Image-Based Geometrical Characterization of Nodes in Additively Manufactured Lattice Structures.

Additive manufacturing allows for the creation of lattice structures with tailored mechanical and thermal properties. Prior research has shown that lattice structures often contain geometric deviations in struts, which affect numerical predictions. However, the geometry of nodes remains poorly understood despite their influence on structural performance. No prior work had resolved the impact of node-level deviations in lattice structures. This gap motivated the need to examine as-manufactured node geometries in detail. Understanding how nodes differ from CAD models can help improve design accuracy. Prior knowledge has shown that struts are commonly studied, but nodes are less explored. This uncertainty drove the development of new methods to analyze node geometry. The goal is to bridge the gap between CAD expectations and real-world AM outcomes.

This study aimed to investigate the geometrical characteristics of nodes in additively manufactured lattice structures. The specific problem is the lack of detailed understanding of how node geometry deviates from CAD models. The motivation stems from the need to improve numerical models for AM lattices. Current models often overlook node-level variations, which may affect mechanical predictions. The researchers propose to use μCT imaging to capture real-world node geometries. This approach allows for quantification of deviations at the node level. The study focuses on face-centered cubic and axial strut lattices fabricated via selective laser melting. The goal is to develop tools for isolating and analyzing node geometries.

The study employed X-ray microcomputed tomography to capture detailed images of as-manufactured lattice structures. A custom software tool was developed to isolate and classify nodal joints from μCT-derived cross-sectional slices. The tool enabled automated extraction of geometrical properties from each node. These properties were compared with their idealized CAD model counterparts. The method involved analyzing face-centered cubic and axial strut lattices fabricated via selective laser melting. Cross-sectional slices were used to assess deviations in node geometry. The process included auto-isolation of nodal regions to ensure accurate comparisons. The researchers propose that this method improves the ability to quantify geometric uncertainties.

The study found significant variations between as-manufactured node geometries and their CAD counterparts. These deviations were quantified using μCT-derived cross-sectional slices. The custom tool successfully isolated and classified nodal joints for analysis. Face-centered cubic and axial strut lattices showed distinct geometrical differences. Node geometry deviations were found to be non-uniform across the lattice structure. The results suggest that node-level uncertainties may affect mechanical predictions. The researchers propose that these findings can inform improved lattice design. Quantification of defects provides insight into how nodes differ from idealized models.

The authors suggest that quantifying node-level geometrical deviations improves numerical modeling accuracy. Their findings indicate that as-manufactured nodes differ from CAD models in non-uniform ways. This research is an initial step toward better node design for AM lattices. The study proposes that understanding node geometry helps refine lattice performance predictions. The researchers suggest that their method can be extended to other lattice types and AM processes. The results support the need for more detailed node-level analysis in AM research. The authors propose that future work should focus on integrating these findings into design workflows. This approach may enhance the reliability of numerical simulations for AM structures.