含动态再结晶粘塑性模型的参数识别

Parameter Identification of Viscoplastic Model Considering Dynamic Recrystallization

针对含动态再结晶粘塑性模型中的材料参数应用传统的测试方法很难准确测定的问题,吸收了遗传算法、增广高斯-牛顿算法、Levenberg-Marquardt算法和可变多面体算法的优点,构造了一套混合的全局优化算法.以26Cr2Ni4MoV为例,以镦粗实验提供的实验数据和刚塑性有限元模拟提供的数值解差值的l2范数的平方作为目标函数,应用构造的算法识别了该模型中的材料参数,计算结果和实验结果符合良好.

The viscoplastic model considering dynamic recrystallization describes the coupling process of macroscopic deformation and microstructure evolution during hot working. It is difficult to measure the material parameters accurately by means of traditional testing methods. A hybrid global optimization algorithm is designed, which combines the strengths of genetic algorithm, Levenberg-Marquardt algorithm, augmented Gauss-Newton method and flexible tolerance method. The square sum of the norm of the difference between the experimental values obtained from upsetting experiment and the calculated values obtained from finite element simulation is defined as an objective function. Taking 26Cr2Ni4MoV as an example, the material parameters are identified by the designed algorithm. The comparison between simulated and experimental results shows that the calculated results are well with the experimental. This indicates that the constructed algorithm can effectively identify the material parameters of the model and the model can describe accurately the evolution of microstructure during hot working.