Wear characterization of zirconium diboride (ZrB 2 ) reinforced AA7178 matrix composites produced by stir casting route

This research work is to analyze the tribological performance of AA7178–ZrB 2 . Metal matrix composite is fabricated by stir casting (SC) method using SN ratio analysis and ANOVA. Scanning electron microscope (SEM), X-ray diffraction (XRD), and energy dispersive X-ray spectroscopy (EDS) analysis proved the zirconium diboride (ZrB 2 ) were homogeneously dispersed in the matrix and the presence of required elements. The experiments were carried out by pin on disc (POD) wear tester. Signal to noise (SN) ratio and analysis of variance (ANOVA) were used to predict the parameters for low wear rate (WR). The WR of the composite was minimized by optimizing the four diverse process factors: load (P), sliding distance (D), sliding velocity (V), and wt% of ZrB 2 based on Taguchi’s L16 orthogonal array. Artificial neural network (ANN) with Lavernberg Marquardt (LM) algorithm was used to validate the wear rate (WR) and coefficient of friction (COF). The main effect plot result showed that the “P” is the most dominating factor affecting the WR. Also, the low “P,” low wt% of ZrB 2 , high “V,” and low “D” are the conditions (A 1 , B 1 , C 4 , D 2 ) to obtain low WR for the composites tested. The ANOVA results showed that the “P” is the dominant one affecting the WR of the produced composites followed by “V,” “D,” and wt% of reinforcement. ANN results show that the predictable data are perfectly acceptable with actual experimental test values. The significant wear parameters are discussed using worn surface of the samples analyzed from SEM.