Though numerous analytical techniques are used to assess performance of surface-modified metals, identification of dominant wear modes, as well as transitions between them, is challenging. Nevertheless, the works devoted to analysis the COF oscillations are still scarce. In this paper, Recurrence Quantification Analysis, Principal Component Analysis and Self-Organizing Map neural network are proposed as tools for analysis of COF time series acquired during tribological tests of the non-coated and DLC-coated Ti6Al4V. The proposed approach can be used to recognize the dominant wear modes present during friction and the transitions between them, as well as to spot the frictional damage of the protective film.

尽管使用多种分析技术来评估表面改性金属的性能，但识别主要磨损模式以及它们之间的过渡具有挑战性。然而，致力于分析 COF 振荡的工作仍然很少。在本文中，提出了递归量化分析、主成分分析和自组织映射神经网络作为分析非涂层和 DLC 涂层 Ti6Al4V 摩擦学测试期间获得的 COF 时间序列的工具。所提出的方法可用于识别摩擦期间存在的主要磨损模式及其之间的过渡，以及发现保护膜的摩擦损坏。