Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Advancing sustainable machining of inconel 718 through nanoparticle-enhanced coconut oil and RSM–GA optimization
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 31 March 2026

Advancing sustainable machining of inconel 718 through nanoparticle-enhanced coconut oil and RSM–GA optimization

  • Omar Almomani1,
  • Vipulsinh Rajput2,
  • A. C. Umamaheshwer Rao3,
  • Sikata Samantaray4,
  • Nagaraj Patil5,
  • Ripendeep Singh6,
  • B. Vishnu Vardhana Naidu7,
  • Mohit Sahani8,
  • Abhijit Bhowmik9,10 &
  • …
  • Lema Abate11 

Scientific Reports , Article number:  (2026) Cite this article

  • 84 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Materials science
  • Nanoscience and technology

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

  1. 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).

  2. 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

  3. 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).

    Google Scholar 

  4. 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).

    Google Scholar 

  5. 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).

  6. 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).

    Google Scholar 

  7. 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).

    Google Scholar 

  8. 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).

  9. 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).

    Google Scholar 

  10. 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).

  11. 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).

    Google Scholar 

  12. 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).

    Google Scholar 

  13. 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).

    Google Scholar 

  14. 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).

    Google Scholar 

  15. 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).

    Google Scholar 

  16. 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).

    Google Scholar 

  17. 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).

    Google Scholar 

  18. 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).

    Google Scholar 

  19. 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).

    Google Scholar 

  20. 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).

    Google Scholar 

  21. 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).

    Google Scholar 

  22. 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).

    Google Scholar 

  23. 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).

    Google Scholar 

  24. 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).

  25. 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).

    Google Scholar 

  26. 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).

    Google Scholar 

  27. 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).

    Google Scholar 

  28. 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).

    Google Scholar 

  29. 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).

    Google Scholar 

  30. 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).

  31. 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).

    Google Scholar 

  32. 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).

  33. 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).

    Google Scholar 

  34. 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).

    Google Scholar 

  35. 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).

    Google Scholar 

  36. Deb, K. An introduction to genetic algorithms. Sadhana 24, 293–315 (1999).

    Google Scholar 

  37. 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).

    Google Scholar 

  38. 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).

    Google Scholar 

  39. 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).

    Google Scholar 

  40. 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).

Download references

Author information

Authors and Affiliations

  1. Department of Networks and Cybersecurity, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan

    Omar Almomani

  2. Department of Mechanical Engineering, Faculty of Engineering, Gokul Global University, Sidhpur, Gujarat, India

    Vipulsinh Rajput

  3. Vardhaman College of Engineering, Hyderabad, Telangana, India

    A. C. Umamaheshwer Rao

  4. Department of Mechanical Engineering, Siksha ’O’ Anusandhan (Deemed to be University), Bhubaneswar, 751030, Odisha, India

    Sikata Samantaray

  5. Department of Mechanical Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India

    Nagaraj Patil

  6. Department of Mechanical Engineering, Chandigarh University, Mohali, Punjab, India

    Ripendeep Singh

  7. Department of Mechanical Engineering, School of Engineering, Mohan Babu University, Tirupati, 517102, Andhra Pradesh, India

    B. Vishnu Vardhana Naidu

  8. Department of Mechanical Engineering, Sharda School of Engineering & Sciences, Sharda University, Greater, Noida, India

    Mohit Sahani

  9. Department of Additive Manufacturing, Mechanical Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Thandalam, 602105, Chennai, India

    Abhijit Bhowmik

  10. Chitkara School of Planning and Architecture, Chitkara University, Rajpura, 140401, Punjab, India

    Abhijit Bhowmik

  11. Department of Statistics, College of Natural and Computational Sciences, Mizan-Tepi University, Mizan-Aman, Ethiopia

    Lema Abate

Authors
  1. Omar Almomani
    View author publications

    Search author on:PubMed Google Scholar

  2. Vipulsinh Rajput
    View author publications

    Search author on:PubMed Google Scholar

  3. A. C. Umamaheshwer Rao
    View author publications

    Search author on:PubMed Google Scholar

  4. Sikata Samantaray
    View author publications

    Search author on:PubMed Google Scholar

  5. Nagaraj Patil
    View author publications

    Search author on:PubMed Google Scholar

  6. Ripendeep Singh
    View author publications

    Search author on:PubMed Google Scholar

  7. B. Vishnu Vardhana Naidu
    View author publications

    Search author on:PubMed Google Scholar

  8. Mohit Sahani
    View author publications

    Search author on:PubMed Google Scholar

  9. Abhijit Bhowmik
    View author publications

    Search author on:PubMed Google Scholar

  10. Lema Abate
    View author publications

    Search author on:PubMed Google Scholar

Contributions

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.

Corresponding author

Correspondence to Lema Abate.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received: 05 January 2026

  • Accepted: 27 March 2026

  • Published: 31 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-46713-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Nickel alloy
  • Nanoparticles
  • Coconut oil
  • Machining responses
  • Genetic algorithm
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com footer links

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing