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

This data article presents the experimental data of the wear behavior of four types of open-cell AlSi10Mg materials and open-cell AlSi10Mg-AlO 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-AlO 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 mechanisms in porous materials, as well as to optimize the material design and processing parameters to enhance the wear resistance of open-cell AlSi10Mg materials. The data are available in DWF and XLSX format and can be opened by any text editor or spreadsheet software. The data article is related to an original research article entitled "Production and Tribological Characterization of Advanced Open-Cell AlSi10Mg-AlO Composites", where the details of the experimental methods, the microstructural characterization, and the analysis of the wear mechanisms are provided [1].