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

确定超弹性镍钛形状记忆合金疲劳裂纹形核的驱动力

• 批准号: 1934753
• 负责人: John Moore
• 金额: $ 44.8万
• 依托单位: Marquette University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934753&HistoricalAwards=false
• 关键词: Determining; Driving; Force; Fatigue; Crack; Determining; Driving; Force; Fatigue; Crack

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.

材料的疲劳寿命是它可以在破裂之前承受的负载周期的数量。例如，拉直的回形针的疲劳寿命是它可以在破裂之前来回弯曲的次数。飞机，汽车和生物医学设备容易出现这种故障，但是许多控制疲劳生活的机制却鲜为人知。这种理解尤其限制在镍 - 丁烷合金中，这些镍合金用于生物医学设备和火星漫游轮胎等多种应用。该奖项支持研究疲劳裂纹形成原因的研究，最终导致镍钛材料中的物质故障，并将创建一种设计工具，以增强疲劳寿命。结果将通过增加人造心脏瓣膜，支架和其他微创生物医学设备的鲁棒性来使医疗保健领域受益，从而降低患者创伤和医疗费用。该项目还使研究生接触高性能计算和Argonne National Laboratory的高级光子来源。在高级光子源获得的技能将使学生定位为国家能源和国防需求做出贡献。高性能计算培训将扩大针对大数据，机器学习和人工智能的劳动力。此外，该项目将通过制作有关疲劳的视频来熟悉工程词汇量，该视频旨在减少第一代工程师的障碍并改善工程教育。SuperelasticNickel-Titanium弹性从较大的变形中弹性恢复，因此非常适合微创生物医学设备和其他应用程序，例如Mars Rover Tires。然而，由于显微镜缺陷，镍钛容易容易循环疲劳裂纹。该项目构建了镍 - titanium的晶体可塑性模型，并使用X射线微谱学和高能量衍射显微镜在缺陷周围测量裂纹成核。然后将测得的缺陷几何形状与模型结合，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，将使用晶体可塑性模型。数据驱动的过程将自动化疲劳指标参数的生成，以预测机械状态驱动裂纹成核。疲劳指标参数是疲劳抗性材料的计算设计中通常提出的工具；但是，当前的疲劳指标参数遭受了不准确的措施，该项目解决了这些参数。这些项目结果是经过验证的疲劳指标基于参数的建模范式和一种用于开发抗疲劳性抗疲劳性超弹性超弹性镍tit材料的变革性设计工具。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来支持的。