An Innovative Approach to Find the Optimum Lubricant for Diverse Applications Based on Scikit-Learn Library Using Python

Year : 2025 | Volume : 13 | Special Issue 03 | Page : 25-35
    By

    D. V. A. Rama Sastry,

  • Shaikh Azharuddin Kutubuddin,

  1. Associate Professor, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
  2. Research Scholar, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

Abstract

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This paper presents an innovative approach for finding the optimum lubricant using the Scikit-learn library in Python. The proposed approach uses a linear regression model to analyze a dataset of lubricant properties and performance, specifically the viscosity, wear, and friction. The model is trained on the dataset to predict the wear and friction for a given viscosity, which can be used to identify the optimum lubricant. By analyzing a dataset of lubricant properties and performance, the present study uses a linear regression model to predict the wear and friction for a given viscosity. The results of this approach show how data-driven techniques can be used for selecting optimal semi-solid and solid lubricants pertaining to multiple industries such as polymers, composites, manufacturing, and automotive sectors.  It promises in reducing wear and friction in machinery, ultimately increasing their performance and lifespan. The use of Python and Scikit-learn makes this approach easily replicable and adaptable to other datasets and applications. This paper explores the use of machine learning techniques for optimizing semi-solid and solid lubricants, emphasizing diverse applications, including machinery and polymer-based systems. The study highlights how data-driven methods predict wear and friction based on viscosity, providing insights applicable to multiple industries such as composites, manufacturing, and automotive sectors. Overall, this paper demonstrates the potential of innovative data-driven methods in the field of lubricant optimization.

Keywords: Optimization, scikit-learn, python, lubricants, data driven method.

[This article belongs to Special Issue under section in Journal of Polymer and Composites (jopc)]

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How to cite this article:
D. V. A. Rama Sastry, Shaikh Azharuddin Kutubuddin. An Innovative Approach to Find the Optimum Lubricant for Diverse Applications Based on Scikit-Learn Library Using Python. Journal of Polymer and Composites. 2025; 13(03):25-35.
How to cite this URL:
D. V. A. Rama Sastry, Shaikh Azharuddin Kutubuddin. An Innovative Approach to Find the Optimum Lubricant for Diverse Applications Based on Scikit-Learn Library Using Python. Journal of Polymer and Composites. 2025; 13(03):25-35. Available from: https://journals.stmjournals.com/jopc/article=2025/view=0



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Special Issue Subscription Original Research
Volume 13
Special Issue 03
Received 01/02/2025
Accepted 04/03/2025
Published 26/03/2025
Publication Time 53 Days
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