Optimization of Wear Behavior of DLC Coatings Through Optimization of Deposition Conditions

Abstract

Based on genetic algorithm (GA) and fuzzy neural network, a new method for the study of sputtering process is proposed in this paper. Diamond-like carbon (DLC) coatings were deposited on SKD11 steel by magnetron sputtering. An orthogonal array design is implemented and the effects of control factors on surface properties of the coatings were systematically analyzed. The coating properties were investigated by scanning electron microscopy and Raman spectroscopy, and wear volume surface performance of the Zr-doped DLC coatings was evaluated by a wear tests pin-on-disk tribometer. The Raman analyses showed that, at lower ID/IG ratio, a lower wear volume of the Zr-doped DLC coatings can be obtained. Scratch tests as well as Rockwell indentation tests revealed that the graded Zr-doped DLC structures efficiently provide better adhesive strength of DLC coatings. The results show that the wear behaviors of the DLC coatings can be improved by Zr-doping, which the Zr-doped DLC coatings exhibited promising tribological properties. Also, the predictive ability of the GA-ANFIS computations for the tribological behaviors of the Zr-DLC coatings within the experimental domains proved to be reliably obtained, where the forecasted values and experimental results are close.