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                <text>Faculty Publications</text>
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          <name>Creator</name>
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              <text>Ravitej, Y.P.; Keshavamurthy, R.; Kumar, Rahul; Kshaurad, Krantikumar; Halemani, Balachandra; Ramakumar, B.V.N.; Sripad Kulkarni, S.; Shankaranarayana, Ramesh</text>
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              <text>Wear rate prediction of hybrid composites: A comparative study using experimental analysis, finite element simulation and machine learning prediction</text>
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              <text>01-01-2025</text>
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              <text>Jurnal Tribologi;Volume;47;pp.1-23</text>
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              <text>&lt;a href="https://www.scopus.com/pages/publications/105028115300?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105028115300?origin=resultslist&lt;/a&gt;</text>
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              <text>Ravitej Y.P., Department of Mechanical Engineering, Dayananda Sagar University, India; Keshavamurthy R., Department of Mechanical and Automobile Engineering, CHRIST University, Karnataka, Bengaluru, 560074, India; Kumar R., Department of Mechanical Engineering, Dayananda Sagar University, India; Kshaurad K., Department of Mechanical Engineering, Dayananda Sagar College of Engineering, India; Halemani B., Department of Mechanical Engineering, KLE Technological University, India; Ramakumar B.V.N., Department of Aerospace Engineering, Dayananda Sagar University, India; Sripad Kulkarni S., Department of Aerospace Engineering, Dayananda Sagar University, India; Shankaranarayana R., College of Engineering and technology, University of technology and applied sciences-Nizwa, Oman</text>
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              <text>This study evaluates wear rate predictions by comparing experimental results with Finite Element Analysis (FEA) simulations, focusing on the influence of track radius. Regression models were developed to analyze the trends, while residual analysis identified deviations between the two approaches. A correlation matrix highlighted significant relationships between wear rate, pressure, and sliding distance. Confidence interval analysis confirmed the reliability of both models, though polynomial regression provided a more accurate representation of wear rate trends than linear models. While FEA predictions are closely aligned with experimental data, some discrepancies suggest the need for further refinement in computational modeling to improve accuracy. Future work will focus on enhancing FEA simulations and expanding experimental validation for better predictive reliability.  2025, Malaysian Tribology Society (Mytribos). All rights reserved.</text>
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              <text>Correlation matrix; Finite element analysis; Polynomial regression; Residual analysis</text>
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          <name>Publisher</name>
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              <text>Malaysian Tribology Society (Mytribos)</text>
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              <text>ISSN: 22897232;</text>
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              <text>Restricted Access; Hardcopy may be available in the library</text>
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