A framework driven by physics-guided machine learning for process-structure-property causal analytics in additive manufacturing
参考中译:物理引导的机器学习驱动的加法制造过程-结构-性能因果分析框架


          

刊名：Journal of Manufacturing Systems
作者：Hyunwoong Ko（School of Manufacturing Systems and Networks, Ira A. Fulton Schools of Engineering, Arizona State University）
Yan Lu（Engineering Laboratory, National Institute of Standards and Technology）
Zhuo Yang（Associate, Engineering Laboratory, National Institute of Standards and Technology）
Ndeye Y. Ndiaye（Associate, Engineering Laboratory, National Institute of Standards and Technology）
Paul Witherell（Engineering Laboratory, National Institute of Standards and Technology）
刊号：737C0076
ISSN：0278-6125
出版年：2023
年卷期：2023, vol.67
页码：213-228
总页数：16
分类号：TH165; TP2
关键词：Additive manufacturing；Data-Driven；Machine learning；Physics-Informed；Process-Structure-Property
参考中译：加性制造；数据驱动；机器学习；物理信息；过程-结构-特性
语种：eng
文摘：Data analytics with Machine Learning (ML) using physics knowledge and big data offers high potential to continuously transform raw data to newfound knowledge of Process-Structure-Property (PSP) causal relationships. In Additive Manufacturing (AM), however, realizing the potential is still limited largely due to the lack of a systematic way to learn the PSP relationships for various AM processes. To address the limitation, this paper proposes a novel framework driven by physics-guided ML, which consists of three tiers: (1) knowledge of predictive PSP models and physics, (2) PSP features of interest, and (3) raw AM data. The framework defines a PSP-learning process with two sub-processes. The first uses a knowledge-graph-guided top-down approach to generate the requirements for predictive analytics and data acquisition. The second uses a data-driven bottom-up approach to construct and model new PSP knowledge. Together, these processes connect the proposed framework to decision-making and control activities and physical and virtual AM systems, respectively. The paper includes a case study based on Laser Powder Bed Fusion processes including AM Metrology Testbed at the National Institute of Standards and Technology (NIST). The case study introduces predictive ML models and PSP knowledge extracted from the models. We also demonstrate the framework using an ML-Integrated Knowledge Extraction module called MIKE in NIST's collaborative AM Material Database. The framework newly enables a systematic physics-guided data-driven approach for PSP in AM that can couple physics knowledge with the versatility of data-driven ML models. Using the approach, the framework continuously updates the models (1) to improve the understanding of dynamically generated AM data and (2) to link sub-models into coupled PSP models. Based on the improved understanding, the framework also facilitates decision-making and control activities for AM at multiple scales.
参考中译：使用物理知识和大数据的机器学习(ML)的数据分析提供了将原始数据持续转换为新发现的过程-结构-属性(PSP)因果关系知识的高潜力。然而，在添加制造(AM)中，实现潜力仍然有限，这在很大程度上是因为缺乏一种系统的方法来学习各种AM过程的PSP关系。针对这一局限性，本文提出了一种由物理制导的ML驱动的新框架，该框架由三层组成：(1)预测PSP模型和物理知识，(2)感兴趣的PSP特征，(3)原始AM数据。该框架定义了一个PSP学习过程，包括两个子过程。第一种方法使用知识图谱引导的自上而下的方法来生成预测性分析和数据获取的需求。第二种方法使用数据驱动的自下而上的方法来构建和建模新的PSP知识。这些流程一起将建议的框架分别连接到决策和控制活动以及物理和虚拟AM系统。本文包括一个基于激光粉床融合过程的案例研究，其中包括美国国家标准与技术研究所(NIST)的AM计量试验台。案例研究介绍了预测最大似然模型和从模型中提取的PSP知识。我们还使用了美国国家标准研究院S协作型AM素材数据库中的ML集成知识提取模块Mike演示了该框架。该框架为AM中的PSP提供了一种系统的物理制导的数据驱动方法，可以将物理知识与数据驱动的ML模型的通用性结合起来。使用该方法，框架不断更新模型(1)以提高对动态生成的AM数据的理解，(2)将子模型链接到耦合的PSP模型。基于改进的理解，该框架还为AM在多个尺度上的决策和控制活动提供了便利。



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