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  Scopus ID: 21100926589

Enhancing Tribological Performance of Aluminum Matrix Composites through Graphene Reinforcement: Insights from Machine Learning Regression Analysis

Swathi Harohalli Ramachari, K. Vimala Devi, Sathiya Priya Selvaraj, Vinay Hiralal Singh, Sumanth Ratna Kandavalli, Revathi Rallabandi, Mayakannan Selvaraju

Abstract

This research delves into the influence of graphene on friction and wear resistance in self-lubricating metal matrix composites (MMCs) based on aluminum. Experimental results from laboratory testing clearly demonstrate that the incorporation of graphene leads to a substantial improvement in the composites’ resistance to both coefficient of friction (COF) and wear rate (WR). The study specifically investigates the friction and wear characteristics of aluminum matrix composites reinforced with graphene. To predict abrasion and friction rates accurately, the research utilizes five different machine learning regression models, shedding light on the potential of these materials for practical applications where enhanced wear resistance is essential. The findings from this research hold promising implications for industries and manufacturing processes, as graphene’s incorporation into these MMCs offers the potential for improved the coefficient of friction and wear rate performance. Machine learning showed that the wear and friction behaviors of aluminum-graphene composites were significantly influenced by the percentage of graphene in the composite, the specific loading conditions, and the material hardness. Graphene has been highlighted as a promising component for improving the tribological characteristics of MMCs, which might lead to major advances in addressing wear and friction difficulties. Improved engineering materials may be created thanks to the insights gained from the machine learning models, which shed light on the complicated relationship between material composition and tribological performance.

Published on: November 03, 2023
doi: 10.17756/nwj.2023-s3-175
Citation: Ramachari SH, Devi KV, Selvaraj SP, Singh VH, Kandavalli SR. 2023. Enhancing Tribological Performance of Aluminum Matrix Composites through Graphene Reinforcement: Insights from Machine Learning Regression Analysis. NanoWorld J 9(S3): S992-S998.

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