A dynamic recrystallization （DRX） cellular automaton （CA） model that can mark the microstructure with DRX circle was developed. The effects of initial grain size on the stress-strain curve, mean grain size and DRX fraction were mainly investigated, and the simulated results were compared with those obtained from previous researches. The results show that the shape of the stress-strain curve is sensitive, while the stress and mean grain size at the steady state are insensitive to the initial grain size. The transition from a multiple-peak stress-strain curve to a single-peak one can be explained by variations in DRX circle fraction, and the initial grain size to make this transition is between 70 and 80 tim.
Baojun YU Xiaojun GUAN Lijun WANG Qingkai ZENG Qianqian LIU Yu CAO
Isothermal compression tests at temperatures from 1 273 to l 423 K and strain rates from 0.1 to 10 s-q were carried out to investigate the flow behaviors of Q420qE steel. Stress-strain data collected from the tests were employed to establish the constitutive equation, in which the influence of strain was incorporated by considering the effect of strain on material constants Q, n, a, and lnA. The results show that the flow stress curves are dependent on the strain, strain rate and deformation temperature. They display typical dynamic recrystallization behavior and consist of three stages, i.e., hardening stage, softening stage and steady stage. The flow stress decreases with increasing the deformation temperature and decreasing the strain rate. In addition, the flow stress data predicted by the proposed constitutive model agree well with the corresponding experimental results, and the correlation coefficient and the average absolute relative error between them are 0.990 3 and 3.686%, respectively.
The microstructures and their kinetics of normal grain growth are simulated using different Monte Carlo （MC） algorithms. Compared with the relative figures and the theoretical normal grain growth exponents of n =0.5, the effects of some factors of MC algorithm, i.e. the lattice types, the methods of selecting lattice sites, and the neighbors selection for energy calculations, on the simulation results of grain growth are studied. Two methods of regression were compared, and the three-parameter nonlinear regression is much more suitable for fitting the grain growth kinetics. A better model with appropriate factors included triangular lattice, the attempted site randomly selected, and the first and second nearest neighbors for energy calculations is obtained.
