Cellular automaton simulation of dynamic recrystallization behavior in V-10Cr-5Ti alloy under hot deformation conditions

The deformation behavior of V-10Cr-5Ti alloy was studied on the Gleeble-1500 thermomechanical simulator at the temperatures of 950-1350℃, and the strain rates of 0.01-10 s^-1. Based on the Arrhenius model, dislocation density model, nucleation model and grain growth model, a numerical cellular automaton (CA) model coupling simulation of hot deformation is established to simulate and characterize the microstructural evolution during DRX. The results show that the flow stress is fairly sensitive to the strain rate and deformation temperature. The error between the predicted stress by the Arrhenius model and the actual measured value is less than 8%. The initial average grain size calculated by the CA model is 86.25 μm, which is close to the experimental result (85.63 μm). The simulations show that the effect of initial grain size on the dynamic recrystallization microstructure evolution is not significant, while increasing the strain rate or reducing the temperature can refine the recrystallized grains.

Meaningful Update and Repair of Markov Decision Processes for Self-Adaptive Systems

Self-adaptive systems are able to adjust their behaviour in response to environmental condition changes and are widely deployed as Internetwares.Considered as a promising way to handle the ever-growing complexity of software systems,they have seen an increasing level of interest and are covering a variety of applications,e.g.,autonomous car systems and adaptive network systems.Many approaches for the construction of self-adaptive systems have been developed,and probabilistic models,such as Markov decision processes(MDPs),are one of the favoured.However,the majority of them do not deal with the problems of the underlying MDP being obsolete under new environments or unsatisfactory to the given properties.This results in the generated policies from such MDP failing to guide the self-adaptive system to run correctly and meet goals.In this article,we propose a systematic approach to updating an obsolete MDP by exploring new states and transitions and removing obsolete ones,and repairing an unsatisfactory MDP by adjusting its structure in a more meaningful way rather than arbitrarily changing the transition probabilities to values not in line with reality.Experimental results show that the MDPs updated and repaired by our approach are more competent in guiding the self-adaptive systems’correct running compared with the original ones.