Strain-Memory Effects on Solid-State Transformation

任何材料的力学响应都取决于其先前变形的历史。这种被称为“应变记忆”的现象，极大地影响了材料在某个时间点的反应。尽管它对材料的机械响应和寿命有很大的影响，但关于应变记忆效应的研究很少。对于形状记忆合金(SMA)等具有相变特性的功能材料尤其如此。该奖项支持基础研究，以深入了解SMA中的应变记忆如何影响固态转变，特别是马氏体相变。来自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，通过带来对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发散的初中生和高中生参与STEM的外联活动。有阅读障碍等DYS障碍的学生将被鼓励在学习工具的支持下从事他们能够出类拔萃的STEM领域。极端环境材料需要准确的性能(机械反应和寿命)预测能力，这只有通过考虑应变记忆效应才能实现。本项目中的协同实验和建模方法将通过提供关于热(驱动)和机械诱导(超弹性)固态转变如何受变形历史影响的基本理解来填补这一知识空白。这将有助于形成一个独特的双表面晶体塑性模型，该模型将作为改进对具有固态转变的先进材料在使用中的热机械响应的预测能力的基石。然而，尽管晶体塑性模拟有许多优点，但全场模拟的计算成本很高，需要更快的计算技术，同时保持纹理的敏感性。因此，还将开发一个图形神经网络(GNN)，并用晶体塑性建模的全场模拟进行训练。因此，该项目将带来对应变记忆效应的准确和快速的预测，这将有助于对暴露在复杂招标和微结构中的固态转变材料的长期工程解决方案。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.