Stochastic mesoscale inhomogeneity of material properties and material symmetries are investigated in a 3D-printed material. The analysis involves a spatially-dependent characterization of the microstructure in 316 L stainless steel, obtained through electron backscatter diffraction imaging. These data are subsequently fed into a Voigt–Reuss–Hill homogenization approximation to produce maps of elasticity tensor coefficients along the path of experimental probing. Information-theoretic stochastic models corresponding to this stiffness random field are then introduced. The case of orthotropic fields is first defined as a high-fidelity model, the realizations of which are consistent with the elasticity maps. To investigate the role of material symmetries, an isotropic approximation is next introduced through ad-hoc projections (using various metrics). Both stochastic representations are identified using the dataset. In particular, the correlation length along the characterization path is identified using a maximum likelihood estimator. Uncertainty propagation is finally performed on a complex geometry, using a Monte Carlo analysis. It is shown that mechanical predictions in the linear elastic regime are mostly sensitive to material symmetry but weakly depend on the spatial correlation length in the considered propagation scenario.

研究 3D 打印材料中材料特性和材料对称性的随机介观不均匀性。该分析涉及 316 L 不锈钢微观结构的空间相关表征，通过电子背散射衍射成像获得。这些数据随后被输入 Voigt-Reuss-Hill 均质化近似，以生成沿实验探测路径的弹性张量系数图。然后引入与该刚度随机场相对应的信息论随机模型。首先将正交各向异性场的情况定义为高保真模型，其实现与弹性图一致。为了研究材料对称性的作用，接下来通过临时投影（使用各种度量）引入各向同性近似。两种随机表示都是使用数据集来识别的。特别地，使用最大似然估计器来识别沿表征路径的相关长度。最终使用蒙特卡罗分析在复杂的几何形状上执行不确定性传播。结果表明，线弹性状态下的力学预测主要对材料对称性敏感，但对所考虑的传播场景中的空间相关长度的依赖程度较弱。