Physically based constitutive equations incorporating the key microstructural mechanisms e.g. dislocations, grain size, etc, have been used widely to predict the stress-strain behaviour of alloys at plastic and viscoplastic conditions. This enables an accurate prediction of the formed geometry as well as the final underlying microstructures. However, these physically based constitutive equations have not been practically validated due to the lack of systematic experimental data at microscopic scale. This leads to a large number of unknown constants required to be determined through various optimization algorithms. The aim of this paper is to provide direct and systematic experimental data by revealing the dislocation (geometrically necessary one) density and grain size evolution of AA6082, which is a widely used high-strength aluminium alloy for automobile structural panels, as functions of strain, strain rate and temperature, and is the first-time using Electron Back Scattering Diffraction (EBSD) technique to visualize the microstructures during the hot deformation. The evolution of the dislocation accumulation during the hot tensile deformation at 300, 450, 530 °C using various strain rates (i.e. 1/s, 0.1/s, 0.01/s) was achieved. EBSD maps were analysed on samples submitted to a true strain level of ~0.1 and ~0.3 under each condition. These maps cover >3000 grains and enable to capture the statistical nature of the geometrically necessary dislocation densities during hot deformation. Despite the rapidly plateaued flow stress curves at high temperatures, a continuously increased average GND density was observed in AA6082 with the imposed true strain levels under all conditions. Dislocation channel structures were observed in the hot deformed samples. Dynamic recrystallization was also observed, which coupled with the GNDs and affected the hardening behaviour of the flow stress-strain curves. This work is the first study, using EBSD, to visualize the high temperature and high strain rate induced dislocation distributions over a relatively large area, providing valuable data that may be used for subsequently improving and calibrating the physically based material models.
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