纬编织物的多级力学建模与计算设计框架
Multi-level mechanical modeling and computational design framework for weft knitted fabrics
AI总结：
本研究提出纬编织物多级建模设计框架，结合体积有限元分析、均化简化模型与力学类比方法，可快速准确预测力学性能，支持异质织物及定制化针织产品开发。
摘要：
本研究提出了一种面向纬编织物的多级建模与设计框架，其核心是基于基本原理的体积有限元分析，用于捕捉织物的力学行为。通过引入纱线层级的数据，该框架能够准确预测应力-应变响应，减少了对大量物理测试的依赖。研究采用简化的应变能方法，将计算结果均化为三个关键变量，可在数分钟内完成快速、准确的预测。经实验验证后，本框架无需额外测试即可模拟新型针织面料。\n在实际应用场景中，织物常存在纱线材料或花型的差异。该框架可扩展应用于异质织物，研究表明不同区域间的过渡特性可通过简单的力学类比（串联弹簧与并联弹簧）来描述，这使得异质纺织品可被视为均质单元拼接而成的理想拼合结构，同时保持预测精度。研究通过设计并制作一款压力均匀的压缩袖套验证了该方法，展示了该框架如何支持针对特定辅助力度和解剖特征定制针织产品的开发。通过结合体积有限元分析、均化简化模型以及可控的材料过渡设计，该方法为下一代纬编织物设计提供了可扩展、高保真的实现路径。
英文摘要：
This work presents a multi-level modeling and design framework for weft knitted fabrics, beginning with a volumetric finite element analysis capturing their mechanical behavior from fundamental principles. Incorporating yarn-level data, it accurately predicts stress-strain responses, reducing the need for extensive physical testing. A simplified strain energy approach homogenizes the results into three key variables, enabling rapid, accurate predictions in minutes. After validation against experiments, our framework can simulate new knit fabrics without additional tests. In real-world scenarios, fabrics often feature variations in yarn materials or patterns. The framework extends to heterogeneous fabrics, showing that transitions between distinct regions can be captured using simple mechanical analogies: springs in series and parallel. This allows heterogeneous textiles to be treated as idealized patchworks of homogeneous pieces, preserving predictive accuracy. The method is demonstrated by designing and producing a compression sleeve with uniform pressure, illustrating how the framework supports development of knits tailored to specific assistance levels and anatomical features. By combining volumetric finite element analysis, simplified model through homogenization, and controlled material transitions, this approach provides a scalable, high-fidelity path toward next-generation weft knitted fabric design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
