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arXiv AI By Giacomo Rizzieri, Saif-Ur-Rehman, J\"org F. Unger, Annika Robens-Radermacher

Influence of Extruded Filament Shape on Buildability in 3D Concrete Printing: A Geometry-Informed Deep Learning-FEM Approach

Read the original on arXiv AI →

The paper presents a geometry‑informed modeling framework that couples a deep‑learning filament shape predictor (ShapeGen3DCP) with a layer‑activation finite element method to assess buildability in 3D concrete printing. By generating realistic filament geometries directly from material and process parameters, the approach eliminates the need for experimental filament characterization or fluid‑flow simulations. Validation and parametric studies show that extrusion parameters and filament shape significantly affect buildability predictions, especially for free‑flow deposition, and that an elliptical approximation balances fidelity and simplicity while volume‑conserving rectangular representations improve prediction reliability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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