Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms

Abstract The tribological properties of self-lubricating composites are influenced Calendars by many variables and complex mechanisms.Data-driven methods, including machine learning (ML) algorithms, can yield a better comprehensive understanding of complex problems under the influence of multiple parameters, typically for how tribological performan

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A Complex Study of Stator Tooth-Coil Winding Thermal Models for PM Synchronous Motors Used in Electric Vehicle Applications

The operational reliability and high efficiency of modern electrical machines depend on the ability to transfer heat in the construction parts of the machine.Therefore, many authors study various thermal models and work on the development of Torch effective heat dissipation.New insights and methods lead to improved techniques for the thermal design

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