JOURNAL ARTICLE

Temperature dependent tensile strength modeling and analysis of shape memory polymers with physics‐based energy equivalence principle.

  • Published In: Journal of Applied Polymer Science, 2023, v. 140, n. 29. P. 1 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Zhang, Xiaoyong; Li, Ying; Zuo, Mini; Yang, Mengqing; Li, Weiguo 3 of 3

Abstract

The quantitative characterization of the tensile strength of shape memory polymers (SMPs) at different temperatures has always been an important research topic. In this study, the critical failure energy density of SMPs including the strain energy density, potential energy and kinetic energy of atomic motion per unit volume is first introduced. Then, based on the equivalent contribution of these energies on material failure, a temperature dependent tensile strength (TDTS) model considering the corresponding physical mechanism for SMPs is established. The model provides the quantitative relationship among temperature, Young's modulus, hardening index and the tensile strength of SMPs. Meanwhile, the predicted results of the proposed model are compared with the available TDTS of SMPs, and the agreement between theory and experiment is satisfactory. In addition, the influencing factors of tensile strength and their variation with temperature are analyzed. This work contributes the novel insight for the theoretical predictions on the TDTS of SMPs, which is helpful for the high temperature strength evaluation and property optimization. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Applied Polymer Science. 2023/08, Vol. 140, Issue 29, p1
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2023
  • ISSN:00218995
  • DOI:10.1002/app.54060
  • Accession Number:164480673
  • Copyright Statement:Copyright of Journal of Applied Polymer Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.