Identification from field measurements allows several parameters to be identified from a single test, provided that the measurements are sensitive enough to the parameters to be identified. To do this, authors use empirically defined geometries (with holes, notches...). The first attempts to optimize the specimen to maximize the sensitivity of the measurement are linked to a design space that is either very small (parametric optimization), which does not allow the exploration of very different designs, or, conversely, very large (topology optimization), which sometimes leads to designs that are not regular and cannot be manufactured. In this paper, an intermediate approach based on a non-invasive CAD-inspired optimization strategy is proposed. It relies on the definition of univariate spline Free-Form Deformation boxes to reduce the design space and thus regularize the problem. Then, from the modeling point of view, a new objective function is proposed that takes into account the experimental setup and constraint functions are added to ensure that the gain is real and the shape physically sound. Several examples show that with this method and at low cost, one can significantly improve the identification of constitutive parameters without changing the experimental setup.

中文翻译：

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基于样条的试样形状优化，用于稳健的材料模型校准

从现场测量中识别允许从单个测试中识别多个参数，前提是测量对要识别的参数足够敏感。为此，作者使用经验定义的几何形状（带有孔、槽口……）。第一次优化试样以最大化测量灵敏度的尝试与设计空间相关联，该设计空间要么非常小（参数优化），不允许探索非常不同的设计，要么相反，非常大（拓扑优化)，这有时会导致设计不规则且无法制造。在本文中，提出了一种基于非侵入式 CAD 启发优化策略的中间方法。它依靠单变量样条自由形式变形框的定义来减少设计空间，从而使问题正规化。然后，从建模的角度出发，提出了一个新的目标函数，该目标函数考虑了实验设置并添加了约束函数，以确保增益真实且形状物理合理。几个例子表明，使用这种方法并且成本低，可以显着改善本构参数的识别，而无需更改实验设置。