4.7 Article

Physical mechanism based crystal plasticity model of NiTi shape memory alloys addressing the thermo-mechanical cyclic degeneration of shape memory effect

期刊

MECHANICS OF MATERIALS
卷 112, 期 -, 页码 1-17

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.mechmat.2017.05.005

关键词

NiTi shape memory alloys; Crystal plasticity; Shape memory effect; Cyclic degeneration; Dislocation slipping

资金

  1. National Natural Science Foundation of China [11532010, 11602203]
  2. project for Sichuan Provincial Youth Science and Technology Innovation Team, China [2013TD0004]
  3. Young Elite Scientist Sponsorship Program by CAST
  4. Fundamental Research Funds for the Central Universities [A0920502051707]

向作者/读者索取更多资源

Recent experimental observations show that a progressive degeneration of shape memory effect occurs during the thermo-mechanical cyclic deformation of NiTi shape memory alloys (SMAs), and aggravates with the increasing applied loading level. In this work, at first, the physical mechanism for the degeneration of shape memory effect is summarized, i.e., martensite transformation, reorientation, transformation-induced plasticity, reorientation-induced plasticity and their interactions, simultaneously. Then, a physical mechanism based crystal plasticity model is constructed by attributing the transformation- and reorientation-induced plasticity to the dislocation slipping at the interfaces between austenite and martensite phases and between different martensite variants, respectively. The thermodynamic driving forces of inelastic deformation processes and the thermodynamic constraints on the proposed constitutive equations are obtained from the Clausius's dissipative inequality and a newly constructed Helmholtz's free energy. The evolution equations of internal variables controlling the degeneration of shape memory effect are set to be dependent on the current dislocation density. Finally, the capability of the proposed model to describe the thermo-mechanical cyclic degeneration of the shape memory effect of NiTi SMAs is verified by comparing the predicted results with the corresponding experimental ones. The predicted results are in good agreement with the experimental ones. (C) 2017 Elsevier Ltd. All rights reserved.

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