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Performance Prediction of Aluminum Oxide, Silicon Oxide, and Copper Oxide as Nanoadditives Across Conventional, Semisynthetic, and Synthetic Lubricating Oils Using ANN

  • Research Article-Mechanical Engineering
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Abstract

In the realm of lubrication, nanoparticles play a pivotal role in enhancing the tribological efficacy of lubricating oils. Unveiling a critical need, the research underscores the necessity for a predictive model capable of anticipating these performance characteristics. This research endeavors to fill this gap by introducing an artificial neural network (ANN) tailored specifically for predicting the behavior of nanolubricants. The optimized neural network structure, at 5 × 8 × 2, attains a remarkable minimum mean square error of 0.00046667, with R-values hovering at impressive proximity to unity (0.99828). During the confirmation phase, the neural network's predictions demonstrate a deviation of 7.51% (negative) and 2.87% (negative) for COF, alongside 0.50% and 1.80% for WSD, further affirming its predictive capacity in assessing lubricant performance characteristics.

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Acknowledgements

The author is grateful to Maulana Azad National Institute of Technology Bhopal (MANIT) where this research has been conducted. The authors would like to thank Dr. Bharat Kumar Modhera (Chemical Engineering) and Dr. Sudhanshu Kumar (Mechanical Engineering) for allowing them to use their laboratories for research.

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Authors and Affiliations

  1. Bansal Institute of Science and Technology Bhopal, Bhopal, India

    Anoop Pratap Singh

  2. Maulana Azad National Institute of Technology Bhopal, Bhopal, India

    Ravi Kumar Dwivedi & Amit Suhane

  3. National Institute of Technology Raipur, Raipur, India

    Prem Kumar Chaurasiya

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  1. Anoop Pratap Singh
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Correspondence to Anoop Pratap Singh.

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Singh, A.P., Dwivedi, R.K., Suhane, A. et al. Performance Prediction of Aluminum Oxide, Silicon Oxide, and Copper Oxide as Nanoadditives Across Conventional, Semisynthetic, and Synthetic Lubricating Oils Using ANN. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-09078-3

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