Image-based material characterization of complex microarchitectured additively manufactured structures

TL;DR

This paper presents a novel, efficient image-based framework using high-order parallel Finite Cell Method for characterizing complex microarchitectured additively manufactured structures directly from CT-scan images, enabling better simulation of real-world AM parts.

Contribution

It introduces a flexible, non-geometry-conforming discretization method that simplifies the analysis of complex AM microstructures from CT images, improving computational efficiency.

Findings

The framework accurately captures microstructural variations.

Numerical results align well with experimental data.

The method reduces computational costs for complex geometries.

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.