Significant developments in the field of additive manufacturing (AM) allowed the fabrication of complex microarchitectured components with varying porosity across different scales. However, due to the high complexity of this process, the final parts can exhibit significant variations in the nominal geometry. Computed tomographic images of 3D printed components provide extensive information about these microstructural variations, such as process-induced porosity, surface roughness, and other undesired morphological discrepancies. Yet, techniques to incorporate these imperfect AM geometries into the numerical material characterization analysis are computationally demanding. In this contribution, an efficient image-to-material-characterization framework using the high-order parallel Finite Cell Method is proposed. In this way, a flexible non-geometry-conforming discretization facilitates mesh generation for very complex microstructures at hand and allows a direct analysis of the images stemming from CT-scans. Numerical examples including a comparison to the experiments illustrate the potential of the proposed framework in the field of additive manufacturing product simulation.

增材制造（AM）领域的重大发展使制造复杂的微体系结构组件的孔隙率在不同规模上得以实现。但是，由于此过程的高度复杂性，最终零件的标称几何形状可能会出现显着变化。3D打印组件的计算机断层扫描图像可提供有关这些微结构变化的大量信息，例如过程引起的孔隙率，表面粗糙度和其他不良形态学差异。然而，将这些不完美的AM几何形状并入数值材料表征分析的技术在计算上是需要的。在此贡献中，提出了一种使用高阶并行有限元方法的有效图像到材料表征框架。通过这种方式，灵活的不符合几何形状的离散化功能有助于为非常复杂的微观结构生成网格，并可以直接分析源自CT扫描的图像。包括与实验进行比较的数值示例说明了所提出框架在增材制造产品仿真领域的潜力。