Image-based material characterization of complex microarchitectured additively manufactured structures

• Published in: Computers & mathematics with applications (1987) Vol. 80; no. 11; pp. 2462 - 2480
• Main Authors: Korshunova, N., Jomo, J., Lékó, G., Reznik, D., Balázs, P., Kollmannsberger, S.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.12.2020; Elsevier BV
• Subjects: Additive manufacturing; Complexity; Computed tomography; Convolutional Neural Networks; Deep learning segmentation; Finite cell method; Finite element method; Geometrical defects; Image-based material characterization; Mesh generation; Microstructure; Porosity; Surface roughness; Three dimensional printing; Geometrical defects; Deep learning segmentation; Finite cell method; Additive manufacturing; Convolutional Neural Networks; Image-based material characterization
• ISSN: 0898-1221; 1873-7668; 1873-7668
• DOI: 10.1016/j.camwa.2020.07.018

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.