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.

制成品是一长串相互交织的制造步骤的末端，这些步骤可能跨越地理、行业和制造过程。每个步骤都可以被优化，但对于一个步骤来说，其贡献的更大背景可能存在不确定性，如果在生产过程中已知，将允许更高的效率、更好的质量和提高的生产率。 此外，如果生产过程中的每个步骤都能协同进行全局系统分析和优化，那么最终产品和整体生产将大大受益。混合分布式控制和优化、高度网络化的网络物理系统以及人工智能和机器学习的进步为网络化制造带来了新的机遇。 这个未来制造研究补助金 (FMRG) 网络制造项目连接和协调各种制造流程的整体，在网络协调的紧密结合的系统下在不同的制造阶段运行，该系统由称为 STREAM 的分析框架定义，STREAM 是一个多阶段分布式未来的制造系统。该项目提供了一个公共在线存储库，其中托管 STREAM 数据、模型、模拟器、控制器、分析和实证研究，并有助于与研究和行业社区分享有关 STREAM 的研究经验。该项目为 K-12、本科生和研究生以及制造领域的专业人士创建了新的外展和劳动力发展活动。这些研究成果将纳入大学先进制造、架构设计、机器学习、仿真以及系统控制和优化课程。该项目是一个跨学科的研究项目，具有三个相互关联的研究任务：（1）STREAM架构设计和实现。有一种新颖的桥接中间件架构可以实现无缝机器互操作性，并且将构建基于软件和硬件的加速器以实现网络制造系统中的高效通信和计算。 (2)STREAM建模和质量控制。有一种新颖的多层功能图模型来解决STREAM机器上的高维功能依赖性，并有一种时空迭代学习控制方法来实现准确高效的过程质量控制。 (3)STREAM模拟和生产控制。为了处理系统中的多级动态过程交互，共同满足过程质量、成本和资源限制，有协调的、高保真多分辨率模拟器，协助 STREAM 生产的混合控制。这些研究任务在用于制造高能量/功率密度超级电容器的开源增材制造测试台中得到验证。建模和分析方法为持续改进未来多阶段和分布式制造系统的质量、可制造性和生产率奠定了严格的基础。该项目由计算机理事会计算机和网络系统 (CNS) 部门共同资助和信息科学与工程（CISE）以及土木部门。工程理事会 (ENG) 的机械和制造创新 (CMMI) 以及工程教育和中心 (EEC)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。