Abstract
The dry sliding wear behaviour of composites based on ZA-27 alloy reinforced with titanium carbide (TiC) particles is discussed in current research. The incorporation of TiC particles to the ZA-27 alloy aims to enhance its wear resistance and improve its performance in applications subjected to sliding contact. The ZA-27 alloy was fabricated through stir casting technique and composites an in-situ technique, ensuring a homogeneous dispersion of TiC particles. This paper primarily studies the impact of three test factors, namely TiC weight percentage, load, and speed on the wear rate of ZA-27 alloy and its composites. A pin-on-disc tribometer was employed to carry out wear tests under dry sliding testing conditions at different loads and speeds. The results show that ZA-27 + 10 wt.% TiC composite demonstrated superior wear resistance in comparison to the ZA-27 + 5 wt.% TiC composite and ZA-27 alloy. The study uses machine learning and statistical techniques to examine the influence of test parameters on the wear rate. The ANOVA results highlight that the TiC weight percentage significantly influences the wear rate, followed by sliding speed and contact load. The artificial neural network (ANN) model exhibited superior performance (R2 = 94.12%) compared to the design based on RSM. The results clearly indicate that the ANN outperforms RSM in accurately estimating outcomes. The current research provides novel insights for developing TiC-reinforced composites of ZA-27 alloy to enhance their dry sliding wear resistance, which is a relatively unexplored area in the scientific literature of bearings. Furthermore, optimizing the wear resistance of ZA-27 alloy is essential for improving component longevity, reducing costs, enhancing performance, minimizing downtime, and promoting sustainability.
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