Optimization of Dry Sliding Wear Parameters of Aluminium Reinforced Hybrid Composite Materials with Silicon Carbide and Graphene Using Taguchi and Regression Analysis

Aluminum hybrid composites are widely used in various engineering applications due to their excellent balance of mechanical strength, low weight, and enhanced wear resistance. These materials are especially valued in areas where components are subjected to continuous friction and require long-lasting performance. This project aims to evaluate and improve the wear behavior of such composites under dry sliding conditions using systematic testing and analysis methods. This study focuses on the optimization of dry sliding wear behavior of aluminum-based hybrid composite materials reinforced with silicon carbide (SiC) and graphene. These two particles are added to improve the strength, hardness, and wear resistance of the material. To study the effect of different factors like load, sliding speed, and distance on the wear rate, experiments were planned using the Taguchi method, which helps in reducing the number of trials while still giving reliable results. After conducting the tests, regression analysis was used to develop a mathematical model and understand how each factor affects the wear performance. The results showed that the addition of SiC and graphene improves the wear resistance of the aluminum matrix significantly. This study also helps in identifying the best combination of parameters to reduce material loss due to wear. The findings can be useful for applications like automotive and mechanical parts, where materials are exposed to continuous friction and need to last longer. The conclusions drawn from this work highlight the effectiveness of combining reinforcements and optimizing parameters to significantly reduce wear. The study successfully demonstrates a practical approach to improving the performance of aluminum hybrid composites under dry sliding conditions.