CDS&E/Collaborative Research: Interpretable Machine Learning for Microstructure-Sensitive Fatigue Crack Initiation from Defects in Additive Manufactured Components

CDS

• 批准号: 2152938
• 负责人: Michael Sangid
• 金额: $ 29.74万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152938&HistoricalAwards=false
• 关键词: CDS& Collaborative; Research; Interpretable; Machine

Advancements in experimental and computational methods in recent decades have enabled production of a wealth of data for many engineering and science applications. However, these data do not readily translate into engineering knowledge. The objective of this project is to develop a machine-learning approach to facilitate this data-to-knowledge translation to better understand materials integrity. Such knowledge can help understand mechanical behaviors, such as fatigue, in additive manufactured components. The developed approach will improve the conventional process of materials certification, which is prohibitively expensive. The outcome of this project could potentially reduce consumer costs and increase adoption, which may ultimately advance the U.S. economy. To engage future generations and promote inclusion, K-12 students will interact with a user-friendly interface for hands-on demonstration of learning natural laws at the Utah Engineering Day and Purdue Space Day.Increasing the success and reliability of translating data into knowledge requires a shifted focus toward explainability and interpretability to perpetuate sound science and engineering principles. To this end, Genetic Programming based Symbolic Regression (GPSR) will be utilized to model fatigue damage in structural materials. GPSR models will then be trained using generated data sets from materials simulations, experiments, and guided by existing knowledge to discover new underlying mechanisms, i.e., knowledge. The research tasks will address a tractable means to model microstructure-dependent mechanisms into fatigue life predictions and supplant current practices. Specifically, GPSR models will be trained on a combination of high-energy X-ray diffraction microscopy and crystal plasticity finite element simulation data. The generated GPSR models will be a physics-regularized multiscale homogenization of pore-induced, microstructure-dependent fatigue crack initiation in an additive manufactured metal.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.

近几十年来，实验和计算方法的进步使许多工程和科学应用都可以生产大量数据。但是，这些数据不容易转化为工程知识。该项目的目的是开发一种机器学习方法来促进这种数据对知识翻译，以更好地了解材料的完整性。这些知识可以帮助理解加成型制成部分中的机械行为，例如疲劳。 开发的方法将改善材料认证的传统过程，这非常昂贵。该项目的结果可能有可能降低消费者成本并提高采用率，这最终可能会推动美国经济。为了吸引子孙后代并促进包容性，K-12学生将与用户友好的界面进行互动，以动手演示在犹他州工程日和普渡大学太空日学习自然法律。将数据转化为知识的成功和可靠性需要转移的焦点转移到解释性和解释性方面，以使声音科学和工程学的原理具有解释性。为此，将利用基于遗传编程的符号回归（GPSR）来模拟结构材料中的疲劳损伤。然后，将使用来自材料模拟，实验的生成数据集对GPSR模型进行培训，并以现有知识为指导，以发现新的潜在机制，即知识。研究任务将解决一种可拖动的手段，将微观结构依赖性机制建模为疲劳生活预测并取代当前实践。 具体而言，GPSR模型将通过高能X射线衍射显微镜和晶体可塑性有限元仿真数据的组合进行训练。生成的GPSR模型将是一个物理规范化的多尺度均质化，该杂质诱导的，微结构依赖的疲劳裂纹在增材制成的金属中启动。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智力和更广泛影响的评估来通过评估来获得支持的。