FMRG: Cyber: Cyber-Coordinated Analytical Framework for Multi-stage Distributed Future Manufacturing Systems

FMRG：网络：多阶段分布式未来制造系统的网络协调分析框架

• 批准号: 2412020
• 负责人: Hongyue Sun
• 金额: $ 233.31万
• 依托单位: University of Georgia Research Foundation Inc
• 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: 2023-10-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2412020&HistoricalAwards=false
• 关键词: FMRG; Cyber; Coordinated; Analytical; Framework

A manufactured product is the end of a long chain of interwoven manufacturing steps that may span geography, industries, and manufacturing processes. Each step may be optimized, yet for a step there may be uncertainty about the larger context of its contribution that if known during production would allow for greater efficiencies, better quality, and improved productivity. If a global system analysis and optimization could, in addition, be conducted cooperatively with each step during production, the end product and overall production would greatly benefit. Advances in hybrid distributed control and optimization, in highly networked cyber-physical systems, and in artificial intelligence and machine learning have opened new opportunities in cyber-enabled manufacturing. This Future Manufacturing Research Grant (FMRG) CyberManufacturing project connects and coordinates the ensemble of various manufacturing processes, operating at different manufacturing stages under a cyber-coordinated close-knit system, defined by an analytical framework, known as STREAM, a multi-stage distributed future manufacturing system. This project provides a public online repository that hosts the STREAM data, models, simulators, controllers, analytics, and empirical studies, and that facilitates sharing research experiences about STREAM with the research and industry communities. The project creates new outreach and workforce development activities for K-12, undergraduate and graduate students, as well as working professionals in the field of manufacturing. These research results will be included in the university curriculum for advanced manufacturing, architecture design, machine learning, simulation, and system control and optimization. This project is an interdisciplinary research project with three interconnected research tasks: (1) STREAM architecture design and implementation. There is a novel bridging middleware architecture to enable seamless machine interoperability, and software- and hardware-based accelerators will be built to enable efficient communication and computing in cyber-manufacturing systems. (2) STREAM modeling and quality control. There is a novel multi-layer functional graphical model to address the high-dimensional functional dependencies over STREAM machines and a spatial-temporal iterative learning control method to achieve an accurate and efficient process quality control. (3) STREAM simulation and production control. To handle the multi-level dynamic process interactions in the system that collectively meet process quality, cost, and resource constraints, there are coordinated, high-fidelity multi-resolution simulators, assisting with the hybrid control of STREAM production. These research tasks are validated in an open-source additive manufacturing testbed for the manufacture of high energy/power density supercapacitors. The modeling and analysis methodologies form a rigorous basis for continuous improvement of quality, manufacturability, and productivity of future multi-stage and distributed manufacturing systems.This project is jointly funded by the Division of Computer and Network Systems (CNS) in the Directorate for Computer and Information Science and Engineering (CISE) and the Divisions of Civil. Mechanical and Manufacturing Innovation (CMMI) and Engineering Education and Centers (EEC) in the Directorate for Engineering (ENG).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.

制成品是一系列长链的交织制造步骤的终点，该步骤可能涵盖地理，行业和制造过程。每个步骤都可以优化，但是对于一个步骤，其贡献的更大背景可能会有不确定性，如果在生产过程中知道，则可以提高效率，更高的质量和提高的生产率。 另外，如果在生产过程中的每个步骤都可以合作进行全球系统分析和优化，则最终产品和整体生产将极大地有益。混合分布式控制和优化的进步，高度网络的网络物理系统以及人工智能和机器学习方面的进步为支持网络的制造业开辟了新的机会。 这项未来的制造研究赠款（FMRG）网络制造项目连接并协调各种制造工艺的合奏，这些过程在网络协调的封闭式系统下在不同的制造阶段运行，由分析框架（称为流）定义为多阶段的未来分布式制造系统。该项目提供了一个公共在线存储库，该存储库可容纳流数据，模型，模拟器，控制器，分析和实证研究，并促进与研究和行业社区共享有关流有关的研究经验。该项目为K-12，本科和研究生以及制造业领域的工作专业人员创建了新的外展和劳动力发展活动。这些研究结果将包括在大学高级制造，建筑设计，机器学习，模拟以及系统控制和优化的大学课程中。该项目是一个跨学科研究项目，具有三个互连的研究任务：（1）流架构设计和实施。有一个新颖的桥接中间件体系结构来启用无缝的机器互操作性，将构建基于软件和硬件的加速器，以在网络制造系统中启用有效的通信和计算。 （2）流建模和质量控制。有一个新型的多层功能图形模型，可以解决流机上的高维功能依赖性，以及一种时空迭代的学习控制方法，以实现准确有效的过程质量控制。 （3）流模拟和生产控制。为了处理系统中共同满足流程质量，成本和资源限制的多级动态过程交互，有协调的，高保真的多分辨率模拟器，有助于对流生产的混合控制。这些研究任务在开源添加剂测试台上进行了验证，以制造高能/功率密度超级电容器。建模和分析方法构成了持续提高未来多阶段和分布式制造系统质量，生产性和生产率的严格基础。该项目由计算机和网络系统（CNS）的计算机和信息科学和工程局（CISE）（CISE）（CISE）以及民事划分的计算机和网络系统（CNS）共同资助。机械和制造创新（CMMI）以及工程教育和中心（EEC）在工程局（ENG）（ENG）。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知识分子优点和更广泛影响的评估审查标准来通过评估来获得支持的。