Microstructures and Mechanical Properties of Al 6061 /Al2O3-TiB2 Hybrid Nano-Composite layer Produced via Friction Stir Processing Using Optimized Process Parameters

Aluminum and its alloys have been used effectively in the aerospace and automotive industries because of their useful properties, such as high strength-to-weight ratio, low density, high thermal conductivity, and corrosion resistance. However, low outer part features, such as hardness and wear resistance, are some of the disadvantages for usage in those industries. Aaluminum matrix composites (AMCs) been manufactured by incorporating ceramic particles as reinforcement in the metal matrix has been used to enhance surface characteristics. In addition, Metal-matrix composite materials are finding a variety of applications in sectors of engineering fields due to their crucial properties. Hybrid composite materials are advanced composite materials reinforced with more than one element so as to produce a uniquely combined effect. This permits a more high degree of flexibility in the design of the material. The necessity for light-weight and high-performance materials increases by the day due to an increase in its usage professional fields such as automotive, aerospace, deep-ocean, nuclear-energy-generation, structural applications, etc., that has consequently brought about the invention of hybrid materials in terms of composites. This thesis concentrates on the fabrication of Al6061/ Al2O3-Tib2 hybrid metal matrix composite using friction stir processing. Al6061 and Al2O3-Tib2 nano powder were utilized as the fundamental material and reinforcement particles, respectively. The influence of a number of FSP passes and tool pin profiles were studied on the distribution of Al2O3-Tib2 particles in aluminum matrix, microstructure, hardness, and wear properties of specimens. Also relation between process parameters iv (rotational speed, feed rate, number of passes) and hardness behavior of the composite layer was studied using mathematical models such as artificial neural network (ANN) and response surface methodology (RSM). Friction stir processing was conducted using different tool pin profiles, different rotation and traverse speeds, and a number of passes. Microstructural characterization done using optical microscopy, (SEM) and (TEM). Wear resistance analysis and hardness (H) were obtained. It was presented that fine grains formed in the stir zone due to the dynamic re crystallization. It was confirmed that refinement of these particles can increase the effective pining of the grain boundaries and reduce grain growth. The outcomes showed that increase in the number of passes led to a more uniform dispersion of composite particles thereby decreasing the particles clustering. Additionally, an increase in the number of FSP passes was found to reduce the matrix grain size (minimum grain size 0.7 µm) of the outer surface hybrid composite. With an increasing number of FSP passes, the hardness of the composite layer increases significantly as result of the pinning effect and the presence of hard Al2O3- Tib2 particles. The peak hardness for the composite layer was 175 HV while that the hardness of received AL6061 was 110 HV. Also, at higher number of passes, the outer surface hybrid composite wear rate increased. In addition, the distribution of Al2O3-Tib2 particles in the specimens produced using square and triangular tool pin profiles was more aligned due to their tool geometry, which resulted in a better stirring of the material and good material flow. A greater reduction of particle clustering was noted consequently, and thus the mechanical properties were improved. Moreover, the samples made utilizing square and triangular pin profiles showcased more grain refinement (minimum grain size 1.1 µm) than the other samples. More uniform structure, less clustering, and finer grains produced by square and triangular pin profiles caused a higher hardness (maximum hardness 160 HV) and wear resistance. The artificial neural networks and response surface methodology have been effectively utilized to predict the hardness behavior of the friction stir processed Al6061/Al2O3-Tib2 nano composite. ANN was found a better tool to model the hardness performance of the FSPed composite layer. The trained ANN proved acceptable results when compared with the experimental results. Similarly, Response surface methodology could be employed to model the hardness of the processed composite layer. The error of both model was less than 1.5% which was satisfactory. Keywords: Friction stir processing, hybrid composite layer, Al2O3-Tib2, number of passes, tool pin profile, rotational speed and feed rat