Researchers have long been studying the effects of the modification of friction material compositions on their tribological properties. Predictive models have also been developed, but they are of limited use in the design of new compositions. Therefore, this research aims to investigate the tribological behaviour of single ingredients in friction materials to develop a tribological dataset. This dataset could then be used as a foundation for a cellular automaton (CA) predictive model, intended to be a tool for designing friction materials. Tribological samples were almost entirely composed of four distinct friction material ingredients, and one sample composed of their mixture was successfully produced. Pin-on-disc (PoD) tribometer testing and scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDXS) analysis were used for the tribological characterization. Each material showed distinct tribological properties and evolution of the contact surface features, and the synergistic effect of their mutual interaction was also demonstrated by their mixture.

研究人员长期以来一直在研究摩擦材料成分的改性对其摩擦学性能的影响。预测模型也已被开发出来，但它们在新组合物的设计中的用途有限。因此，本研究旨在研究摩擦材料中单一成分的摩擦学行为，以开发摩擦学数据集。然后，该数据集可用作元胞自动机 (CA) 预测模型的基础，旨在成为设计摩擦材料的工具。摩擦学样品几乎完全由四种不同的摩擦材料成分组成，并成功生产了一种由其混合物组成的样品。销盘 (PoD) 摩擦计测试和扫描电子显微镜/能量色散 X 射线光谱 (SEM/EDXS) 分析用于摩擦学表征。每种材料都表现出独特的摩擦学特性和接触表面特征的演变，并且它们的混合物也证明了它们相互作用的协同效应。