Material characterization and optimization of CNT-reinforced aluminum (AA7075) functionally graded material processed by ultrasonic cavitation

Abstract

Abstract Conventional materials that have to function in rigorous conditions can exhibit extraordinary properties in tough conditions just from reinforcement with carbon nanotubes (CNTs). However, the manufacturing methods available for CNTs are complex and cumbersome, in terms of the process parameters. In our research work, we have intensively concentrated on the synthesis of functionally-graded material reinforced with long carbon fiber nanotubes for cylinder linings in a four-wheeler. The matrix constituent under consideration is AA7075 and the reinforcing constituents are titanium nitride (TiN) and multi-walled carbon nanotubes. These constituents are blended as per defined mixture rules, having matrix mass percentages of 100%, 95%, 90%, 85% and 80%, TiN and CNT mass percentages of 0%, 2.5%, 5%, 7.5%, and 10% are taken in equal proportions. The specimens are synthesized using centrifugal casting and layer-by-layer deposition of the matrix and the reinforcing constituents from the bottom to the top layer, as per the ASTM G33 standard. The uniform dispersion of the CNTs is assured with the aid of an ultrasonic cavitation methodology. The specimens are tested for their mechanical properties, such as tensile and impact strength, and macro- and microhardness. Scanning electron microscopy (SEM) together with energy-dispersive x-ray analysis (EDS) are utilized to verify a continuous phase and uniform dispersion of the carbon nanotubes and the titanium nitride within the matrix constituent. The results show that there is strong interfacial bonding with respect to layers and the interstitial region. Tribological characterization shows that the maximum wear rate, frictional force and coefficient of friction attained values of 100 microns, 15.6 N and 0.15 respectively, at a constant speed of 675 rpm and a wear time of 30 min, over a sliding distance of 9500 m. ANOVA and multiple linear regression are conducted to optimize and standardize the process parameters of the manufacturing process.