Bammann-Chiesa-Johnson粘塑性本构模型的参数识别方法与验证

Bammann-Chiesa-Johnson(BCJ)粘塑性本构模型对材料力学响应的再现和预测能力强烈依赖于其模型参数的确定,而模型参数的确定往往是通过反分析方法来进行。由于BCJ粘塑性模型包含了应变、应变率和温度耦合效应以及加载路径和温度历史,其常数多达18个,所以寻找最佳的模型参数识别值十分繁琐。针对BCJ本构模型参数复杂、识别困难的问题,本文基于参数的物理意义,在准静态、蠕变及动态加载试验基础上,通过模型参数解耦分离、粒子群智能优化的方法分6步对18个材料常数进行识别,并用识别结果对1060纯铝动态加载试验力学响应进行模拟,模拟结果与试验结果符合良好。通过定量化误差分析,证明了BCJ粘塑性模型对实验数据的预测具有较高精度,该模型参数识别方法科学可行。

A Comprehensive Method of Parameter Identification and Validation for Bammann-Chiesa-Johnson Viscoplasticity Constitutive Model

The Bammann-Chiesa-Johnson (BCJ) viscoplasticity constitutive model is advanced to predict mechanical behavior of metals. And the capability of prediction relies on the determination of the model parameters. Normally, the parameters would be identified by using the back-analysis method. However, the method is very complicated because there are quite a number of parameters in the BCJ model and it is not easy to obtain the optimal values. These parameters are involved to describe the coupling effects of strain, strain rate, temperature, as well as the load path and temperature history. This paper proposed a method to identify the 18 papameters, in which comprehensive experiments, based on the physics of the parameters, had been conducted, including quasi-static tests, creep tests and the split Hopkinson pressure bar (SHPB) tests, furthermore parameters decoupling and the Particle Swarm Optimization (PSO) algorithm had been applied. The dynamic mechanical response of Al 1060 was taken to validate the method and the prediction on flow stressesis in good agreement with the test data. The quantitative error analysis showed that the method was effective for a large range of strain rate and temperature variation with high accuracy.