Triboinformatic analysis and prediction of B4C and granite powder filled Al 6082 composites using machine learning regression models
Abstract The traditional methods for fabricating and evaluating wear properties are inherently time-consuming and financially demanding. To address these challenges, machine learning (ML) has emerged as a potent approach in predicting the mechanical and tribological behavior of advanced materials, i...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-12603-5 |
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