Evidence map›Paper›PMID 41917162›Full record

ArticleScientific reports2026

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

Omar Almomani, Vipulsinh Rajput, A C Umamaheshwer Rao, Sikata Samantaray, Nagaraj Patil, Ripendeep Singh, B Vishnu Vardhana Naidu, Mohit Sahani, Abhijit Bhowmik, Lema Abate

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Omar AlmomaniDepartment of Networks and Cybersecurity, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan.
Vipulsinh RajputDepartment of Mechanical Engineering, Faculty of Engineering, Gokul Global University, Sidhpur, Gujarat, India.
A C Umamaheshwer RaoVardhaman College of Engineering, Hyderabad, Telangana, India.
Sikata SamantarayDepartment of Mechanical Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, 751030, Odisha, India.
Nagaraj PatilDepartment of Mechanical Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India.
Ripendeep SinghDepartment of Mechanical Engineering, Chandigarh University, Mohali, Punjab, India.
B Vishnu Vardhana NaiduDepartment of Mechanical Engineering, School of Engineering, Mohan Babu University, Tirupati, 517102, Andhra Pradesh, India.
Mohit SahaniDepartment of Mechanical Engineering, Sharda School of Engineering & Sciences, Sharda University, Greater, Noida, India.
Abhijit BhowmikDepartment of Additive Manufacturing, Mechanical Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Thandalam, 602105, Chennai, India.
Lema AbateDepartment of Statistics, College of Natural and Computational Sciences, Mizan-Tepi University, Mizan-Aman, Ethiopia. lema@mtu.edu.et.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Coconut oilGenetic algorithmMachining responsesNanoparticlesNickel alloy

Identifiers

PMID41917162
PMCPMC13181016

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