Image-based material characterization of complex microarchitectured additively manufactured structures

Abstract

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. Computer 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.

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Cited by in corpus (6)

• Deep Learning in Deterministic Computational Mechanics
• Uncertainty quantification of microstructure variability and mechanical behaviour of additively manufactured lattice structures
• Support Vector Machine Guided Reproducing Kernel Particle Method for Image-Based Modeling of Microstructures
• Finite Cell Method for functionally graded materials based on V-models and homogenized microstructures
• Two-scale analysis and design of spaceframes with complex additive manufactured nodes
• Bending behavior of additively manufactured lattice structures: numerical characterization and experimental validation