Multi-level mechanical modeling and computational design framework for weft knitted fabrics
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912663301259264 |
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| author | Pasquier, Cosima du Jeong, Sehui Liu, Pan Williams, Susan Mnejja, Nour Okamura, Allison M. Tibbits, Skylar Chen, Tian |
| author_facet | Pasquier, Cosima du Jeong, Sehui Liu, Pan Williams, Susan Mnejja, Nour Okamura, Allison M. Tibbits, Skylar Chen, Tian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-level mechanical modeling and computational design framework for weft knitted fabrics Pasquier, Cosima du Jeong, Sehui Liu, Pan Williams, Susan Mnejja, Nour Okamura, Allison M. Tibbits, Skylar Chen, Tian Soft Condensed Matter 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. |
| title | Multi-level mechanical modeling and computational design framework for weft knitted fabrics |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2501.07567 |