Open-Cell AlSn6Cu-SiC Composites: Fabrication, Dry-Sliding Wear Behavior, and Machine Learning Methods for Wear Prediction.

Open-cell AMMCs are high-strength and lightweight materials with applications in different types of industries. However, one of the main goals in using these materials is to enhance their tribological behavior, which improves their durability and performance under frictional conditions. This study presents an approach for fabricating and predicting the wear behavior of open-cell AlSn6Cu-SiC composites, which are a type of porous AMMCs with improved tribological properties. The composites were fabricated using liquid-state processing, and their tribological properties are investigated by the pin-on-disk method under different loads (50 N and 100 N) and with dry-sliding friction. The microstructure and phase composition of the composites were investigated by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction. The mass wear and coefficient of friction (COF) of the materials were measured as quantitative indicators of their tribological behavior. The results showed that the open-cell AlSn6Cu-SiC composite had an enhanced tribological behavior compared to the open-cell AlSn6Cu material in terms of mass wear (38% decrease at 50 N and 31% decrease at 100 N) while maintaining the COF at the same level. The COF of the composites was predicted by six different machine learning methods based on the experimental data. The performance of these models was evaluated by various metrics (R2, MSE, RMSE, and MAE) on the validation and test sets. Based on the results, the open-cell AlSn6Cu-SiC composite outperformed the open-cell AlSn6Cu material in terms of mass loss under different loads with similar COF values. The ML models that were used can predict the COF accurately and reliably based on features, but they are affected by data quality and quantity, overfitting or underfitting, and load change.

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.

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 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-Al2O3 Composites", where the details of the experimental methods, the microstructural characterization, and the analysis of the wear mechanisms are provided [1].

Data on mechanical properties of open-cell AlSi10Mg materials and open-cell AlSi10Mg-SiC composites with different pore sizes and strain rates.

This data article describes the stress-strain curves, energy absorption and energy absorption efficiency of open-cell AlSi10Mg materials and open-cell AlSi10Mg-SiC composites with different pore sizes and strain rates. The data were obtained by quasi-static compression loading up to 60% strain at strain rates of 0.01 and 0.001 s-1 according to ISO 13,314:2011 standard. The data can be used to compare the effects of pore size and strain rate on the compressive properties of the materials. The data are related to the research article entitled "Fabrication, Experimental Investigation and Prediction of Wear Behavior of Open-Cell AlSi10Mg-SiC Composite Materials" (Kolev, M., Drenchev, L., & Petkov, V. (2023). Fabrication, Experimental Investigation and Prediction of Wear Behavior of Open-Cell AlSi10Mg-SiC Composite Materials. Metals, 13(4), 814. MDPI AG. Retrieved from http://dx.doi.org/10.3390/met13040814).