Variational phase-field modeling of fracture and fatigue in shape memory alloys
Alma Brambilla, Laura De Lorenzis, Lorenza Petrini
TL;DR
This work introduces a variational phase-field framework for fracture and fatigue in pseudoelastic shape memory alloys within a 1D setting, anchored to the Auricchio–Petrini constitutive model. A key feature is a transformation-strain limit $\varepsilon_L$ that causes damage to diffuse over a fully transformed region, delaying fracture and enabling realistic fatigue predictions under cyclic loading. The model couples phase transformation and damage through damage-dependent SMA parameters, yielding a gradient-damage energy functional and an energy-balance–driven evolution. Validation against Ni-Ti multi-wire experimental data shows good agreement in fatigue life predictions and the ability to discriminate safe from critical loading conditions, highlighting the framework’s potential for SMA fatigue assessment and design optimization.
Abstract
We propose a novel variational phase-field model for fracture and fatigue in pseudoelastic shape memory alloys (SMAs). The model, developed in a one-dimensional setting, builds upon the Auricchio-Petrini constitutive formulation for SMAs and couples damage evolution with phase transformation. We study analytically and numerically the homogeneous and localization responses of a bar under both monotonic and cyclic loading, and we investigate various macroscopic behaviors by tuning the constitutive parameters. A key feature of the model is the introduction of a transformation strain limit, beyond which the material is fully martensitic and behaves elastically. This leads to a distinctive behavior in which the region of localized damage widens, yielding a delay of fracture. The capability of the model to predict the fatigue performance is demonstrated by simulating the uniaxial response of Ni-Ti multi-wire samples under different loading conditions. The results show promising agreement with experimental fatigue life data, enabling the discrimination between safe and critical loading scenarios.
