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

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 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,...

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Bibliographic Details
Main Authors: Huifeng Ning, Faqiang Chen, Yunfeng Su, Hongbin Li, Hengzhong Fan, Junjie Song, Yongsheng Zhang, Litian Hu
Format: Article
Language:English
Published: SpringerOpen 2024-04-01
Series:Friction
Subjects:
self-lubricating composites
machine learning (ML)
tribological properties
prediction
Online Access:https://doi.org/10.1007/s40544-023-0847-2

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