CAREER: Using Physics-Based Machine Learning to Reconcile the Crack Tip with the Plastic Zone during Fracture of Metals

职业：使用基于物理的机器学习来协调金属断裂过程中的裂纹尖端与塑性区

• 批准号: 2237039
• 负责人: Ryan Sills
• 金额: $ 62.22万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237039&HistoricalAwards=false
• 关键词: CAREER; Using; Physics; Based; Machine

Metal fracture occurs when cracks or other flaws grow, leading to failure of metallic structures such as buildings and aircraft that has been estimated to cost 4 percent of US gross domestic product. Current theories and models used to predict fracture are based on an incomplete picture of the fracture process, which lead to inaccurate predictions of crack growth, making it difficult to design engineering structures and next-generation structural metals. This Faculty Early Career Development (CAREER) award supports research to develop better material models used in the design of fracture-resistant engineering structures relevant to energy, defense, aerospace, and transportation applications, thereby supporting the U.S. economy and defense. The machine learning methods developed under this project will be broadly applicable to many fields of science and engineering. Through a collaboration with local and national science teaching organizations, the research findings from this project will be used to develop a multi-day lesson-plan for the high school science classroom wherein students learn about fracture of metals using computer simulations. This lesson-plan will be made available to science teachers nationwide. Prevailing theories of metal fracture focus on either the crack tip where dislocation nucleation governs the propensity for brittle fracture, or the plastic zone surrounding the crack tip where plastic dissipation governs the fracture toughness. Recent work has shown, however, that there are important interplays between the crack tip and plastic zone which affect various fracture behaviors such as dislocation multiplication and growth of fatigue cracks. This project is to develop a three-dimensional discrete dislocation dynamics model which simultaneously accounts for the crack tip and the plastic zone by capturing all relevant dislocation and bond-breaking processes in one unified model. To achieve this goal, a physics-informed machine learning-based image solver will be developed which utilizes a new convolutional architecture that couples to the finite element method. Atomistic simulations will be used to quantify the dislocation nucleation rate near the crack tip, grain boundary weakening due to dislocation adsorption and/or emission, and grain boundary decohesion. Predictions from the fracture model will be compared with state-of-the-art fracture experiments. Insights gained from this research will directly inform fracture models used in engineering design, prediction of embrittlement, and alloy design.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.

当裂纹或其他缺陷扩大时，就会发生金属断裂，导致建筑物和飞机等金属结构的失效，据估计，造成的损失占美国国内生产总值的 4%。目前用于预测断裂的理论和模型基于断裂过程的不完整图像，这导致裂纹扩展的预测不准确，从而难以设计工程结构和下一代结构金属。该学院早期职业发展（CAREER）奖支持研究开发更好的材料模型，用于设计与能源、国防、航空航天和运输应用相关的抗断裂工程结构，从而支持美国经济和国防。该项目开发的机器学习方法将广泛适用于科学和工程的许多领域。通过与地方和国家科学教学组织的合作，该项目的研究成果将用于为高中科学课堂制定为期多天的课程计划，学生可以通过计算机模拟了解金属断裂。该课程计划将向全国科学教师提供。 流行的金属断裂理论要么关注裂纹尖端，其中位错成核控制脆性断裂的倾向，要么关注裂纹尖端周围的塑性区，其中塑性耗散控制断裂韧性。然而，最近的工作表明，裂纹尖端和塑性区之间存在重要的相互作用，影响各种断裂行为，例如位错倍增和疲劳裂纹的生长。该项目旨在开发一种三维离散位错动力学模型，通过在一个统一模型中捕获所有相关位错和键断裂过程，同时考虑裂纹尖端和塑性区。为了实现这一目标，将开发一种基于物理信息的机器学习图像求解器，该求解器利用与有限元方法耦合的新卷积架构。原子模拟将用于量化裂纹尖端附近的位错成核率、位错吸附和/或发射导致的晶界弱化以及晶界脱聚。断裂模型的预测将与最先进的断裂实验进行比较。从这项研究中获得的见解将直接为工程设计、脆化预测和合金设计中使用的断裂模型提供信息。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。