Open Access
Review
Table 2
Summary of literature review of nanofluid MQL and optimization.
| Reference Paper | Nano fluid | Nano particle size | Process | Material used | Findings |
|---|---|---|---|---|---|
| Mao et al. [47] | Al2O3 nanoparticles mixed in deionized water | 60 nm | Surface grinding | AISI 52100 steel | Reduction of specific tangential grinding force (1.90 N/mm), coefficient of friction (0.3), surface roughness (0.2 μm) and grinding temperature (350 °C) obtained using 0.75 wt% concentration of nanofluid. |
| Setti et al. [1] | Al2O3 and CuO mixed in water | 40 nm | Surface grinding | Ti-6Al-4V | Water based Al2O3 nanofluid improves grindability of material by reducing tangential grinding forces, coefficient of friction, grinding zone temperature. |
| Nam et al. [63] | Nanodiamond particles in paraffin and vegetable oils (concentration: 2, 4% vol.) | 30 nm | Micro-drilling | Aluminum | The optimized process parameters obtained by genetic algorithm gives minimum drilling torques and thrust forces and maximized material removal rate (MRR). |
| Gupta et al. [17] | Aluminium oxide (Al2O3), molybdenum disulfide (MoS2) and graphite mixed in vegetable oil (concentration: 3 wt.%) | 40 nm | Turning | Titanium alloy | Optimized conditions such as cutting speed (215 m/min), feed rate (0.10 mm/rev), approach angle (83°) and graphite based nanofluid reduces the cutting forces, tool wear, surface roughness and cutting temperature. Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO) found better technique of optimization. |
| Wang et al. [49] | MoS2,SiO2, Nanodiamond, CNT, Al2O3,and ZrO2 nanoparticles mixed in palm oil (6% mass fraction) | CNT (average length 10–30 μm) & other nanofluid (50 nm) | Grinding | Nickel alloy GH4169 | The reduction of sliding friction coefficient (0.348), specific sliding grinding energy (82.13 J/mm3), and surface roughness (0.302 μm) obtained using Al2O3 nanofluid. |
| Patil and Patil [64] | Water based Al2O3 and CuOnanofluids | 100 nm | Surface grinding | En8 flat plate | The best optimized process parameters such as CuO nanofluid (2% concentration), depth of cut (5 μm), coolant flow rate (5 ml/min), feed rate (2000 mm/min), and wheel speed (35 m/s) obtained by multi-objective grey relational analysis. It gives better G ratio and surface finish. |
| Wang et al. [50] | Al2O3 mixed in palm oil | 50 nm | CNC surface grinder | Ni-based alloy GH4169 | The better tribological performance such as force ratio (0.28), specific energy (65 J/mm3), G-ratio (30), and surface roughness (0.301 μm) reported using nanofluid of 1.5 vol.% concentration. |
| Paul et al. [51] | MWCNT and Alumina in de-ionized water | 40 nm | Surface grinding | Ti-6Al-4V plates | 1 wt% MWCNT nanofluid gives the lowest grinding forces (1.04 N/mm), specific energy (62.4 J/mm3) and surface roughness (0.62 μm) using optimized process parameters. |
| Setti et al. [53] | Al2O3 nanoparticle mixed in water (0.1 vol.%) | 40 nm | Surface grinding | Ti-6Al-4V | Al2O3 nanofluid reduces coefficient of friction, surface roughness whereas wheel life improved. |
| Chakule et al. [65] | Al2O3 nanoparticle mixed in distilled water | 30–50 nm | Horizontal surface grinding machine | EN31 soft and hard type | Better surface finish is obtained for hardened material. Optimized values of Jaya algorithm gives reduction of surface roughness (0.138 μm) value for soft steel. |
| Seyedzavvar et al. [54] | Graphite nanoparticles mixed in distilled water plus 20 vol.% canola oil | 32 nm | Surface grinding | AISI 1045 steel | Graphite nanofluidof 0.35 vol.% concentration under MQL gives lower specific tangential force, force ratio and surface roughness. |
| Sirina and Kıvak [66] | hBN, graphite, MoS2 mixed in vegetable oil (concentration: 0.25, 0.50, 0.75 and 1.0 vol.%) | 80 nm | Milling | Inconel X-750 superalloy | Optimized value of hBN nanofluid gives superior performance in-terms of surface roughness, cutting force and tool wear using 0.50 vol.% nanofluid concentrations. |
| Sharmin et al. [67] | CNT-water based nanofluids (concentration: 0.2, 0.3, 0.4 and 0.5%) | Single walled, size less than 30 nm | Milling | 42CrMo4 hardened steel | Stable nanofluid concentration of 0.3 vol.% gives reduction in temperature by 29%, surface roughness by 34%, cutting forces by 33% and reduction in tool wear by 39%. |
| Seyedzavvar et al. [68] | CuO added in vegetable oil | 20 nm | Surface grinding | AISI 1045 steel | 1% mass fraction of CuO nanoparticles in base fluid reduces wear rate by 71.2%, tangential grinding force by 20%, and surface roughness by 30% compared to lubricant without nanoadditive. |
| Ibrahim et al. [69] | Graphene nanoplatelets mixed in palm oil (0.1 wt.% to 0.4 wt.%) | Diameter (5–10 μm),Thickness (3–10 nm) | Grinding | Ti-6Al-4V alloy | GNPs (0.1 wt. %) decreased the cutting forces and save the energy by 91.78% compared to dry cutting. The surface quality improved using nanofluid. |
| Manoj Kumar and Ghosh [59] | MWCNT mixed in de-ionized water | – | Surface grinding | Hardened AISI 52100 steel | Reduction of specific energy, force ratio, and temperature is obtained. The maximum enhancement of thermal conductivity is obtained using 1 wt.% nanofluid concentration. |
| Prashantha Kumar et al. [70] | Al2O3, CuO mixed in emulsified base fluid (concentrations: 0.3, 0.5 and 0.7 vol.%) | 30–50 nm | Turning | Duplex stainless steel (DSS-2205) | Better result of surface roughness and cutting force by Al2O3 nanofluid (0.7%) is obtained using optimized process parameters based on Desirability Function Analysis. |
| Tiwari et al. [71] | Al2O3, CuO, TiO2 mixed in water (concentration: 0, 1,2,3,4,5 and 6%) | Average diameter 50-75 nm | Grinding Milling Drilling Turning | Analysis through MATLAB | Input parameters such as thermal conductivity, specific heat, viscosity and density whereas responses like surface roughness, tool wear, machining temperature were considered. Better result obtained by Al2O3nanofluid using 5-6 vol.% concentration. |
| Yiicel et al. [72] | MoS2 mixed in mineral oil (0.6% vol. conc.) | 80 nm | Turning | AA 2024 T3 aluminum alloy | Improvement of surface roughness and surface topography is obtained. Built-up edge formation is also significantly reduced. |
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.