Recurrence analysis of coefficient of friction oscillations in DLC-coated and non-coated Ti6Al4V titanium alloy

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.