11] investigated the impact of MQL in the turning process with AISI 4320 alloy steel and showed that MQL provides excellent tool and workpiece interplay, low temperature and minimal cutting force. These results are consistent with recent study of Sun et al. [12], whereby milling force and cutting temperature by using MQL decrease by 12.8% and 9.2%, respectively, compared to that of dry milling.

Cryogenic machining is an environmentally friendly cooling method that typically employs liquid nitrogen as the cooling medium [13]. An external spray cooling cryogenic machining setup was created to spray the cryogenic coolant at the machining zone [14]. Pusavec et al. [15] revealed that cryogenic machining produces lower surface roughness compared with other machining methods. Danish et al. [16] also showed that the cryogenic–LN2 system can significantly reduce the cutting force by 32.1%, tool flank wear by 33.3%, and total energy consumption by 18% compared with dry machining conditions. Table 1 shows some of the previous research that conducted by using different cooling–lubrication techniques.

Table 1 Previous researches that conducted by using different cooling-lubrication techniques
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Nanofluids have shown higher efficiency at heat transfer than conventional fluids in recent studies. Nanofluid is a dispersion of nanometre-sized solid particles in base fluids, such as water and oils [17]. In 2019, Sirin and Kivak [18] reported that better surface roughness values, lower cutting force and better tool wear are observed when hBN nanofluid is used during cutting instead of MoS2 and graphite nanofluids. Chakma et al. [19] discovered that machining aluminium metal matrix nanocomposite with carbon nanotube nanofluid significantly enhances surface quality compared with that in dry environment. Danish et al. [20] concluded that the interlayer slip behaviour of graphene in sunflower oil reduces the contact surface between the tool and workpiece and results in improved surface morphology and machining efficiency.

Hybrid nanofluid, another type of cutting fluid, is a combination of two or more nanoparticles mixed in a medium of base fluid. In 2016, Sidik et al. [21] reported that hybrid nanofluids present higher heat transfer performance and thermophysical properties compared to nanofluids with a single type of nanoparticle. Similarly, Sarkar et al. [22] demonstrated that hybrid nanofluids present higher thermal conductivity than individual nanofluids because of the synergistic effect. The hybrid nanofluid can be produced by suspending (i) various types of nanoparticles (two or more) and (ii) composite nanoparticles in the base fluid.

By using different cooling–lubrication techniques in machining, the machining performance has been extensively investigated with various optimisation methods. For example, Aslantas et al. [23] explored the multi-objective optimisation of micro-turning process parameters, such as cutting speed, feed rate and depth of cut, using response surface method (RSM). The researchers revealed that optimised values for surface roughness of Sa and Sz are 0.50 and 4.16 µm, respectively, and the material removal rate is 239.03 mm3/min. Jamil et al. [24] also used the RSM to design experiments for bone drilling with micro cooling spray technique. The researchers reported that parameters of cutting speed and feed rate highly influence temperature and thrust force, respectively. Danish et al. [25] utilised the RSM to develop an arithmetic model for predicting the maximum temperature of the surface during cryogenic and dry machining of AZ31 magnesium alloy. The investigation revealed that the cutting speed, feed rate and depth of cut present a significant impact on the maximum temperature. Sada and Ikpeseni [26] applied artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models to predict the metal removal rate and tool wear in the turning process of AISI 1050 steel. The researchers indicated that ANN outperforms ANFIS techniques in terms of the accuracy of results. Apart from these methods, genetic algorithm (GA) technique has also been widely used in optimisation studies. Kabil and Kaynak [27] reported that optimisation via GA can effectively increase the machining performance (material removal rate, tool wear and power consumption) of Titanium Ti-5553 alloy.

In the literatures, although many experimental and modelling studies have been conducted on machining performance using various cooling–lubrication techniques, a comprehensive review on the use of nanofluid and hybrid nanofluid in the machining area is still scarce. Therefore, this paper aims to provide a critical and comprehensive review of the current advancements in the application of nanofluids and hybrid nanofluids in machining, particularly in turning, grinding, milling and drilling processes. The multi-response optimisation studies and the mechanism of nanofluid and hybrid nanofluid techniques for improving the machining performance also have been explained. This paper also discusses the thermophysical properties of nanofluids and hybrid nanofluids, such as viscosity, thermal conductivity, stability and wettability.

2 Nanofluid and hybrid nanofluid

The term nanofluid was first coined by Choi and Eastman [17] at Agronne National Laboratory in the USA. Raja et al. [59] stated that nanofluid is created from the dispersion of nanomaterials in base fluids, such as oil and water. This new type of heat transfer fluids has attracted considerable research attention in the last few decades. Nanofluid is more efficient at heat transfer than conventional cutting fluids. As for nanoparticles, the size is 1–100 nm and the widely used nanoparticles are metal and metal oxide nanoparticles.

Yu and Xie [60] demonstrated that nanofluid can be prepared using a one- or two-step method. Base fluids are very important in the stage of nanofluid preparation because of special requirements of nanofluids, such as stable and even suspension, low particle agglomeration, adequate durability, absence of chemical change of particles or fluid, good fluidity, low viscosity and high thermal conductivity [60, 61]. According to Heris et al. [62], base fluids, such as ethylene glycol, oil and water, show poor heat transfer. Therefore, properties should be enhanced to improve their suitability for cutting fluids in practical metal cutting operations. Nanofluids have been widely used in many industry sectors as coolant, lubricant and heat transfer agent due to their advantages of high safety margin, reduced cost, reduced size of the system and improved efficiency of heat conversion [63].

Hybrid nanofluids are considered an extension of nanofluids and formed by suspending two or more different nanoparticles in a base fluid in either composite or mixture form [22]. The benefit of hybrid nanofluid in heat transfer enhancement is due to its synergistic effect. Hybrid nanofluids may present better thermal properties compared with base fluids and nanofluids containing a single nanoparticle [64].

Figure 2 depicts the lubrication mechanism and tribochemical reaction effect of the base fluid with nanoparticles. There are few mechanisms have been observed, such as rolling, interlayer sliding, polishing, mending and protective film. The rolling mechanism refers to nanoparticles that roll like bearings between friction surfaces. If the shear force applied to nanoparticles is sufficiently large to overcome van der Waals force, then sliding between nanoparticles layers will occur and result in an interlayer sliding effect. In terms of the polishing mechanism, nanoparticles will grind the peaks under high pressure and speed to smoothen the surface, reduce friction and improve surface quality. As for mending mechanism, the occurrence of fine nanoparticles subsides and they become adsorbed in the furrows whilst lowering the surface roughness. In addition, nanoparticles also adhere to the contact surface and form a physical tribo-film, which reduces friction and wear [65].

Fig. 2
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Nanofluid mechanisms formed in the cutting zone. Reprinted from [66] with permission from Elsevier

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3 Thermophysical properties of nanofluid and hybrid nanofluid

3.1 Viscosity

Investigation of the viscosity rate is important to reveal the fluidic behaviour of the liquid [67]. According to the previous studies, temperature significantly affects the viscosity of nanofluid and hybrid nanofluid. Dardan et al. [68] revealed that with the increment of temperature at a range of 25–30 °C, the viscosity reduces due to the enhancement of nanofluid movement. However, when the temperature further increases, the movement of nanotube becomes perpendicular to the flow direction, thereby increasing the viscosity. Esfe et al. [69] investigated the viscosity of ZnO–MWCNT/10w40 hybrid nanofluid at a temperature from 5 to 55 °C with a solid volume fraction of 0.05–1% and showed that the viscosity decreases as the temperature increases.

Other than temperature, mass volume fraction is another factor that influences the nanofluid and hybrid nanofluid viscosity. Figure 3 illustrates the variations of dynamic viscosity with nanoparticles volume fraction at different temperatures [70]. The viscosity clearly increases with the increase of the nanoparticle volume fraction. This result is consistent with the findings of Ghasemi and Karimipour [71], wherein the viscosity of CuO–paraffin nanofluid significantly changes when the particle mass fraction is higher than 1.5 wt%. According to Hemmat et al. [72], collisions between nanoparticles and the base fluid caused by van der Waals forces will increase and result in the increase of viscosity when the amount of nanoparticles in a constant volume of a liquid increases.

Fig. 3
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Variation of dynamic viscosity to solid volume fraction at different temperature. Reprinted from [70] with permission from Elsevier

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Notably, the effect of particle size on the viscosity of nanofluids achieves contradictory results. According to Hu et al. [73], the relative viscosity of the nanofluid increases with the increase of the particle size, particularly at a high volume fraction. However, Nithiyanantham et al. [74] indicated that the nanofluid with smaller particles (SiO2: 27 nm) present higher viscosity than those with larger particles (SiO2: 450 and 800 nm) due to the availability of larger surface area. Hu et al. [73] concluded that interparticle spacing and extent of particle aggregation, which are highly influenced by nanoparticle size, are extremely important factors in determining the viscosity of nanofluids.

The viscosity of different types of nanofluids has been extensively investigated. For example, Kazemi et al. [75] investigated the effect of adding silica (SiO2) and graphene (G) nanoparticles as well as their hybrid (G [30%] + SiO2 [70%]) on the viscosity of water. The researchers discovered that the SiO2/water mono-nanofluid indicates the least rise in viscosity amongst the three types of nanofluids at high concentrations. Furthermore, Shahsavar et al. [76] demonstrated that the viscosity of CNT–Fe3O4 hybrid nanofluid is nearly 28.60% higher than that of Fe3O4 nanofluid. The presence of CNT significantly affects the increase of viscosity value on hybrid nanofluids. Ahammed et al. [77] revealed that the viscosity of graphene–alumina hybrid nanofluid is greater than that of the alumina nanofluid but lower than that of the graphene nanofluid. However, Yang et al. [78] indicated that describing the effect of particle type on viscosity is impossible because viscosity values for different nanofluids vary substantially and appear erratic.

3.2 Thermal conductivity

Thermal conductivity refers to the fluid’s ability to absorb and disperse heat to its surrounding. Temperature significantly affects the thermal conductivity of nanofluid and hybrid nanofluid. According to Baby and Sundara [79] and Madhesh et al. [80], the nanofluid’s thermal conductivity increases as the temperature increases. Besides that, many studies have explored the influence of nanoparticle concentration on thermal conductivity. For example, Thakur et al. [81] discovered that the thermal conductivity of SiC nanofluids improves when the nanoparticle concentration increases, as shown in Fig. 4. Hamid et al. [82] also showed that the thermal conductivity of TiO2–SiO2 hybrid nanofluids is higher than that of the base fluid at all concentrations and the maximum thermal conductivity occurs at a concentration of 3.0%. This finding is consistent with data obtained by Sajid and Ali [83], whereby the concentration of nanoparticles is directly proportional to thermal conductivity due to the high thermal conductivity of nanoparticles. However, other reports also claimed that there is no improvement of thermal conductivity, especially at high concentrations [84, 85]. Nfawa et al. [85] reported that the decline of thermal conductivity above a volume concentration of 1% is attributed to the decrease of the specific area-to-volume ratio of nanomaterials and the formation of nanoparticle clusters.

Fig. 4
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Variation of thermal conductivity with concentration of SiC. Reprinted from [81], Copyright 2020, with permission from Springer

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Particle size is another crucial factor that influences the thermal conductivity of nanofluid and hybrid nanofluid. For example, Nithiyanantham et al. [74] compared the effect of SiO2 nanoparticle size (27, 450 and 800 nm) on the thermal conductivity of nanofluid and revealed that larger spherical-shaped SiO2 nanoparticles have lower thermal conductivity than smaller nanoparticles. This observation is consistent with the findings of Loong et al. [86]. Yang et al. [78] stated that the increase of thermal conductivity with small particles is due to enhanced Brownian velocity and surface effect that small particles can achieve.

Apart from the aforementioned factors, particle type also affects the thermal conductivity of nanofluids and hybrid nanofluid. According to Yang et al. [78], carbon nanomaterials (CNTs and graphene), metals (Au, Ag, Cu and Fe) and metal oxides (CuO, Al2O3, TiO2, ZnO, SiC and SiO2) are commonly used nanomaterials. High thermal conductivity in nanoparticles can remarkably enhance the thermal conductivity of nanofluids in general. For example, Loong et al. [86] investigated various types of metal oxide (Al2O3, CuO, MgO, TiO2, SiO, ZnO and ZrO2) water-based nanofluids and discovered that the MgO nanofluid presents the best heat transfer performance amongst these metal oxides due to its high thermal conductivity. In addition, CNTs and graphene nanomaterials are also widely known for their capability to enhance the thermal conductivity of nanofluids due to their exceptional thermal conductivity [64]. Nasiri et al. [87] found that single-walled CNT nanofluid demonstrated the maximum improvement in thermal conductivity over the base fluid. Furthermore, Das et al. [88] showed that 0.1 wt% of graphene nanoparticles can enhance the thermal conductivity of graphene nanofluid by 29% compared with that of deionised water.

3.3 Stability

Nanofluid stability can be affected by many factors, such as dielectric constant of base fluid, pH value, particle size, shape and concentration of nanofluid. Zhu et al. [89] and Kim et al. [90] used zeta potential to examine the stability of copper and Au–water nanofluids, respectively. According to Hwang et al. [91], suspended nanoparticle characteristics (particle morphology and chemical structure) and the base fluid significantly impacted the nanofluid stability. Similarly, Suresh et al. [92] showed that the nanofluid stability reduces with the increase of volume concentration. The stability of the nanofluid seriously influences the machining performance. This view is also supported by Kim et al. [93], who found that the stability of the water-based bohemite alumina nanofluid significantly depends on the particle shape. The researchers concluded that the sedimentation of blade-shaped particles is faster than that of platelet- and brick-shaped particles. Poor stability was also observed with the increase of particle concentration because the van der Waals attractive potential dominated over the electrostatic repulsive potential and caused the agglomeration of nanoparticles that led to sedimentation [94]. According to Sharmin et al. [95], 0.3 vol% of carbon nanotube nanofluid can be used as an efficient coolant to improve the surface roughness, cutting force and tool wear because of its high stability.

3.4 Wettability

Contact angle values in the surface wettability test represent the ability of lubricants to wet metal surfaces, and a low contact angle value (high wettability) increases the chances of tribo-film formation [96]. Kumar et al. [97] revealed that the alumina–MoS2 hybrid nanofluid recorded the smallest contact angle compared with the alumina nanofluid and base fluid. This result is consistent with those of Xie et al. [96]. The lower contact angle of SiO2:graphene combination nanofluids than that of pure water and graphene nanofluids indicated that the workpiece surface is more wettable with SiO2:graphene combination nanofluids than that with pure water and graphene nanofluids.

According to Kumar et al. [98], physical and morphological features of nanoparticles significantly affect wettability characteristics. Figure 5 shows that the contact angle of hard nanoparticles, such as B4C, ZnO and Al2O3, remarkably decreases compared with that of soft nanoparticles (WS2, MoS2 and h-BN) when added to the base fluid (DI water).

Fig. 5
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a 3D profile of refined silicon nitride surface and b–k contact angle between the surface and various nanofluids. Reprinted from [98] with permission from Elsevier

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4 Nanofluid and hybrid nanofluid in the machining process

4.1 Turning

Turning or lathe is a type of machining operation that removes unwanted materials from the workpiece using a single-point cutting tool to obtain the final product. A preshaped material was attached to the fixture on the turning machine and then rotated at a high-speed motion during the lathe operation [99]. Due to the regular contact of workpiece and cutting tool, the cutting zone generated a considerable amount of heat and deteriorated the surface finish of the product. Lubricants and coolants are normally supplied to the workpiece–tool intersection to solve this issue. Various types of nanofluids have been applied to the turning operation due to their great advantages. For example, Amrita et al. [100] revealed that the addition of nanographite increases the heat carrying capacity of soluble oil, thereby improving tool wear, surface roughness and cutting force compared with dry and wet conditions. Saravanakumar et al. [101] investigated the turning process with and without silver nanoparticles under different machining conditions. The researchers claimed that tool tip temperature can reduce with the inclusion of 0.5 vol% of silver nanoparticles, mainly due to the increase of thermal conductivity of the silver nanofluid caused by the interfacial layer between nanoparticles and the liquid interface as well as the Brownian motion of nanoparticles.

Su et al. [102] turned the AISI 1045 using vegetable-based oil mixed with graphite nanoparticles through the MQL method. The results indicated that the temperature and cutting force remarkably reduce when the nanofluid MQL is used. Li et al. [103] revealed that the efficiency of cooling and lubrication enhances as the frictional force and cutting temperature significantly reduces with the addition of graphene oxide nanosheets into the coolant. As illustrated in Fig. 6, under nanofluid minimum quantity lubrication (NMQL), tool wear evidently decreased by 18.17% and 4.54% and surface quality improved by 55.58% and 5.48% compared with those under dry and flood machining, respectively [104].

Fig. 6
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Tool wear and surface roughness under dry, wet and NMQL. Reprinted from [104], Copyright 2018, with permission from Springer

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Duc et al. [105] compared the turning performance of MoS2 and Al2O3 nanofluids on 90CrSi steel and demonstrated that the Al2O3 nanofluid achieves better surface finish compared with the MoS2 nanofluid. Khan et al. [106] compared the machining performance of Ag and Cu nanofluids and showed that Cu-based nanolubricant produces a lower coefficient of friction, cutting force and cutting temperature than Ag-based nanolubricants. Yi et al. [107] indicated that the respective tool flank wear reduces by about 44.1%, 53.9% and 71.3% when 0.1, 0.3 and 0.5 wt% of graphene oxide (GO) nanofluids are used instead of conventional coolants (Fig. 7). MQL turning of SiC–based nanofluids exhibited better performance than MQL turning in terms of temperature, cutting force and surface roughness height [81]. Other important studies that utilise nanofluids in the turning process are summarised in Table 2.

Fig. 7
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SEM image of tool flank wear using various types of coolant: a conventional coolant b 0.1 wt.% GO coolant c 0.3 wt.% GO coolant d 0.5 wt.% GO coolant. Reprinted from [107] with permission from Elsevier

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Table 2 Application of nanofluid in turning
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Hybrid nanofluids have been widely used as coolant in the machining process in recent years. For example, Kumar et al. [97] demonstrated that the Al–MoS2 hybrid nanofluid shows a significant reduction of 7.35% in cutting force (Fz), 18.08% in feed force (Fx), 5.73% in thrust force (Fy) and 2.38% in surface roughness compared with the Al2O3 mixed nanofluid. The results showed that the reduction in cutting force and surface roughness can be attributed to the entrapment of nanoparticles in porous abrasives and tribo-film layers consisting of the Mo–S–P (molybdenum, sulphur and phosphorus) chemical complex at the machining zone that generates lubrication due to the continuous shearing and alignment. Gugulothu and Pasam [108] also presented that minimum surface roughness can be obtained using the CNT/MoS2 hybrid nanofluid compared with that of dry machining and conventional cutting fluids. Jamil et al. [109] reported that Al2O3–MWCNT hybrid nanofluid can reduce 8.72% of surface roughness and 11.8% of cutting force whilst increasing the tool life by 23% compared with cryogenic cooling. Kumar and Krishna [110] examined the efficiency of CuO and Al2O3 hybrid nanofluids at various weight percentages during the turning process of AISI 1018 steel. The findings indicated that the combination of CuO and Al2O3 (50:50) can reduce the surface roughness value to a maximum of 13.72%. Moreover, the combination of CuO and ZnO with coconut oil can lead to a better surface finish than other syntheses [111]. Khan et al. [112] compared the turning performance of AISI52100 steel using base fluid, Al2O3 nanofluid and Al–GnP hybrid nanofluid. The results revealed that the hybrid nanofluid MQL achieves the best surface quality, cutting power, MRR and tool life compared with other cutting fluids.

4.2 Drilling

Drilling is a simple machining process that uses a spiral fluted tool to remove the material in a circular motion to create a hole [123]. High temperature at the cutting zone will affect the surface quality during machining because the tip of the drill bit begins to burn and wear [124].

Nanofluids have recently been utilised to improve the drilling performance. For example, Chatha et al. [125] revealed that the use of nanofluids with MQL enhances the number of drilled holes whilst decreasing thrust forces and drilling torques compared with the use of pure MQL, wet and dry drilling conditions. These findings are consistent with those of Huang et al. [126]. The researchers stated that the use of 2 wt% of nanodiamond in micro drilling with MQL can reduce the force, torque and burr formation around the holes and thus increase the cutting tool life with the reduction of force. Liew et al. [127] performed the drilling process on titanium alloy using carbon nanofibre nanofluid. When compared the results with those utilising pure deionised water, the carbon nanofibre nanofluid produces better surface finish and lower cutting temperature. Compared with the coconut oil–based fluid, Muthuvel et al. [128] stated that copper nanofluid can reduce the surface roughness and flank wear by 71% and 53%, respectively, with the optimum setting predicted via ASN-RSM.

Babu and Muthukrishnan [129] investigated the effect of copper nanofluid under MQL condition in the drilling of AA 5052 alloy. The results indicated that the use of copper nanofluid significantly decreases the cutting temperature, tool wear and surface roughness compared with the application of oil lubrication and dry machining. Moreover, nanofluids under MQL generated a thin protective cover on the machined zone and thus lowered the machining temperature and friction at the tool–workpiece interface whilst reducing the adhesion wear and chipping on the inserts. This view is further supported by Khanafer et al. [130]. When using MQL nanofluid, the tribo-film that formed between the cutting tool and the inner drilled hole (Fig. 8) can reduce the rubbing action caused by the rolling effect, thus reducing the cutting heat and burrs cut around the circumference of the drilled hole (Fig. 9). Other important studies that use nanofluids in the drilling process are listed in Table 3.

Fig. 8
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Schematic of nanofluid mist mechanisms in the drilling zone. Reprinted from [130], Copyright 2020, with permission from Springer

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Fig. 9
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SEM of burr formation using a flood b pure MQL c MQL-nanofluid. Reprinted from [130], Copyright 2020, with permission from Springer

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Table 3 Application of nanofluids in drilling
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4.3 Grinding

Grinding is an abrasive machining process that is commonly used in the finishing process for component that requires accurate dimensional tolerances and smooth surface textures [123]. Surface texture and precision obtained through grinding are 10 times greater than those via turning or milling. Abrasive grains come into controlled contact with the rotating wheel in a binder during the grinding process. According to Sanchez et al. [138], the heat generated during the grinding operation may negatively affect the surface integrity in the form of metallurgical transformations and oxidation. Coolants and lubricants were used to reduce the resistance within the wheel and workpiece to protect them from unfavourable conditions, such as heat distortion, burning, undesirable waste, tensile stress and phase transformation. Moreover, coolants and lubricants can also wash out chips, free the wheel surface from contaminations and thus reduce heat generation and grinding force [139]. Tu et al. [140] concluded that the use of cutting fluid in the grinding process is an important factor to ensure that the fluid can penetrate the contact zone and maintain its lubricating effectiveness.

Wang et al. [141] explored the temperature distribution of 45 steel during the grinding process under four cooling and lubrication conditions, namely, dry, MQL, nanofluid and flood conditions. The researchers discovered that the grinding zone temperature is significantly reduced because of the excellent heat transfer property of nanofluids. Setti et al. [142] demonstrated that the tangential force and grinding zone temperature decrease when nanofluids are utilised. In 2017, Li et al. [143] carried out the grinding process on Ni-based alloy using nanofluid MQL with six different types of nanoparticles. The CNT nanofluid presented the lowest grinding temperature amongst the investigated nanofluids due to its highest heat transfer coefficient. Dambatta et al. [144] showed that forces and surface roughness decrease significantly when SiO2 nanofluid is used. According to Wang et al. [145], nanoparticles can easily penetrate into the sliding contact, form a protective film and convert sliding friction into sliding–rolling composite friction between grains and the workpiece (Fig. 10a). Furthermore, nanoparticles can fill cavities and incomplete spaces on the workpiece surface, repair dents on the friction surface and result in mending effect (Fig. 10b) that can improve workpiece surface quality. Previous studies that utilise nanofluids in the grinding process are listed in Table 4.

Table 4 Application of nanofluids in grinding
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Similar to nanofluids, hybrid nanofluids have also been used in the grinding process. Zhang et al. [146] presented that MoS2/CNT hybrid nanoparticles can significantly improve surface quality compared with the pure nanofluid with a single nanoparticle (Fig. 11). These findings are consistent with those of Zhang et al. [147]. The study noted that hybrid nanofluid MQL grinding is superior over pure nanofluid MQL grinding due to the ‘physical synergistic effect’. Hamid et al. [82] exhibited that MoS2–WS2 hybrid nanofluid reduces the specific grinding energy and grinding force by 39% and 27%, respectively, when compared with deionised water; meanwhile, the hybrid nanofluid also reduces chipping layer depth and surface roughness by 86% and 41%, respectively, compared with flood grinding.

Fig. 10
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a Protective films and b mending effect of nanoparticles in grinding. Reprinted from [145] with permission from Elsevier

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Fig. 11
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SEM image of workpiece using different machining conditions. Reprinted from [146] with permission from Elsevier

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4.4 Milling

The milling process is operated by removing the chip with rotational and multi-teeth tool on a fixed workpiece [123]. Many studies on the use of nanofluids in the milling process have demonstrated significant improvement in machining performance. For example, Kim et al. [168] indicated that the hBN nanofluid MQL with chilly CO2 gas can produce better surface finish and reduce the coefficient of friction and tool wear. Their findings are supported by Kim et al. [169], and the results showed that chilly CO2 gas with only 0.1 wt% of nanodiamond nanofluid in MQL can reduce milling forces and coefficient of friction. According to Sirin and Kivak [18], hBN nanofluid can obtain better surface roughness, lower cutting force and improved tool wear compared with the MoS2 and graphite nanofluid. The machined surface with graphene nanofluid MQL can also produce a smoother surface without adhesion and large furrows compared with machining under dry and gas conditions [170]. The summary of previous studies that utilise nanofluids in the milling process is presented in Table 5.

Table 5 Application of nanofluids in milling
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Researchers have also attempted to examine the milling machining performance using hybrid nanofluids. Sahid et al. [171] revealed that the combination of cutting parameters and MQL-hybrid nanocoolant is critical in attaining enhanced surface roughness, satisfactory material removal rate and reduced tool wear. Jamil et al. [172] reported that due to the tribo-film formation of Al2O3-MWCNT hybrid nanofluid, variable-sized nanoparticles function as spacers to reduce the severity of tool rubbing on the workpiece surface, fill microvoids on the workpiece surface and function as ball bearing in the rolling action. As a result, tool life and surface finish can be improved.

5 Optimisation of machining performances under nanofluid and hybrid nanofluid

Many research papers have been published demonstrating the effectiveness of optimisation techniques for machining process under nanofluid and hybrid nanofluid. For example, Chakma et al. [19] utilised the Taguchi orthogonal array design to optimise the turning parameters of an aluminium metal matrix nanocomposite. The researchers discovered that high cutting speeds and low feed rates can significantly improve the surface quality when carbon nanotube nanofluid is used. Liew et al. [186] carried out multi-response optimisation in turning D2 steel using the Taguchi-RSM integration method. The experimental results revealed that a cutting speed of 144.58 m/min, feed rate of 0.14 mm/rev and usage of carbon nanofiber nanofluid as coolant produce the optimal tool wear and surface finish.

Besides that, in the milling of Inconel 718, Barewar et al. [187] incorporated the Taguchi grey relational analysis (TGRA) to investigate the effect of cutting speed, feed rate and machining environment (Ag/ZnO hybrid nanofluid-MQL, dry and MQL) on the surface roughness and cutting temperature. They concluded that at the cutting speed of 30 mm/min, feed rate of 0.036 mm/tooth and Ag/ZnO hybrid nanofluid-MQL cutting environment, multi-response–optimised machining performance can be achieved. Jamil et al. [172] also reported that by using Taguchi method, the desirability function attempts to obtain a balanced operating level to achieve minimal energy consumption, enhanced surface quality and high material removal rate in the milling of Ti–6Al–4 V alloy when hybrid nanofluids Al2O3-MWCNTs is added.

Furthermore, Junankar et al. [188] used multi criteria decision-making hybrid approach to optimise the turning parameters under CuO and ZnO nanofluid-MQL. They found that CuO nanofluid showed better enhancement by reducing the surface roughness and machining temperature as compared to ZnO nanofluid. Huang and Chen [189] used ANN and TGRA to develop a highly accurate micro drilling predictive model and reported that the parameter combination for predicting the optimal micro drilling force and torque deviated from the experiment results by just 0.44% and 1.24%, respectively. Garg et al. [132] revealed that genetic programming (GP) has performed better than the ANFIS model on the thrust force and material removal rate in micro drilling process.

6 Challenges and future works

On the basis of previous studies, maintaining the stability of nanofluids and hybrid nanofluids remain challenging in actual application despite their ability to improve the machining performance compared with conventional fluids. The stability of nanofluid was mainly affected by the agglomeration and the formation of large clusters of suspended particles [94]. The nanoparticle agglomeration causes not only the settlement and clogging of micro channels but also a reduction in the thermal conductivity of nanofluids [60]. Therefore, surfactants have been used to enhance the stability of nanofluid [60]. Although the addition of surfactant can effectively enhance nanoparticle dispersibility, surfactants may cause some problems. For example, the addition of surfactants may contaminate heat transfer media; produce foam during the heating process [190] and likely degrade the viscosity, thermal conductivity and chemical stability of nanofluids when an excessive amount of surfactant is added [191]. Therefore, the stability of nanofluid and hybrid nanofluid requires further investigation. The machinist should use a stirrer or ultrasonic vibration in the coolant tank to assist the machining process and ensure excellent output when nanofluids or hybrid nanofluids are used as coolant in machining. The stirrer or ultrasonic vibration of nanofluids may help disperse nanoparticles in the suspension and avoid sedimentation during the machining process.

In addition to the stability issue of nanofluids, high production cost is another major factor that may hinder the practical industrial application of nanofluids and hybrid nanofluids. The entire procedure adds to the overall cost because it requires the appropriate selection of nanoparticles, natural extracts, equipment and hardware [192]. Hence, the researcher or machinist may consider using a green nanofluid that contains eco-friendly nanoparticles to reduce the cost. The use of green nanofluids as coolant in machining combined with optimum machining parameters that affect the process should be investigated further.

7 Conclusion

An overview of various cooling–lubrication techniques as well as the use of nanofluids and hybrid nanofluids in turning, milling, grinding and drilling processes is presented in this study. Thermal conductivity and viscosity of nanofluids and hybrid nanofluids are strongly affected by the temperature, mass volume fraction, nanoparticle size and type. The majority of experimental studies revealed that nanofluids and hybrid nanofluids can achieve better machining performance than other cooling–lubrication techniques due to their heat transfer and lubrication capabilities. The formation of a tribo-film, the sliding–rolling action and the mending effect of nanoparticles are the main mechanisms that contribute to the improvement of surface finish and reduction of cutting temperature, tool wear and cutting force during machining with nanofluids and hybrid nanofluids. Multi-response optimisation using various optimisation techniques such as Taguchi, RSM and ANN is useful to predict the optimum parameters for enhancing the machining efficiency under nanofluid and hybrid nanofluid cutting environment.