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<p>To meet the deformation requirements of embodied intelligence robotic joint coverings, this study investigates the structural design and tensile behavior of highly stretchable weft-knitted fabrics produced using four yarn types and seven knit structures (28 samples in total). Tensile properties, cyclic elastic recovery, and plastic deformation were systematically evaluated, and deformation behavior under flexion (0°–110°) and torsion (0°–120°) was quantified using a 3D-printed joint model combined with a grid-based measurement method. Results show that fabric structure is the dominant factor governing elastic performance (<i>η</i><sup>2</sup> = 0.740–0.995), while the outer polyester filament fineness of spandex-covered yarns play a secondary role. Rib and purl structures exhibit a clear directional reversal in extensibility and recovery behavior due to loop geometry differences. Under joint motion, local fabric elongation reaches up to 110% during flexion and approximately 21% during torsion, indicating strongly non-uniform deformation distributions. Based on these findings, a zoned design strategy for robotic joint coverings is proposed, assigning different knitted structures to functional regions according to deformation characteristics. This work provides a structure–deformation–function framework for the design of textile-based robotic skins.</p>

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Structural Design and Tensile Performance of High-Stretch Fabric for Embodied Intelligence Outer Covering

  • 期刊论文

  • 发表日期：

    2026年7月26日

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  • Binlin Shi,
  • Zhijia Dong,
  • Kai Wang,
  • Bo Liu,
  • Honglian Cong,
  • Pibo Ma

摘要

To meet the deformation requirements of embodied intelligence robotic joint coverings, this study investigates the structural design and tensile behavior of highly stretchable weft-knitted fabrics produced using four yarn types and seven knit structures (28 samples in total). Tensile properties, cyclic elastic recovery, and plastic deformation were systematically evaluated, and deformation behavior under flexion (0°–110°) and torsion (0°–120°) was quantified using a 3D-printed joint model combined with a grid-based measurement method. Results show that fabric structure is the dominant factor governing elastic performance (η2 = 0.740–0.995), while the outer polyester filament fineness of spandex-covered yarns play a secondary role. Rib and purl structures exhibit a clear directional reversal in extensibility and recovery behavior due to loop geometry differences. Under joint motion, local fabric elongation reaches up to 110% during flexion and approximately 21% during torsion, indicating strongly non-uniform deformation distributions. Based on these findings, a zoned design strategy for robotic joint coverings is proposed, assigning different knitted structures to functional regions according to deformation characteristics. This work provides a structure–deformation–function framework for the design of textile-based robotic skins.