Abstract:

At the temperature of 653-713 K and the strain rate of 0.01-10 s^-1， Hot compression test of w（GNP/7075Al）= 0.5% composite was applied， and strain compensated Arrhenius and BP neural network model were established. At the same time， the hot processing map and dynamic recrystallization volume fraction prediction model of the composite were established. The hot deformation behavior of the composite was studied， and the hot processing parameters of the composite were determined. The results show that the predicted values of flow stress obtained by BP neural network model are in good agreement with the experimental results. The highest correlation coefficient is 99.998 3%， and the minimum absolute value of average relative error is 0.5%. It shows that neural network has high prediction accuracy for the hot deformation behavior of w（GNP/7075Al）= 0.5% composites. The optimum deformation temperature and strain rate of w（GNP/7075Al）= 0.5% composite are 685-705 K and 0.01-0.1 s^-1， respectively. Dynamic recrystallization （DRX） tends to occur at low strain rates and high deformation temperatures. Numerical simulation and hot extrusion test show that the profile with good surface quality can be extruded under the temperature of 693K and extrusion speed 1mm/min.

Key words: materials processing engineering, GNP/Al composites, thermal deformation, constitutive equation, thermal processing map, recrystallization model, numerical simulation