Determining the Driving Force for Fatigue Crack Nucleation in a Superelastic Nickel Titanium Shape Memory Alloy

材料的疲劳寿命是它在断裂前可以承受的载荷循环次数;例如，拉直的回形针的疲劳寿命是它在断裂前可以来回弯曲的次数。飞机、汽车和生物医学设备很容易出现这种故障，但许多控制疲劳寿命的机制却知之甚少。这种理解特别局限于镍钛合金，它被用于生物医学设备和火星探测器轮胎等各种应用。该奖项支持对疲劳裂纹形成原因的研究，最终导致镍钛材料的材料失效，并将创建一个提高疲劳寿命的设计工具。该结果将通过增加人工心脏瓣膜、支架和其他微创生物医学设备的鲁棒性而使医疗保健领域受益，从而降低患者创伤和医疗保健成本。该项目还使研究生接触到高性能计算和阿贡国家实验室的先进光子源。在高级光子源获得的技能将定位学生为国家能源和国防需求做出贡献。高性能计算培训将扩大专注于大数据、机器学习和人工智能的劳动力队伍。此外，该项目还将通过制作一个关于疲劳的视频，让学生熟悉工程词汇，旨在减少第一代工程师的障碍，并改善工程教育。超弹性镍钛合金可以从大变形中弹性恢复，因此是微创生物医学设备和其他应用（如火星探测器轮胎）的理想选择。然而，由于微尺度缺陷，镍钛合金易于发生循环疲劳裂纹。本计画建立镍钛合金的晶体塑性模型，并利用X射线断层摄影术及高能绕射显微镜量测缺陷周围的裂纹形核。然后将测得的缺陷几何形状与模型相结合，以预测测得的疲劳裂纹周围的塑性应变。塑性应变在疲劳裂纹成核中起关键作用，但难以测量;因此，将使用晶体塑性模型。数据驱动程序将自动生成疲劳指标参数，预测驱动裂纹成核的机械状态。疲劳指标参数是一种常用的抗疲劳材料的计算设计工具，然而，目前的疲劳指标参数遭受不准确，这一项目地址。该项目的成果是一个经过验证的基于疲劳指标参数的建模范例和一个用于开发抗疲劳超弹性镍钛材料的变革性设计工具。该奖项反映了NSF的法定使命，并被认为值得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来支持。

The fatigue life of a material is the number of load cycles it can withstand before breaking; for example, the fatigue life of a straightened paperclip is the number of times it can be bent back-and-forth before it breaks. Aircrafts, automobiles, and biomedical devices are prone to such failures, yet many of the mechanisms that govern fatigue life are poorly understood. This understanding is especially limited for Nickel-Titanium alloys, which are used in such varied applications as biomedical devices and Mars rover tires. This award supports research into the cause of fatigue crack formation which eventually leads to material failure in a Nickel-Titanium material and will create a design tool for enhancing fatigue life. The result will benefit the healthcare field by increasing the robustness of artificial heart valves, stents, and other minimally invasive biomedical devices, therefore decreasing patient trauma and healthcare costs. This project also exposes graduate students to high-performance computing and Argonne National Laboratory's Advanced Photon Source. Skills gained at the Advanced Photon Source will position the student to contribute to national energy and defense needs. High-performance computing training will expand a workforce focused on big-data, machine learning, and artificial intelligence. Additionally, this project will familiarize students with engineering vocabulary by producing a video about fatigue that is intended to reduce barriers for first generation engineers and improve engineering education.Superelastic Nickel-Titanium elastically recovers from large deformations and thus is ideal for minimally invasive biomedical devices and other applications such as Mars rover tires. However, due to microscale defects, Nickel-Titanium is prone to cyclic fatigue cracking. This project builds a crystal plasticity model of Nickel-Titanium and measures crack nucleation around a defect using X-ray micro-tomography and high energy diffraction microscopy. The measured defect geometry is then combined with the model to predict the plastic strain around the measured fatigue crack. Plastic strain plays a key role in fatigue crack nucleation but is difficult to measure; thus, a crystal plasticity model will be used. A data-driven procedure will automate the generation of a fatigue indicator parameter that predicts the mechanical state driving crack nucleation. Fatigue indicator parameters are a commonly proposed tool in the computational design of materials for fatigue resistance; however, current fatigue indicator parameters suffer from inaccuracies, which this project addresses. The projects outcomes are a validated fatigue indicator parameter-based modeling paradigm and a transformative design tool for the development of fatigue resistant superelastic Nickel-Titanium materials.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.