Phenomenological constitutive equations contain material parameters which cannot be measured directly in the experiment. We address the problem of error-resistant parameter identification for models of large strain elasto-plasticity. The identification is based on tests with a heterogeneous stress state. A methodology is presented which allows us to assess the reliability of identification strategies in terms of their sensitivity to measurement errors. A vital part of the methodology is the mechanics-based metric in the space of material parameters. The measure of sensitivity is the size of a parameter cloud, computed using this metric. Efficient procedures of Monte Carlo type for computation of the parameter cloud are presented and discussed. The methodology is exemplified in terms of a model with combined nonlinear isotropic-kinematic hardening. First, for an aluminum alloy, non-monotonic torsion tests with different sample cross sections are analyzed. Second, for the identification of hardening parameters of steel, three different tension–compression samples are considered. In both examples, various combinations of tests are checked for sensitivity to measurement errors identifying best and worst combinations.

中文翻译：

---

用于可靠识别弹塑性参数的样品形状

现象学本构方程包含无法在实验中直接测量的材料参数。我们解决了大应变弹塑性模型的抗错误参数识别问题。该识别基于具有异质应力状态的测试。提出了一种方法，使我们能够根据识别策略对测量误差的敏感性来评估识别策略的可靠性。该方法的一个重要部分是材料参数空间中基于力学的度量。灵敏度的度量是参数云的大小，使用该度量计算。提出并讨论了用于计算参数云的蒙特卡罗类型的有效程序。该方法以具有组合非线性各向同性-运动硬化的模型为例。首先，对于铝合金，分析了具有不同样品横截面的非单调扭转试验。其次，为了确定钢的硬化参数，考虑了三种不同的拉伸-压缩样品。在这两个示例中，检查各种测试组合对测量误差的敏感性，确定最佳和最差组合。