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

几何感知的深度学习-有限元方法增强3D混凝土打印可构建性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

一项新研究提出了一个用于3D混凝土打印（3DCP）的几何感知建模框架，该框架集成了深度学习与有限元分析（FEM）。该方法旨在通过使用真实的丝材几何形状而非简化的矩形来提高可构建性评估的准确性。该框架包含一个名为ShapeGen3DCP的基于深度学习的工具，可直接从材料和工艺参数生成几何感知的数值模型。验证研究表明，挤出参数和丝材形状对可构建性预测有显著影响，尤其是在自由流动沉积场景下。 AI

影响通过提高模拟保真度，这项研究可能带来更准确、更高效的3D混凝土打印设计和施工流程。

排序理由该集群包含一篇详细介绍3D混凝土打印模拟新方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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本文如何被排名

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Giacomo Rizzieri, Saif-Ur-Rehman, J\"org F. Unger, Annika Robens-Radermacher · 2026-09-04 04:00

挤出丝形状对3D混凝土打印可构建性的影响：一种几何信息驱动的深度学习-有限元方法

arXiv:2609.04028v1 Announce Type: cross Abstract: The geometric morphology of deposited filaments can significantly influence the structural performance and stability of 3D concrete-printed (3DCP) structures. However, most finite element (FEM)-based approaches for buildability as…

本文如何被排名

报道来源 [1]

挤出丝形状对3D混凝土打印可构建性的影响：一种几何信息驱动的深度学习-有限元方法

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