Strain-Memory Effects on Solid-State Transformation

任何材料的力学反应都取决于其先前变形的历史。这种现象被称为“应变记忆”，它极大地影响了材料在特定时间点的反应。尽管应变记忆效应对材料的力学响应和寿命有很大的影响，但对其的研究却很少。对于具有相变特性的功能材料，如形状记忆合金（sma）尤其如此。该奖项支持基础研究，以深入了解固态相变，特别是马氏体相变如何受到sma应变记忆的影响。该项目的见解也将有助于理解其他结构合金，如钢，以及功能材料，如sma，通过提高机械性能预测的信心。这些改进的预测对成本节约和生命周期管理有直接影响。重要的是，该奖项资助的研究还包括旨在鼓励神经分化的中学生和高中生参与STEM的外展活动。患有“DYS”障碍（如阅读障碍）的学生将被鼓励从事STEM领域，在学习工具的支持下，他们可以脱颖而出。极端环境材料需要精确的性能（机械响应和寿命）预测能力，这只能通过考虑应变记忆效应来实现。该项目中的协同实验和建模方法将通过提供对变形历史如何影响热（驱动）和机械诱导（超弹性）固态转变的基本理解来填补这一知识空白。这将有助于建立一个独特的双表面晶体塑性模型，该模型将成为提高具有固态转化的先进材料在用热机械响应预测能力的基石。然而，尽管晶体塑性建模有许多优点，但全场模拟的计算成本很高，需要更快的计算技术，同时保持纹理敏感性。因此，图形神经网络（GNN）也将通过晶体塑性建模的全场模拟来开发和训练。因此，该项目将带来准确和快速的应变记忆效应预测，这将有助于长期工程解决暴露于复杂恳求和微观结构的固态转变材料。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.