4.5 Article

A viscoplastic theory of shape memory polymer fibres with application to self-healing materials

出版社

ROYAL SOC
DOI: 10.1098/rspa.2011.0628

关键词

shape memory polymer; fibre; self-healing; plasticity

资金

  1. LONI Institute, under the Louisiana Board of Regents [LEQSF(2007-12)-ENH-PKSFI-PRS-01]
  2. Center for Computation and Technology (CCT) at Louisiana State University
  3. US National Science Foundation [CMMI 0900064]
  4. NASA [NNX11AM17A]
  5. Louisiana Board of Regents [NASA/LEQSF(2011-14)-Phase3-05]
  6. LONI
  7. CCT
  8. NSF
  9. LEQSF
  10. Div Of Civil, Mechanical, & Manufact Inn
  11. Directorate For Engineering [0900064] Funding Source: National Science Foundation

向作者/读者索取更多资源

The difficulty in healing structural damage is that most existing schemes need external help to bring the fractured surfaces in contact before healing can occur. To facilitate the existing schemes to heal macroscopic cracks, we envision that the cracked surfaces can be brought in contact through constrained shape recovery of a shape memory polymer (SMP) fibre-reinforced grid skeleton that is embedded in thermoset polymer matrix, similar to stitch a cut in the human skin by suture. In this study, we show that polyurethane SMP fibres can be hardened through cyclic cold-drawing programming, which makes them suitable for reinforcement and healing in thermoset polymer composites. We characterized the microstructure of the SMP fibres, which provides fundamental understanding of the effect of programming on the degree of crystallinity and molecular orientation. Then, a micromechanical multiscale viscoplastic theory is developed to predict the thermomechanical behaviours of the SMP fibres, including the cyclic hardening and stress recovery responses. The proposed theory takes into account the stress-induced crystallization process and the evolution of the morphological texture based on the applied stresses. The cyclic loading and the thermomechanical responses of the SMP fibres confirm the capabilities of the proposed model in capturing these phenomena.

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