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挤出长丝形状对3D混凝土打印可打印性的影响：基于几何信息的深度学习-有限元方法

文章背景与核心概要

3D混凝土打印（3DCP）结构的结构稳定性和可打印性很大程度上取决于沉积长丝的几何形态。传统的有限元方法（FEM）模拟通常依赖于对打印层简化的矩形表示，这可能会损害预测的准确性。

本文引入了一个“几何信息建模框架”，将基于深度学习的长丝形状预测工具 ShapeGen3DCP 与层激活有限元（FEM）方法相结合。通过直接从材料和工艺参数生成具有几何感知能力的数值模型，该方法消除了对昂贵的流体流动模拟或复杂实验表征的需求。该研究为选择高效的几何近似方法提供了实用指南，并强调了体积守恒显著提高了简化矩形网格的可靠性。

Executive Summary / 执行摘要

The structural stability and buildability of 3D concrete-printed (3DCP) structures heavily depend on the geometric morphology of the deposited filaments. Traditionally, finite element method (FEM) simulations rely on simplified rectangular representations of printed layers, which can compromise predictive accuracy.

This paper introduces a geometry-informed modeling framework that pairs the deep-learning-based filament shape prediction tool, ShapeGen3DCP, with a layer-activation FEM approach. By directly generating geometry-aware numerical models from material and process parameters, the methodology eliminates the need for expensive fluid-flow simulations or complex experimental characterizations. The study provides practical guidelines for selecting efficient geometric approximations, highlighting that volume conservation significantly improves the reliability of simplified rectangular meshes.

3D混凝土打印（3DCP）结构的结构稳定性和可打印性在很大程度上取决于沉积长丝的几何形态。传统上，有限元方法（FEM）模拟依赖于打印层的简化矩形表示，这可能会降低预测准确性。

本文引入了一个几何信息建模框架，将基于深度学习的长丝形状预测工具 ShapeGen3DCP 与层激活有限元（FEM）方法相结合。通过直接从材料和工艺参数生成具有几何感知能力的数值模型，该方法消除了对昂贵的流体流动模拟或复杂实验表征的需求。该研究为选择高效的几何近似方法提供了实用指南，强调体积守恒显著提高了简化矩形网格的可靠性。

Abstract / 摘要

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 assessment represent printed layers as simplified rectangles, potentially limiting predictive accuracy. This study proposes a geometry-informed modelling framework that integrates the deep-learning-based filament shape prediction tool ShapeGen3DCP with a layer-activation FEM approach to investigate the effect of realistic filament geometries on buildability. The framework generates geometry-aware numerical models directly from material and process parameters, eliminating the need for experimental filament characterization or computationally intensive fluid-flow simulations. Validation against experimental data and a parametric study of rectilinear walls demonstrate that extrusion parameters and the resulting filament geometry can significantly influence buildability predictions. Realistic filament representations are particularly important for free-flow deposition, whereas layer-pressing strategies are less sensitive to geometric simplifications. Among the investigated representations, an elliptical approximation provides an effective balance between geometric fidelity and modelling simplicity. When rectangular representations are preferred to enable regular computational meshes for faster simulations, defining their dimensions based on volume conservation improves prediction reliability compared with calibrating them using either the maximum filament width or the interlayer contact width. Overall, the proposed methodology demonstrates the importance of incorporating filament geometry into 3DCP simulations and provides practical guidance for selecting efficient and accurate geometric representations for buildability assessment.

沉积长丝的几何形态会显着影响3D混凝土打印（3DCP）结构的结构性能和稳定性。然而，大多数基于有限元（FEM）的可打印性评估方法将打印层表示为简化的矩形，这可能会限制预测准确性。本研究提出了一种几何信息建模框架，该框架将基于深度学习的长丝形状预测工具 ShapeGen3DCP 与层激活 FEM 方法相结合，以研究真实长丝几何形状对可打印性的影响。该框架直接从材料和工艺参数生成几何感知的数值模型，无需进行实验长丝表征或计算密集的流体流动模拟。针对实验数据的验证以及对直线墙的参数研究表明，挤出参数和由此产生的长丝几何形状会显著影响可打印性预测。真实的长丝表征对于自由流沉积特别重要，而层压策略对几何简化则不太敏感。在所研究的表征中，椭圆近似在几何保真度和建模简便性之间提供了有效的平衡。当首选矩形表示以实现规则计算网格以加快模拟速度时，与使用最大长丝宽度或层间接触宽度进行校准相比，基于体积守恒定义其尺寸可以提高预测可靠性。总体而言，所提出的方法论证明了将长丝几何形状纳入 3DCP 模拟的重要性，并为选择用于可打印性评估的高效且准确的几何表示提供了实用指导。

Key Findings & Methodology / 核心发现与方法论

  • Deep Learning Integration: Utilizes ShapeGen3DCP to predict filament geometry directly from process and material inputs, bypassing heavy fluid dynamics simulations.
  • Deposition Strategy Sensitivity:
  • Free-flow deposition: Highly sensitive to realistic filament geometries.
  • Layer-pressing strategies: Less sensitive, making geometric simplifications more viable.
  • Geometric Approximations:
  • Elliptical approximation: Delivers the optimal balance between modeling simplicity and geometric accuracy.

  • Rectangular representation: When used for regular computational meshes, dimensioning based on volume conservation yields higher prediction reliability than calibration via maximum width or interlayer contact width.

  • 深度学习集成： 利用 ShapeGen3DCP 直接从工艺和材料输入预测长丝几何形状，从而绕过繁重的流体动力学模拟。

  • 沉积策略敏感性：
  • 自由流沉积（Free-flow deposition）： 对真实的长丝几何形状高度敏感。
  • 层压策略（Layer-pressing strategies）： 敏感度较低，使得几何简化更具可行性。
  • 几何近似：
  • 椭圆近似： 在建模简便性和几何准确性之间提供了最佳平衡。
  • 矩形表示： 当用于规则计算网格时，基于体积守恒的尺寸设计比通过最大宽度或层间接触宽度校准具有更高的预测可靠性。

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