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

材料的疲劳寿命是指它在断裂之前可以承受的载荷循环次数；例如，拉直回形针的疲劳寿命是它在断裂之前可以来回弯曲的次数。飞机、汽车和生物医学设备很容易发生这样的故障，但许多控制疲劳寿命的机制却鲜为人知。这种理解尤其局限于镍钛合金，这种合金被用于生物医学设备和火星探测车轮胎等各种应用。该奖项支持对最终导致镍钛材料材料失效的疲劳裂纹形成原因的研究，并将创造一种提高疲劳寿命的设计工具。这一结果将提高人工心脏瓣膜、支架和其他微创生物医疗设备的健壮性，从而减少患者创伤和医疗成本，从而使医疗保健领域受益。该项目还让研究生接触到高性能计算和阿贡国家实验室的高级光子源。在高级光子源获得的技能将使学生能够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，该项目将通过制作一段关于疲劳的视频来熟悉工程词汇，该视频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弹性地从大变形中恢复，因此是微创生物医学设备和其他应用(如火星漫游者轮胎)的理想选择。然而，由于存在微小缺陷，镍钛合金极易发生循环疲劳开裂。该项目建立了镍钛合金的晶体塑性模型，并利用X射线显微层析成像和高能衍射显微镜测量缺陷周围的裂纹形核。然后，将测量的缺陷几何形状与该模型相结合来预测测量的疲劳裂纹周围的塑性应变。塑性应变在疲劳裂纹形核中起着关键作用，但很难测量；因此，将使用晶体塑性模型。数据驱动程序将自动生成预测驱动裂纹形核的机械状态的疲劳指示器参数。疲劳指示器参数是材料抗疲劳计算设计中常用的工具；然而，当前的疲劳指示器参数存在不准确的问题，本项目致力于解决这一问题。该项目的成果是一个经过验证的疲劳指示器、基于参数的建模范例和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 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.