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

FMSG：Cyber​​：使用基于云的平台量化 Inconel 718 可重复增材制造的工艺-结构-性能-表面关系的不确定性

• 批准号: 2328112
• 负责人: Bart Raeymaekers
• 金额: $ 50万
• 依托单位: Virginia Polytechnic Institute and State University
• 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: 2023-10-15 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328112&HistoricalAwards=false
• 关键词: FMSG; Cyber; Using; cloud; based

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.

这项未来的制造种子赠款（FMSG）项目支持对使用金属添加剂制造（AM）作为功能最终用途零件的生产过程所需的理论和实验基础的研究。 Metal AM是美国制造业的创新和竞争力的驱动力，因为它允许快速实施新的零件设计，以减少新产品的上市时间。但是，使用金属AM作为生产过程而不是原型工具，需要可靠，反复地制造具有几乎相同的结构，表面地形和特性的零件。因此，了解与过程结构 - 统计表面（PSP）关系相关的不确定性很重要。然而，PSP的研究既耗时又昂贵，因为许多标本需要得出有意义的信息，或者，由于访问/权限/权限和数据格式之间的不一致而汇总了不同研究的现有数据集以扩展和增强对PSP关系的见解。因此，这项研究专门旨在通过利用不确定性定量（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PSP的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获得数据和知识。通过与弗吉尼亚理工学院（VT），弗吉尼亚州立大学（VSU）的合作伙伴关系，与教育和劳动力发展（特别是代表性的群体）相结合的研究使研究成立了4年历史上的黑人学院/大学（HBCU）和英联邦高级制造中心（CCAM）（CCAM），弗吉尼亚州的公私伙伴关系。 Inconel 718的Fusion（L-PBF），并建立一个基于云的数据库，以在不同的研究人员之间汇总和共享PSP数据，以减少努力的重复，并通过数据共享加速PSP研究。为了实现这一目标，该研究旨在将UQ框架与ML算法结合起来，以得出数据驱动的模型，这些模型将L-PBF过程参数与量化微观结构和构建表面形象的指标相关联。这项研究产生的知识将（1）量化微观结构和根据取的表面形貌的不确定性，这是L-PBF过程参数的函数（正向UQ问题）； （2）确定获得微观结构和质量构建的表面地形（反UQ问题）的特定不确定性（或概率定义）所需的L-PBF过程参数； （3）在逆问题和反向问题解决方案及其不确定性作为L-PBF过程参数的函数的情况下得出操作图； （4）实现基于云的数据库来汇总可使用数字对象标识符引用的微观结构图像和表面地形图，并启用组合用户生成的数据集。该项目的结果将减少技术障碍，并刺激将金属AM作为功能最终用途零件的可行制造过程。这项未来的制造研究得到了计算机和信息科学与工程局的计算机和网络系统部（CISE/CNS）以及社会，行为和经济科学局的社会和经济科学局（SBE/SES）的支持。本奖反映了NSF的法定任务，并通过使用基础的智力效果和宽阔的范围来评估支持，并以评估的评估来表达珍贵。