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Tribological performance of nanographite-based metalworking fluid and parametric investigation using artificial neural network

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Abstract

This paper investigates the impact of palm oil methyl ester (POME) and graphite nanoflakes as additives on the thermophysical and tribological properties of naphthenic and groundnut oil blends. Different ratios of the two oil blends containing the selected additives were formulated and tested. The results show that both POME and graphite nanoflakes could be potential friction modifiers. Notably, the four-ball tribometer test reveals that the addition of 7 vol% of POME to naphthenic oil and 0.01 wt% graphite nanoflakes to naphthenic oil results in the reduction of wear scar diameter by 24% and 59%, respectively. Surface micrographs of aluminum workpiece subjected to simple turning operation in a CNC machine corroborated the wear test results. POME in naphthenic oil resulted in better surface finish relative to POME-formulated groundnut oil. The experimental data were analyzed using artificial neural network (ANN) to gain a better understanding of the entire work. The sensitivity analysis developed from the chosen ANN models exhibited the nature of influence of the input parameters (groundnut concentrations, POME, and naphthenic oil) on the outputs (Noack volatility, thermal conductivity, and wear).

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Acknowledgments

We acknowledge the ExcelVite Sdn. Bhd. and the Nynas Pte. Ltd. for supplying the POME and naphthenic base oils, respectively.

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Authors and Affiliations

  1. Sustainable Energy and Green Technology Research Group, Faculty of Innovation and Technology, School of Engineering, Taylor’s University Lakeside Campus, Subang Jaya, Malaysia

    W Rashmi & M Osama

  2. Graphene and Advanced 2D Materials Research Group (GAMRG), School of Science and Technology, Sunway University, No. 5, Jalan Universiti, Sunway City, 47500, Subang Jaya, Selangor, Malaysia

    M Khalid & AK Rasheed

  3. Tribology and Surface Interaction Research Laboratory, Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India

    S Bhaumik & S Datta

  4. Fuel Cell Institute, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    W. Y Wong

  5. Research and Development, Apar Industries Limited, Mumbai, India

    Gupta TCSM

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  1. W Rashmi
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Correspondence to W Rashmi.

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Rashmi, W., Osama, M., Khalid, M. et al. Tribological performance of nanographite-based metalworking fluid and parametric investigation using artificial neural network. Int J Adv Manuf Technol 104, 359–374 (2019). https://doi.org/10.1007/s00170-019-03701-6

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  • DOI: https://doi.org/10.1007/s00170-019-03701-6

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