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

近几十年来，实验和计算方法的进步为许多工程和科学应用提供了丰富的数据。然而，这些数据并不容易转化为工程知识。该项目的目标是开发一种机器学习方法，以促进这种数据到知识的翻译，以更好地了解材料的完整性。这些知识有助于了解添加剂制造的部件中的机械行为，如疲劳。开发的方法将改进昂贵得令人望而却步的传统材料认证程序。该项目的结果可能会降低消费者成本并增加采用率，最终可能会推动美国经济。为了吸引后代并促进包容性，K-12年级的学生将在犹他州工程日和普渡太空日与用户友好的界面互动，实际演示学习自然法则。要提高将数据转化为知识的成功率和可靠性，需要将重点转移到可解释性和可解释性，以延续健全的科学和工程原则。为此，将利用基于遗传编程的符号回归(GPSR)对结构材料的疲劳损伤进行建模。然后，将使用从材料模拟、实验生成的数据集来训练GPSR模型，并在现有知识的指导下发现新的潜在机制，即知识。研究任务将解决一种易于处理的方法，将微观结构依赖的机制建模为疲劳寿命预测，并取代当前的做法。具体来说，GPSR模型将结合高能X射线衍射显微镜和晶体塑性有限元模拟数据进行训练。生成的GPSR模型将是对添加剂制造的金属中气孔诱导的、依赖于微结构的疲劳裂纹萌生的物理规则化多尺度均质化。该奖项反映了NSF的法定使命，并已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。

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.