Open Access

Table 1

Literature review summary of MQL and optimization.

Reference Paper Process Material Findings
Tosun and Pihtili [9] Milling 7075 aluminum alloy Optimized values based on grey relational analysis give better surface finish and maximum MRR. The feed rate is significant input parameter found for milling performance.
Liu et al. [10] Turning Titanium alloy Coupling method of response surfaces under MQL improves manufacturability of titanium alloy in terms of cutting forces and surface roughness. Feed rate found the most significant parameter.
Gaitonde et al. [11] Turning Brass Minimum surface roughness value varies from 0.23–0.5 μm is obtained using optimized MQL and cutting conditions obtained by Genetic Algorithm.
Ali et al. [12] Turning Compacted graphite iron (CGI) Resultant cutting forces reduced by 2-5%, surface roughness by 25%, flank wear by 10%, crater width by 30% under MQL compared to dry condition.
Hadad [13] Surface grinding 100Cr6 steel Tangential force and surface roughness models were developed for improving process performance and to find the effects of MQL parameters such as oil flow rate, air pressure and nozzle distance.
Pavani et al. [14] Turning AISI 1040 steel 3%wt boric acid powder in coconut oil and soybean oil improves the machining performance in-terms of tool temperature, cutting forces and surface roughness under MQL.
Rabiei et al. [15] Surface grinding Soft steel: CK45&S305Hardsteel:HSS& 100Cr6. Better values of cutting forces, coefficient of friction and surface finish reported for grinding hard steel. The surface roughness value of soft steel (0.42 μm) improved using optimized values of parameters obtained by Genetic Algorithm (GA).
Sarikaya and Giilli [88] Turning Haynes 25 The vegetable base cutting fluid, gives minimum values of flank wear, notch wear, and surface roughness based on optimized process parameter obtained by Grey Relational Analysis (GRA) technique.
Do and Hsu [16] Hard Milling AISI H13 steel (SKD 61) Lowest value of surface roughness (0.145 μm) is obtained using optimized input process parameters: cutting speed (60 m/min), feed rate (0.01 mm/tooth), depth of cut (0.3) and workpiece hardness (45 HRC) obtained by Taguchi method.
Gupta et al. [17] Turning Titanium (Grade-II) Lower values of tangential force (132.52), tool wear (0.31), surface roughness (0.53) and tool-chip contact length (0.793) are obtained using optimized process parameter determined by Particle Swarm Optimization (PSO) technique.
Chakule et al. [18] Surface grinding AISI D3 steel Lowest surface roughness (0.124 μm), coefficient of friction (0.391) specific grinding energy (24.32 N/mm2), and temperature (29.07 ⁰C) is obtained under MQL.
Mia et al. [19] Milling AISI 4140 steel Grey based Taguchi method gives lowest values of surface roughness (0.67 μm), and cutting force (6.5 N) using optimized input parameters.
Khan et al. [20] Surface grinding AISI D2 steel Grey-Taguchi method reduces temperature up to (67.4%), cutting forces (79.26%), compared to dry grinding using optimized values of process parameters.
Viswanathan et al. [21] Turning Magnesium alloy (AZ91D) Optimum conditions of MQL system obtained from Taguchi based GRA shows improvement in flank wear by 16.66%, surface roughness by 52.94%, and cutting temperature by 11.36%.
Muaz and Choudhury [22] Milling AISI 4340 Optimized process parameters based on Taguchi-Gray relational analysis and multi-objective genetic algorithm gives better process performance using 10 wt.% boric acid in water based cutting fluid.
Tamang and Chandrasekaran [23] Turning Inconel-825 Lowest values of surface roughness (0.39 μm), tool wear (15.37 μm) and cutting temperature (56.47 ⁰C) using optimized input process values by Genetic Algorithm is obtained.
Awale et al. [24] Plunge grinding AISI H13 tool steel Average droplet size (51.03 μm) at nozzle angle (12°) is obtained using optimal mist parameters: air pressure (4 bar), flow rate (200 ml/h), and stand-off distance (50 mm) determined by grey relational analysis. The lowest grinding force, specific energy, grinding temperature, and surface roughness are also reported.
Abas et al. [25] Turning Aluminum alloy 6026-T9 MQL condition gives lowest surface roughness (1.14 μm) and maximum tool life and MRR (275 cm3/s) using optimized process parameters obtained by Taguchi SN ratios.
Shastri et al. [26] Turning Titanium alloy (Grade II) Multi- Cohort Intelligence (CI) optimization algorithm under MQL machining reduces cutting force by 8%, tool wear by 42%, tool-chip contact length by 38%, and surface roughness by 15% compared with PSO.
Gupta et al. [27] Turning 2205 duplex steel MQL nozzle position (flank + rake direction) produces the lowest surface roughness (1.55 μm). Moreover, dual-jet MQL gives lowest energy consumption (229 kJ) and tool wear (0.15 mm).
Van and Nguyen [28] Roller- Burnishing Hardened steel 5145 Decreased of cylindricity, circularity and surface roughness by 53.14%, 57.83%, and 72.97% respectively is obtained using optimal input values determined by artificial neural network.

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