Kolev Mihail, Drenchev Ludmil
Institute of Metal Science, Equipment and Technologies with Center for Hydro- and Aerodynamics "Acad. A. Balevski", Bulgarian Academy of Sciences, 1574 Sofia, Bulgaria.
Data Brief. 2023 Aug 10;50:109489. doi: 10.1016/j.dib.2023.109489. eCollection 2023 Oct.
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].
本文献展示了通过销盘法在干滑动条件下测试的四种不同孔径的开孔AlSi10Mg材料和开孔AlSi10Mg-AlO复合材料磨损行为的实验数据。数据包括每种材料的摩擦系数（COF）随时间的变化情况，以及使用机器学习模型——极端梯度提升法对COF的预测结果。生成这些数据是为了研究孔径和增强体对开孔AlSi10Mg-AlO复合材料摩擦磨损性能的影响，这些复合材料是用于轻量化和耐磨应用的有前景的材料。这些数据还可用于验证多孔材料磨损机制的理论模型或数值模拟，以及优化材料设计和加工参数以提高开孔AlSi10Mg材料的耐磨性。数据以DWF和XLSX格式提供，可由任何文本编辑器或电子表格软件打开。本文献与一篇题为《先进开孔AlSi10Mg-AlO复合材料的制备及摩擦学表征》的原创研究论文相关，该论文提供了实验方法、微观结构表征以及磨损机制分析的详细信息[1]。
