MACHINE LEARNING-GUIDED DESIGN OF NANOLUBRICANTS FOR MINIMIZING ENERGY LOSS IN MECHANICAL SYSTEMS

Abstract

This study explores the significant potential of machine learning-guided design in optimizing nanolubricants, focusing on their application in reducing friction and wear in mechanical systems. Utilizing neural networks and genetic algorithms, the research demonstrates how advanced computational techniques can accurately predict and enhance the tribological properties of nanolubricants. The findings reveal that nanolubricants, particularly those containing graphene and carbon nanotubes, exhibit marked improvements in reducing friction coefficients and wear rates compared to traditional mineral oil-based lubricants. Additionally, the enhanced thermal stability and load-carrying capacity of these nanolubricants contribute to substantial energy savings and increased operational efficiency. The study underscores the economic and environmental benefits of adopting nanolubricants, highlighting their potential to transform lubrication technology and support sustainable industrial practices.