Parameter identifiability analysis: Mitigating the non-uniqueness issue in the inverse identification of an anisotropic yield function

作者：Yi Zhang, Albert Van Bael, A. Andrade‐Campos, Sam Coppieters · 发表于：International Journal of Solids and Structures · 年份：2022 · DOI：10.1016/j.ijsolstr.2022.111543 · 被引用次数：45 · 研究领域：Optical measurement and interference techniques、Metal Forming Simulation Techniques、Non-Destructive Testing Techniques

A large number of conventional mechanical tests are required to calibrate advanced anisotropic yield functions. A well-designed non-conventional mechanical test generating information-rich inhomogeneous strain fields enables to reduce the experimental effort via inverse identification methods. Gradient-based inverse identification methods have repeatedly been reported to yield non-unique solutions for advanced anisotropic yield functions. Although the generic design of non-conventional mechanical tests progressively improves by maximizing the information content using indicators for describing the inhomogeneity of the strain field, it remains questionable whether the uniqueness problem can be avoided for advanced anisotropic yield functions. The latter problem is studied in this paper through the concept of parameter identifiability. A general identifiability analysis framework is proposed enabling to efficiently quantify the parameters sensitivity strength as well as parameter interactions. The feasibility of the framework is assessed through the inverse identification of the Yld2000-2d yield function using a virtual experiment, i.e. a complex notched tensile specimen designed via shape optimization to maximize the strain field inhomogeneity. The advanced synthetic experimentation approach is crucial to avoid errors due to the choice of an inappropriate constitutive model, yet allows accounting for the intrinsic Digital Image Correlation (DIC) filter. For the non-conventiona...