Summary of literature review of nanofluid MQL and optimization.
|Reference Paper||Nano fluid||Nano particle size||Process||Material used||Findings|
|Mao et al. ||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. ||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. ||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. ||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. ||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 ||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. ||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. ||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. ||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. ||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. ||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 ||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. ||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. ||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. ||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 ||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. ||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. ||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. ||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.|
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