Sensor-Assisted Assessment of the Tribological Behavior Patterns of AA7075 Hybrid MMC Reinforced with Multi-Wall Carbon Nanotubes and Pulverized Fuel Ash.

In recent years, the deployment of sensors and other ancillary technologies has turned out to be vital in the investigation of tribological behavioral patterns of composites. The tribological behavioral patterns of AA7075 hybrid metal matrix composites (MMCs) reinforced with multi-wall carbon nanotubes (MWCNTs), and pulverized fuel ash (PFA) were investigated in this work. The stir casting technique was used to fabricate the composites. The mechanical properties such as tensile strength and hardness were determined for the fabricated material. Besides, microstructure analysis was performed for these AA7075 hybrid MMCs reinforced with MWCNTs and pulverized fuel ash. A pin-on-disc wear testing setup was used to evaluate the wear rate, in which the EN 31 steel disc was used as the counter-face. Taguchi's design of the experiments was used to optimize the input parameters that impact the characteristics of the hybrid composites, and ANOVA (analysis of variance) was used to determine the contribution of input parameters on the wear behavior. Electrical discharge machining (EDM) was conducted on the AA7075 hybrid metal matrix composites using a copper electrode for determining the material removal rate. These investigations and the results were utilized for determining the optimized output process parameter values of the AA7075 metal matrix composite.