基于全局优化算法的超塑性本构模型参数的识别

考虑晶粒长大的超塑性本构模型能否成功模拟成形过程依赖于参数选取的好坏,由于该模型涉及多个物理过程且每个过程都很难同其他过程区别开来,同时模型中包含多个材料参数,因此很难通过试验直接识别模型中的材料参数。模型中材料参数通过反分析方法进行。给出需要识别参数的超塑性本构模型,以使晶粒尺寸—时间关系和应力—应变关系计算值和试验值差值的加权平方和最小化为目标,构造目标函数;基于参数物理意义和数值结果给出参数取值范围。基于目标函数特性构造一全局优化算法,该算法吸收遗传算法能进行全局搜索的优点和Levenberg-Marquardt算法和增广Gauss-Newton算法收敛速度比较快的优点。针对某些参数取值范围比较大的特点,设计出同时使用指数编码和传统实型编码的混合编码的遗传算子。最后以Ti-6Al-4V为例,应用构造的算法识别超塑性本构模型中的材料参数,计算结果和试验结果符合较好。

Parameter Identification of Superplastic Constitutive Model Based on Global Optimization Method

Whether the superplastic constitutive model considering grain growth can successfully simulate the forming process depends on the quality of parameter identification. However, it is difficult to obtain satisfactory parameters by experiment directly. The reasons are due to that it considers many physical processes and it is difficult to differentiate one physical process from other physical processes, at the same time, the model is involved many parameters. The material parameters are identified by the inver...