Bammann-Chiesa-Johnson粘塑性本构模型材料参数的一种识别方法

摘要

Whether the viscoplastic constitutive model can successfully simulate the high strain rate and large strain deformation of metals depends on the quality of material parameter identification.It is diffi-cult to determinate the material parameters in BCJ model by experiment directly.The reasons are due to that it considers the coupling of strain rate and temperature with material hardening as well as the strain rate and temperature history effects,and it also includes a number of parameters.To the coupling and history effects,based on clarification of physical significance of the material parameters,a method was provided to estimate the parameter value in this paper.Using this method,the parameters in BCJ model were decoupled and separated from each other,and the formulas for material parameters estimation were derived,the zone of the parameter value was estimated.On this basis,the radial return algorithm used to integrate the stress of the model and the particle swarm optimization algorithm were implemented,and then inverse analysis reconsidering the coupling and history effects in the model was applied to identify the material parameters by optimizing them over the estimated zone of parameter value.Take OFHC Cu as an example,the material parameters in BCJ model were obtained by using the proposed approach,the calculated results agree well with the experimental ones.%粘塑性本构模型能否成功模拟金属高应变率大应变变形过程依赖于材料参数识别结果的好坏。由于BCJ 模型考虑了应变率、温度与材料硬化之间的耦合效应以及应变率、温度历史效应，同时模型中包含了多个材料参数，因此很难通过试验直接识别模型的材料参数。本文针对BCJ 模型中的耦合效应和历史效应，基于对模型中材料参数物理涵义的界定，给出了一种对材料参数解耦、分离并进行估计的方法，获得了模型材料参数估计公式，估计了材料参数的取值范围。在此基础上，编制了BCJ 模型应力积分径向返回算法和粒子群优化算法的计算程序，应用重新设计了BCJ 模型耦合效应和历史效应的反分析方法，在参数取值范围内对材料参数进行了优化识别。以OFHC Cu为例，应用提出的识别方法对BCJ 模型的材料参数进行了识别，计算结果和试验结果符合较好。