Investigation of friction and wear behaviour of SiC-filled PEEK coating using artificial neural network

PEEK-based composite materials are of great interest for applications such as bearing, slider materials, etc. SiC-filled PEEK coating was prepared using a printing technique. The objective of this study was to evaluate the influence of sliding conditions, in particular, the sliding velocity and applied load on the tribological behaviour of SiC-filled PEEK coating using an artificial neural network (ANN). Test and validation experiments were performed after ANN calculations. It seems that the results obtained by ANN prediction are sufficiently close or, at least related, to the results obtained by friction trials. Sliding conditions for which the applied load is larger than 9 N are found to influence significantly the friction coefficient value. Under lower loads, parabolic relationships of the friction coefficient are predicted with the increase of sliding velocity. A large applied load coupled to intermediate sliding velocity (0.5 m s−1) lowers the wear performance. These results are mainly explained by the influence on morphology of transfer film adhering on the steel counterpart.