Dataset Information

Data on the coefficient of friction and its prediction by a machine learning model as a function of time for open-cell AlSi10Mg-Al₂O₃ composites with different porosity tested by pin-on-disk method.

ABSTRACT: This data article presents the experimental data of the wear behavior of four types of open-cell AlSi10Mg materials and open-cell AlSi10Mg-Al₂O₃ composites with different pore sizes under dry sliding conditions tested by pin-on-disk method. The data include the coefficient of friction (COF) as a function of time for each material, as well as the predictions of COF using a machine learning model - Extreme Gradient Boosting. The data were generated to investigate the effect of pore size and reinforcement on the friction and wear properties of open-cell AlSi10Mg-Al₂O₃ composites, which are promising materials for lightweight and wear-resistant applications. The data can also be used to validate theoretical models or numerical simulations of wear mec

SUBMITTER: Kolev M

PROVIDER: S-EPMC10460942 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Dataset Information

Data on the coefficient of friction and its prediction by a machine learning model as a function of time for open-cell AlSi10Mg-Al₂O₃ composites with different porosity tested by pin-on-disk method.

OmicsDI is part of the ELIXIR infrastructure

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Dataset Information

Data on the coefficient of friction and its prediction by a machine learning model as a function of time for open-cell AlSi10Mg-Al2O3 composites with different porosity tested by pin-on-disk method.

OmicsDI is part of the ELIXIR infrastructure

Tweets

Data on the coefficient of friction and its prediction by a machine learning model as a function of time for open-cell AlSi10Mg-Al₂O₃ composites with different porosity tested by pin-on-disk method.