A Machine Learning Framework for Predicting Friction and Wear Behavior of Nano-Lubricants in High-Temperature

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@article{1704861,
      author = {Kollol Sarker Jogesh},
      title = {A Machine Learning Framework for Predicting Friction and Wear Behavior of Nano-Lubricants in High-Temperature},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {1},
      pages = {591-599},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704861.pdf},
      abstract = {This study introduces a comprehensive machine learning framework tailored for predicting the friction and wear characteristics of Nano-lubricant operating under high-temperature conditions. Nano-lubricants, infused with nanoparticles like metal oxides or carbon-based materials, offer substantial enhancements in thermal stability and tri-biological performance compared to conventional lubricants. However, accurately predicting their complex friction and wear behaviors remains a significant challenge due to the intricate interactions between nanoparticles and lubricant matrices.
The results demonstrate substantial improvements in predictive accuracy compared to conventional methods, underscoring the efficacy of machine learning in optimizing Nano-lubricant formulations for high-temperature applications. Detailed comparative analyses and sensitivity studies highlight the critical factors influencing tri-biological performance, providing valuable insights for future research and industrial applications.
This paper discusses the methodology employed to develop and validate the machine learning models, presents detailed results showcasing the models' performance metrics, and explores the broader implications for advancing materials science and engineering. The findings suggest that the proposed machine learning framework not only enhances predictive capabilities but also accelerates innovation in lubricant technology, paving the way for more efficient and durable lubrication systems across various industrial sectors.},
      keywords = {Nano-lubricants, Machine Learning, High-Temperature Applications, Predictive Modeling, Engineering Applications},
      month = {July},
  }