FMSG: Cyber: Using a cloud-based platform to quantify the uncertainty of the process-structure-property-surface relationship for repeatable additive manufacturing of Inconel 718

该未来制造种子基金（FMSG）项目支持研究使用金属增材制造（AM）作为功能性最终用途零件的生产工艺所需的理论和实验基础。Metal AM是美国制造业创新和竞争力的驱动力，因为它允许快速实施新零件设计，以缩短新产品的上市时间。然而，使用金属AM作为生产工艺而不是原型工具需要可靠且可重复地制造具有几乎相同的结构、表面形貌和特性的部件。因此，理解与过程-结构-性质-表面（PSPS）关系相关的不确定性是重要的。然而，PSPS研究是耗时和昂贵的，因为需要许多标本来获得有意义的信息，或者，聚合不同研究的现有数据集，以扩大和增强对PSPS关系的见解是不简单的，因为访问/许可和数据格式之间的不一致。因此，本研究旨在通过利用不确定性量化（UQ）框架和机器学习（ML）算法来分析微观结构和表面形貌图像，并量化PSPS关系来解决这些基本问题。此外，基于云的数据库将使其他研究人员可以使用数据和知识。通过与弗吉尼亚理工大学（VT）、弗吉尼亚州立大学（VSU）、一所4年制的历史黑人学院/大学（HBCU）和弗吉尼亚州的一个公私合作伙伴关系联邦先进制造中心（CCAM）的合作，研究与教育和劳动力发展相结合，特别是与代表性不足的群体相结合。量化Inconel 718激光粉末床熔合（L-PBF）PSPS关系的不确定性，并建立一个基于云的数据库，在不同的研究人员之间聚合和共享PSPS数据，以减少重复劳动，并通过数据共享加速PSPS研究。为了实现这一目标，该研究的目的是联合收割机的UQ框架与ML算法，以获得数据驱动的模型，相关的L-PBF工艺参数的度量，量化的微观结构和建成的表面形貌。从这项研究中得到的知识将（1）量化的微观结构和建成的表面形貌的L-PBF工艺参数的函数的不确定性（正向UQ问题）;（2）确定L-PBF工艺参数所需的具体不确定度微观结构和竣工表面形貌（或概率定义）（3）导出正问题和逆问题的解的操作映射及其作为L-PBF过程参数的函数的不确定性;（4）实现基于云的数据库，以聚集可以使用数字对象标识符引用的微观结构图像和表面形貌图，并且使得能够组合用户生成的数据集。该项目的成果将减少技术障碍，并促进采用金属增材制造作为功能性最终用途零件的可行制造工艺。（CISE/CNS）和社会，行为和经济科学局社会和经济科学司（SBE/SES）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Future Manufacturing Seed Grant (FMSG) project supports research into the theoretical and experimental foundation required to use metal additive manufacturing (AM) as a production process for functional end-use parts. Metal AM is a driver for innovation and competitiveness of United States manufacturing because it allows rapid implementation of new part designs to reduce the time-to-market of new products. However, using metal AM as a production process instead of a prototyping tool requires reliably and repeatably manufacturing parts with near-identical structure, surface topography, and properties. Consequently, understanding the uncertainty associated with the process-structure-property-surface (PSPS) relationship is important. Yet, PSPS research is time-consuming and costly because many specimens are required to derive meaningful information and, alternatively, aggregating existing datasets of different studies to expand and enhance insights about the PSPS relationship is not straightforward because of access/permissions and inconsistencies between data formats. Hence, this research specifically aims to address these fundamental problems by leveraging an uncertainty quantification (UQ) framework and machine learning (ML) algorithms to analyze microstructure and surface topography images, and quantify the PSPS relationship. Additionally, a cloud-based database will make the data and knowledge available to other researchers. Research integrates with education and workforce development, specifically underrepresented groups, through a partnership with Virginia Tech (VT), Virginia State University (VSU), a 4-year Historically Black College/University (HBCU), and the Commonwealth Center for Advanced Manufacturing (CCAM), a public-private partnership in Virginia.The research objective of this project is twofold: quantify the uncertainty of the PSPS relationship for laser powder bed fusion (L-PBF) of Inconel 718 and, establish a cloud-based database to aggregate and share PSPS data among different researchers, to reduce duplication of effort and accelerate PSPS research by data-sharing. To accomplish this objective, the research aims to combine a UQ framework with ML algorithms to derive data-driven models that relate L-PBF process parameters to metrics that quantify the microstructure and as-built surface topography. The knowledge resulting from this research will (1) quantify the uncertainty of the microstructure and as-built surface topography as a function of the L-PBF process parameters (forward UQ problem); (2) determine the L-PBF process parameters required to obtain specific uncertainty (or probability definition) of microstructure and as-built surface topography (inverse UQ problem); (3) derive an operating map of the solution of the forward and inverse problems and its uncertainty as a function of the L-PBF process parameters; (4) implement a cloud-based database to aggregate microstructure images and surface topography maps that can be cited using a digital object identifier, and enable combining user-generated datasets. The outcomes of this project will reduce technical barriers and spur adoption of metal AM as a viable manufacturing process for functional end-use parts.This Future Manufacturing research is supported by the Computer and Information Science and Engineering Directorate's Division of Computer and Network Systems (CISE/CNS) and the Social, Behavioral and Economic Sciences Directorate’s Division of Social and Economic Sciences (SBE/SES).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.