▎ 摘　　要

Through hot compression experiments at temperatures ranging from 603 to 723 K and strain rates ranging from 0.01 to 10 s(-1), the hot deformation behavior of a 0.5 wt% graphene nanoplatelet-reinforced aluminum (0.5 wt% GNP/Al) composite prepared by the powder metallurgy method was studied. The constitutive equations obtained by mathematical models and a neural network were evaluated. The deformation property of the composite can be better described by the Johnson-Cook (JC) constitutive model optimized by establishing a relationship between the coefficient and variables obtained in the hot compression test, with a correlation coefficient (R) reaching 99.97% with the average relative error of 0.37% (98.1 and 4.17%, respectively, before optimization). Compared with the JC model, the neural network has perfect calculation accuracy and whole-process effectiveness, providing expanded and more accurate constitutive equations for subsequent simulations and for building the dynamic recrystallization model of the composite. The dynamic recrystallization model, hot processing map, and EBSD results are in agreement with each other and indicate that the optimal strain rate and temperature range of the composite are 0.01-0.1s(-1) and 693-723 K, respectively.