DOE OSTI · 3023844
A meshing framework for digital twins for extrusion based additive manufacturing
Abstract
Additive manufacturing (AM) allows for manufacturing of complex three-dimensional geometries not typically realizable with standard manufacturing practices. The internal microstructure of AM components has a significant impact on mechanical, vibrational, and shock properties and permits richer design space when this is controllable. Due to complex interactions of internal geometry of an extrusion-based AM component, it is common practice to assume homogeneous behavior or to perform characterization testing on specific toolpath configurations. To avoid testing or material waste, it is necessary to develop a consistently accurate numerical simulation framework with relevant boundary value problems that can handle the complicated geometry of internal material microstructure present in AM components. Herein, a framework is proposed to directly create computational meshes suitable for finite element analysis (FEA) of the fine-scale features generated from extrusion-based AM tool paths to maintain a strong process–structure–property-performance linkage. This mesh can be manually or automatically analyzed using standard FEA simulations such as quasi-static preloading or modal analysis. The framework allows an in-silico assessment of a target AM geometry where fine-scale features greatly impact quantities of design interest such as in soft elastomeric lattices where toolpath infill can greatly influence the self-contact of a structure in compression, which we use as a motivating exemplar. This approach greatly reduces both time and resource waste present in traditional build and test design cycles for non-intuitive design spaces, and acts as a tool for use in the production of a key component of a digital twin, a mesh suitable for finite element analysis. In conclusion, it also further allows for the exploration of toolpath infill to optimize component properties beyond simple linear properties such as density and stiffness.
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Gallup, Lucas Kelly [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0009000590132158), Long, Kevin Nicholas [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0000000347184161), Roach, Devin J. [Oregon State Univ., Corvallis, OR (United States)] (ORCID:0000000288250340), Reinholtz, William Derek [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)], Cook, Adam [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0000000306951520), Hamel, Craig M. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0000000340869010). 2026-03-10. A meshing framework for digital twins for extrusion based additive manufacturing. https://doi.org/10.1016/j.addma.2026.105137
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