Image-based material characterization of complex microarchitectured additively manufactured structures

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...

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Bibliographic Details
Published in:Computers & mathematics with applications (1987) 2020-12, Vol.80 (11), p.2462-2480
Main Authors: Korshunova, N., Jomo, J., Lékó, G., Reznik, D., Balázs, P., Kollmannsberger, S.
Format: Article
Language:English
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
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Summary: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.
ISSN:0898-1221
1873-7668
DOI:10.1016/j.camwa.2020.07.018