Optimization and analysis of dry sliding wear behaviour of N-B4C/MOS2 unreinforced AA2219 nano hybrid composites using response surface methodology

The effect of heat treatment on nano-size B4C particle reinforced hybrid composites is discussed in this paper. For this, hybrid reinforced AA2219 composites with 2% by weight nano B4C and 2% by weight MoS2 particulates were fabricated using a two-stage stir casting process, and the specimens were heat treated to assess their influence on wear behavior. Experiments were carried out to study the wear behavior by varying important factors such as aging temperature, load, and sliding distance. Response Surface Methodology (RSM) designed by Box-Behnken was used to identify the critical variables influencing wear rate and optimize wear behavior. To comprehend the wear mechanisms involved, an analysis of the worn surface was presented. Based on the analysis, a regression equation with a predictability of 97.2% was developed for the response to obtain the optimum wear rate. The following order effectively captures the relative importance of the various factors determining the alloy's wear resistance: sliding distance, load, and aging temperature. When compared to load and sliding distance, heat treatments via artificial aging in the temperature range of 200-240 °C have no significant effect on the wear resistance of hybrid AA2219 composites reinforced with n-B4C and MoS2 particulates. However, when a temperature range of 200-240 °C is considered, composites exhibit better wear resistance at the aging temperature of 240 °C with ice quenching.