Strain-Memory Effects on Solid-State Transformation

The mechanical response of any material depends on its history of prior deformation. This phenomenon, called “strain memory”, greatly affects how materials respond at a certain point in time. Despite its large implications on the mechanical response and lifetime of materials, studies on strain-memory effects are scarce. This is especially true for functional materials that have phase-transforming properties such as shape memory alloys (SMAs). This award supports fundamental research to gain insights into how solid-state transformation, and martensitic transformation in particular, is affected by strain memory in SMAs. Insights from this project will also benefit the understanding of other structural alloys, like steel, in addition to functional materials, like SMAs, by bringing confidence in mechanical performance predictions. These improved predictions have a direct effect on cost savings and life-cycle management. Importantly, research funded by this award also includes outreach activities designed to encourage the participation of neuro-divergent middle and high school students in STEM. Students with “DYS” disorders such as dyslexia will be encouraged to engage in STEM fields where they can excel with the support of learning tools. Extreme-environment materials require accurate performance (mechanical response and lifetime) predicting capabilities that will only be achieved by accounting for strain-memory effects. The synergistic experimental and modeling approach in this project will fill this knowledge gap by providing a fundamental understanding on how thermally- (actuation) and mechanically-induced (superelasticity) solid-state transformations are affected by the history of deformations. This will help formulate a unique two-surface crystal-plasticity model that will be at the cornerstone of improved predictive capabilities for the in-service thermo-mechanical responses of advanced materials with solid-state transformations. However, though crystal-plasticity modeling has many advantages, full-field simulations are computationally expensive and call for faster computational techniques while maintaining texture sensitivity. Hence, a graph neural network (GNN) will also be developed and trained with full-field simulations from crystal-plasticity modeling. The project will, therefore, bring forth accurate and fast predictions of strain-memory effects that will contribute to a long-term engineering solution for materials with solid-state transformation exposed to complex solicitations and microstructures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.