Optimisation of tribological performance of sputtering Zr/ZrC coatings

Abstract

An intelligent approach, which uses adaptive network based fuzzy inference systems (ANFIS) based on experimental designs, is used to characterise the tribological behaviour of undoped and Zr doped diamond-like carbon (DLC) films that are deposited using magnetron sputtering. An orthogonal array experiment is used and the effect of the deposition parameters on the films is determined. The films are analysed using X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM). Friction and wear tests are performed, using a pin-on-disk tribometer. This study identifies a group of highly developed hillock-like textures and lower wear volume loss is evident in the undoped and Zr doped films. The C1s core level XPS spectra show that the undoped and Zr doped films formed have a relative content of sp3 and sp2 hybrids. It is found that a value that is close to the estimated value, 0·5±0·05, of sp3/sp2 ratio results in better tribological properties in the undoped and Zr doped DLC films. These predicted values and the experimental results, for which an ANFIS predicts the tribological behaviour of the DLC films, are similar. The experimental results demonstrate that the tribological properties of DLC multilayer films are accurately predicted by an ANFIS. The results obtained for the ANFIS model are also compared to those for an ANN model and a Fuzzy system and it is shown that an ANFIS gives more reliable modelling of these sputtering processes and is more accurate and flexible than ANN and Fuzzy system models, which verifies the reliability and feasibility of this approach.