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

制成品是一长串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，但是对于一个步骤，可能存在关于其贡献的更大背景的不确定性，如果在生产期间已知，则将允许更高的效率、更好的质量和提高的生产率。 此外，如果在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，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方面的进步为网络化制造提供了新的机遇。 这个未来制造研究资助（FMRG）网络制造项目连接和协调各种制造过程的整体，在网络协调的紧密系统下的不同制造阶段运行，由一个分析框架定义，称为STREAM，一个多阶段分布式未来制造系统。该项目提供了一个公共在线存储库，其中包含STREAM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业领域的专业人士创造了新的外展和劳动力发展活动。这些研究成果将被纳入先进制造，建筑设计，机器学习，仿真以及系统控制和优化的大学课程。本项目是一个跨学科的研究项目，有三个相互联系的研究任务：（1）STREAM体系结构的设计与实现。有一种新型的桥接中间件架构，以实现无缝的机器互操作性，并将建立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。提出了一种新的多层功能图模型来解决STREAM机器上的高维功能依赖问题，并提出了一种时空迭代学习控制方法来实现精确高效的过程质量控制。(3)STREAM模拟和生产控制。为了处理系统中共同满足过程质量、成本和资源约束的多级动态过程交互，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平台中得到了验证。建模和分析方法为未来多阶段和分布式制造系统的质量、可制造性和生产率的持续改进奠定了严格的基础。本项目由计算机和信息科学与工程局（CISE）计算机和网络系统处（CNS）和民用部门共同资助。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。

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.