Kolev Mihail, Drenchev Ludmil, Petkov Veselin, Dimitrova Rositza, Kovacheva Daniela
Institute of Metal Science, Equipment and Technologies with Center for Hydro- and Aerodynamics "Acad. A. Balevski", Bulgarian Academy of Sciences, Boulevard Shipchenski Prohod 67, 1574 Sofia, Bulgaria.
Institute of General and Inorganic Chemistry, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria.
Materials (Basel). 2023 Sep 14;16(18):6208. doi: 10.3390/ma16186208.
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.
开孔泡沫铝基金属复合材料是一种高强度、轻质的材料，在不同类型的工业中都有应用。然而，使用这些材料的主要目标之一是提高其摩擦学性能，从而在摩擦条件下提高其耐久性和性能。本研究提出了一种制造和预测开孔AlSn6Cu-SiC复合材料磨损行为的方法，该复合材料是一种具有改善摩擦学性能的多孔泡沫铝基金属复合材料。采用液态加工方法制备了复合材料，并通过销盘法在不同载荷（50 N和100 N）和干滑动摩擦条件下研究了其摩擦学性能。通过扫描电子显微镜、能量色散X射线光谱和X射线衍射研究了复合材料的微观结构和相组成。测量了材料的质量磨损和摩擦系数（COF），作为其摩擦学行为的定量指标。结果表明，与开孔AlSn6Cu材料相比，开孔AlSn6Cu-SiC复合材料在质量磨损方面具有更好的摩擦学性能（在50 N时减少38%，在100 N时减少31%），同时保持COF在相同水平。基于实验数据，用六种不同的机器学习方法预测了复合材料的COF。通过各种指标（R2、MSE、RMSE和MAE）在验证集和测试集上评估了这些模型的性能。结果表明，在COF值相似的情况下，开孔AlSn6Cu-SiC复合材料在不同载荷下的质量损失方面优于开孔AlSn6Cu材料。所使用的机器学习模型可以根据特征准确可靠地预测COF，但它们受到数据质量和数量、过拟合或欠拟合以及载荷变化的影响。
