Luka Roblek (Author), Robert Kunc (Author), Andrej Žerovnik (Author), Jože Korelc (Author), Alma Brambilla (Author), Lorenza Petrini (Author)

Abstract

The behavior of shape memory alloys exhibits significant asymmetry under tension, compression, and torsion, posing challenges for accurate modeling in engineering applications. This study introduces a novel approach to improve constitutive models by incorporating an asymmetric function that enables independent parameter definitions for tension, compression, and torsion. This innovation allows material parameters to be determined directly from stress-strain data, enhancing model flexibility and practicality. The proposed method is implemented in the widely used Auricchio-Petrini model, extending its capabilities to capture asymmetry in transformation start stress, hysteresis width, transformation plateau length, and hardening slope, all while maintaining thermomechanical consistency. In the proposed material model, the elastic modulus is differentiated for austenite, multi-variant martensite, and single-variant martensite, with the latter exhibiting an introduced asymmetry. The model also incorporates improved temperature dependency. Validation through comparisons with experimental data demonstrate the model's effectiveness in predicting SMA behavior under diverse proportional and non-proportional loading conditions, including superelasticity and shape memory effects.

Keywords

shape memory alloys;SMAs;constitutive modeling;tension-compression asymmetry;asymmetric function;Auricchio-Petrini model;finite element simulations;superelasticity;shape memory effect;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FGG - Faculty of Civil and Geodetic Engineering
UDC: 620.174:539.3
COBISS: 232261891 Link will open in a new window
ISSN: 0997-7538
Views: 101
Downloads: 36
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Other data

Type (COBISS): Article
Pages: str. 1-21
Issue: ǂVol. ǂ112, [art. no.] 105650
Chronology: 2025
DOI: 10.1016/j.euromechsol.2025.105650
ID: 26202271