Evaluation of tensile strength of Al7075-SiC nanocomposite compacted by gas gun using spherical indentation test and neural networks

Abstract In this paper, fabrication of Al7075-SiC nanocomposite using dynamic compaction is investigated. Evaluation of material tensile characteristics are often destructive and costly and impossible for small samples. Therefore, instrumented indentation tests can be employed to obtain the tensile stress-strain curve of the material. In this investigation, the tensile stress-strain curve of Al7075-SiC nanocomposite is obtained using combination of spherical indentation test, finite element (FE) simulation, and neural networks. To fabricate the Al7075/SiC nanocomposite samples, Al7075 micron-sized powder and SiC nano particles were mixed to obtain Al7075-5 vol% SiC, and Al7075-10 vol% SiC and the mixtures were mechanically milled. The mixtures were then consolidated under impact loading by a single-stage gas gun. The compaction was performed at 573 K temperature and a maximum relative density of 97.5% was obtained. The load-penetration curve for each nanocomposite sample was obtained using spherical indentation test on the compacted samples. The corresponding numerical curve for each sample was also obtained by finite element simulation of the indentation process. Finally, using the experimental and the FE results, the constants of Hollomon’s material model were determined using artificial neural networks (ANN). The results indicated that SiC nano-particles reinforcement of Al7075 increased the tensile strength of the nanocomposite by around 300%.