Abstract
Motivated by the growing imperative for greener and more sustainable machining practices, this investigation evaluates the potential of eco-benign lubricants to mitigate frictional interactions at the tool–workpiece interface. Particular attention is directed toward nanofluids derived from vegetable oils, which are examined as viable and environmentally responsible substitutes for conventional metalworking fluids. In the present study, coconut oil was systematically enhanced by dispersing alumina and silica nanoparticles in the range of 0–1.4%, after which the prepared nanofluid samples were thoroughly examined using spectroscopic techniques to determine the formulation with the highest dispersion stability. Hard milling trials on Inconel 718 were then carried out under four distinct lubrication environments: dry cutting, pure coconut oil, coconut oil enriched with 0.8% alumina nanoparticles, and coconut oil containing 0.8% silica nanoparticles. Among these conditions, the alumina-based nanofluid delivered the most pronounced improvements, achieving decreases of 43.089% in surface roughness, 27.397% in cutting force, 23.437% in cutting temperature, and 45.833% in tool wear relative to dry machining. Capitalizing on the superior capability of this nanofluid, a Taguchi L27 experimental framework was subsequently executed under the optimal lubrication condition, and the resulting data were further optimized using a Genetic Algorithm to determine the best combination of machining parameters. Experimental confirmation of the optimized parameters showed strong alignment with model predictions, with the average deviation limited to just 2.6%. Overall, the findings clearly demonstrate that nanoparticle-infused bio-lubricants substantially enhance machining performance, extend tool longevity, and offer a promising pathway toward more sustainable manufacturing practices.
Data availability
Data supporting this study’s findings are available from the corresponding author upon reasonable request.
References
Sen, B., Bhowmik, A., Prakash, C. & Ammarullah, M. I. Prediction of specific cutting energy consumption in eco-benign lubricating environment for biomedical industry applications: Exploring efficacy of GEP, ANN, and RSM models. AIP Adv. 14 (8). https://doi.org/10.1063/5.0217508 (2024).
Pasupuleti, T., Natarajan, M., Raj, G. S., Silambarasan, R. & Somsole, L. N. Process Parameter Prediction for Advanced Machining of Copper-Nickel Alloy Turbine Components (2025-28-0155). SAE Technical Paper. (2025). https://doi.org/10.4271/2025-28-0155
Zhu, D. et al. Robust macroscale superlubricity in humid air via designing amorphous DLC/crystalline TMDs friction pair. Adv. Funct. Mater. 34 (30), 2316036 (2024).
Sen, B. et al. Alumina-enriched sunflower bio-oil in machining of Hastelloy C-276: a fuzzy Mamdani model-aided sustainable manufacturing paradigm. Sci. Rep. 14 (1), 29194. https://doi.org/10.1038/s41598-024-80254-z (2024).
Singh, H. et al. Artificial neural network modeling and experimental analysis of erosion resistance in tungsten carbide-coated CA6NM stainless steel. Int. J. Adv. Manuf. Technol. 1–21. https://doi.org/10.1007/s00170-025-16018-4 (2025).
Sen, B. et al. Technoeconomic and environmental analysis of cryogenic and MQL-assisted machining of Hastelloy X. Sci. Rep. 15 (1), 21816. https://doi.org/10.1038/s41598-025-07526-0 (2025).
Kumar, G., Sen, B., Ghosh, S. & Rao, P. V. Strategic enhancement of machinability in nickel-based superalloy using eco-benign hybrid nano-MQL approach. J. Manuf. Process. 127, 457–476. https://doi.org/10.1016/j.jmapro.2024.08.015 (2024).
Sen, B. et al. Minimum quantity blended bio-lubricants for sustainable machining of superalloy: An MCDM model-based study. AIP Adv. 14 (7). https://doi.org/10.1063/5.0222561 (2024).
Singh, S. et al. Balancing growth and green the impact of economic growth financial development technological innovation and economic complexity on carbon neutrality in P5 Plus 1 nations. Discover Sustain. 6 (1), 1–30. https://doi.org/10.1007/s43621-025-01684-x (2025).
Sen, B. et al. Exploring cryo-MQL medium for hard machining of hastelloy C276: a multi-objective optimization approach. Int. J. Interact. Des. Manuf. (IJIDeM. 1–14. https://doi.org/10.1007/s12008-024-02069-6 (2024).
Liu, K. et al. A method for the dynamic characteristic analysis of a rotor-rolling bearing system influenced by elastohydrodynamic lubrication. J. Sound Vib. 608, 119075 (2025).
Sen, B., Mia, M., Krolczyk, G. M., Mandal, U. K. & Mondal, S. P. Eco-friendly cutting fluids in minimum quantity lubrication assisted machining: a review on the perception of sustainable manufacturing. Int. J. Precision Eng. Manufacturing-Green Technol. 8, 249–280. https://doi.org/10.1007/s40684-019-00158-6 (2021).
Zou, Y., Tang, S., Guo, S. & Song, X. Tool wear analysis in turning inconel-657 using various tool materials. Mater. Manuf. Processes. 39 (10), 1363–1368 (2024).
Han, T., Zhang, S. & Zhang, C. Unlocking the secrets behind liquid superlubricity: A state-of-the-art review on phenomena and mechanisms. Friction 10 (8), 1137–1165 (2022).
Sharma, A. K., Tiwari, A. K. & Dixit, A. R. Effects of Minimum Quantity Lubrication (MQL) in machining processes using conventional and nanofluid based cutting fluids: A comprehensive review. J. Clean. Prod. 127, 1–18. https://doi.org/10.1016/j.jclepro.2016.03.146 (2016).
Zhang, Y., Li, C., Jia, D., Zhang, D. & Zhang, X. Experimental evaluation of MoS2 nanoparticles in jet MQL grinding with different types of vegetable oil as base oil. J. Clean. Prod. 87, 930–940. https://doi.org/10.1016/j.jclepro.2014.10.027 (2015).
Jia, D. et al. Specific energy and surface roughness of minimum quantity lubrication grinding Ni-based alloy with mixed vegetable oil-based nanofluids. Precis. Eng. 50, 248–262. https://doi.org/10.1016/j.precisioneng.2017.05.012 (2017).
Duan, C. et al. Smart polymer self-lubricating material: Optimal structure of porous polyimide with base oils for super-low friction and wear. Friction 13 (8), 9441007 (2025).
Gajrani, K. K., Suvin, P. S., Kailas, S. V. & Mamilla, R. S. Thermal, rheological, wettability and hard machining performance of MoS2 and CaF2 based minimum quantity hybrid nano-green cutting fluids. J. Mater. Process. Technol. 266, 125–139. https://doi.org/10.1016/j.jmatprotec.2018.10.036 (2019).
Singh, H., Sharma, V. S., Singh, S. & Dogra, M. Nanofluids assisted environmental friendly lubricating strategies for the surface grinding of titanium alloy: Ti6Al4V-ELI. J. Manuf. Process. 39, 241–249. https://doi.org/10.1016/j.jmapro.2019.02.004 (2019).
Pal, A., Chatha, S. S. & Sidhu, H. S. Experimental investigation on the performance of MQL drilling of AISI 321 stainless steel using nano-graphene enhanced vegetable-oil-based cutting fluid. Tribol. Int. 151, 106508. https://doi.org/10.1016/j.triboint.2020.106508 (2020).
Şirin, Ş., Sarıkaya, M., Yıldırım, Ç. V. & Kıvak, T. Machinability performance of nickel alloy X-750 with SiAlON ceramic cutting tool under dry, MQL and hBN mixed nanofluid-MQL. Tribol. Int. 153, 106673. https://doi.org/10.1016/j.triboint.2020.106673 (2021).
Öndin, O., Kıvak, T., Sarıkaya, M. & Yıldırım, Ç. V. Investigation of the influence of MWCNTs mixed nanofluid on the machinability characteristics of PH 13 – 8 Mo stainless steel. Tribol. Int. 148, 106323. https://doi.org/10.1016/j.triboint.2020.106323 (2020).
Sen, B. et al. Comparative Analysis of NSGA-II and TLBO for Optimizing Machining parameters of Inconel 690: A sustainable Manufacturing paradigm. J. Mater. Eng. Perform. 1–16. https://doi.org/10.1007/s11665-024-10539-x (2024).
Shi, K., Zhang, D. & Ren, J. Optimization of process parameters for surface roughness and microhardness in dry milling of magnesium alloy using Taguchi with grey relational analysis. Int. J. Adv. Manuf. Technol. 81, 645–651. https://doi.org/10.1007/s00170-015-7218-8 (2015).
Coppel, R., Abellan-Nebot, J. V., Siller, H. R., Rodriguez, C. A. & Guedea, F. Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches. Int. J. Adv. Manuf. Technol. 84, 2219–2238. https://doi.org/10.1007/s00170-015-7807-6 (2016).
Malghan, R. L., Rao, K. M., Shettigar, A. K., Rao, S. S. & D’souza, R. J. Application of particle swarm optimization and response surface methodology for machining parameters optimization of aluminium matrix composites in milling operation. J. Brazilian Soc. Mech. Sci. Eng. 39 (9), 3541–3553. https://doi.org/10.1007/s40430-016-0675-7 (2017).
Cica, D. & Kramar, D. Multi-objective optimization of high-pressure jet-assisted turning of Inconel 718. Int. J. Adv. Manuf. Technol. 105, 4731–4745. https://doi.org/10.1007/s00170-019-04513-4 (2019).
Sen, B., Mia, M., Mandal, U. K., Dutta, B. & Mondal, S. P. Multi-objective optimization for MQL-assisted end milling operation: an intelligent hybrid strategy combining GEP and NTOPSIS. Neural Comput. Appl. 31, 8693–8717. https://doi.org/10.1007/s00521-019-04450-z (2019).
Yuan, F. et al. Spatial Modification Optimization Methods for Harmonic Drives Using a 3D Non-Uniform Line-Contact Elastohydrodynamic Lubrication Model 111288 (Tribology International, 2025).
Xie, Y., Dong, C., Liu, Z., Yi, Y. & Zhou, Y. The Influence of Rolling Reduction on the Mechanical, Corrosion, Osteogenic, and Antibacterial Properties of Zn–Mg Alloys. ACS omega. 10 (33), 37141–37153 (2025).
Lu, M., Wang, X., Lin, J., Chen, Y. & Du, Y. Modeling and analysis of specific cutting energy for Ti6Al4V alloy using quasi-intermittent vibration assisted swing cutting (Precision Engineering, 2025).
Kumar, R., Choudhury, A. R., Sahoo, A. K., Panda, A. & Malakar, A. Machinability investigation on novel incoloy 330 super alloy using coconut oil based SiO2 nano fluid. Int. J. Integr. Eng. 12 (4), 145–160 (2020).
Sen, B., Hussain, S. A. I., Mia, M., Mandal, U. K. & Mondal, S. P. Selection of an ideal MQL-assisted milling condition: an NSGA-II-coupled TOPSIS approach for improving machinability of Inconel 690. Int. J. Adv. Manuf. Technol. 103, 1811–1829. https://doi.org/10.1007/s00170-019-03620-6 (2019).
Manikanta, J. E., Nikhare, C., Gurajala, N. K., Ambhore, N. & Mohan, R. R. A review on hybrid nanofluids: preparation methods, thermo physical properties and applications. Iran. J. Sci. Technol. Trans. Mech. Eng. 49 (1), 67–79 (2025).
Deb, K. An introduction to genetic algorithms. Sadhana 24, 293–315 (1999).
Sen, B., Mia, M., Mandal, U. K. & Mondal, S. P. Synergistic effect of silica and pure palm oil on the machining performances of Inconel 690: A study for promoting minimum quantity nano doped-green lubricants. J. Clean. Prod. 258, 120755. https://doi.org/10.1016/j.jclepro.2020.120755 (2020).
Manikanta, J. E., Abdullah, M., Ambhore, N. & Kotteda, T. K. Analysis of machining performance in turning with trihybrid nanofluids and minimum quantity lubrication. Sci. Rep. 15 (1), 12194 (2025).
Ambhore, N., Kamble, D. & Chinchanikar, S. Analysis of tool vibration and surface roughness with tool wear progression in hard turning: An experimental and statistical approach. J. Mech. Eng. Sci. 14 (1), 6461–6472 (2020).
Manikanta, J. E., Ambhore, N., Nikhare, C. & Gurajala, N. K. Machining performance of SS 304 steel with hybrid nanocutting fluids using Taguchi-based gray relational analysis. J. Mech. Eng. Sci., 10290–10302. (2024).
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Omar Almomani contributed to the conceptualization, methodology formulation, investigation, data curation, and initial drafting of the manuscript. Vipulsinh Rajput supported the experimental design, formal analysis, and validation of results. (A) C. Umamaheshwer Rao provided supervision, essential resources, and critical manuscript revisions. Sikata Samantaray assisted in data acquisition, characterization studies, and interpretation of findings. Nagaraj Patil contributed to software development, modeling, and statistical analysis. Ripendeep Singh handled visualization, figure preparation, and proofreading. (B) Vishnu Vardhana Naidu supported materials preparation, laboratory activities, and technical review. Mohit Sahani conducted an extensive literature review, editing, and improvement of the scientific content. Abhijit Bhowmik contributed to project administration, quality assurance, and refinement of the manuscript. Lema Abate, as the corresponding author, oversaw the overall supervision of the study, managed funding acquisition, coordinated revisions, and approved the final version of the manuscript.
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Almomani, O., Rajput, V., Rao, A.C.U. et al. Advancing sustainable machining of inconel 718 through nanoparticle-enhanced coconut oil and RSM–GA optimization. Sci Rep (2026). https://doi.org/10.1038/s41598-026-46713-5
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DOI: https://doi.org/10.1038/s41598-026-46713-5