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,...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-04-01
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| Series: | Friction |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40544-023-0847-2 |