Elastic Manufacturing systems - a platform for dynamic, resilient and cost-effective manufacturing services

弹性制造系统 - 动态、弹性和经济高效的制造服务平台

• 批准号: EP/T024429/1
• 负责人: Svetan Ratchev
• 金额: $ 357.24万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT024429%2F1
• 关键词: Elastic; Manufacturing; systems; platform; dynamic

Society complexity and grand challenges, such as climate change, food security and aging population, grow faster than our capacity to engineer the next generation of manufacturing infrastructure, capable of delivering the products and services to address these challenges. The proposed programme aims to address this disparity by proposing a revolutionary new concept of 'Elastic Manufacturing Systems' which will allow future manufacturing operations to be delivered as a service based on dynamic resource requirements and provision, thus opening manufacturing to entirely different business and cost models. The Elastic Manufacturing Systems concept draws on analogous notions of the elastic/plastic behaviour of materials to allow methods for determining the extent of reversible scaling of manufacturing systems and ways to develop systems with a high degree of elasticity. The approach builds upon methods recently used in elastic computing resource allocation and draws on the principles of collective decision making, cognitive systems intelligence and networks of context-aware equipment and instrumentation. The result will be manufacturing systems able to deliver high quality products with variable volumes and demand profiles in a cost effective and predictable manner. We focus this work on specific highly regulated UK industrial sectors - aerospace, automotive and food - as these industries traditionally are limited in their ability to scale output quickly and cost effectively because of regulatory constraints.The research will follow a systematic approach outlined in to ensure an integrated programme of fundamental and transformative research supported by impact activities. The work will start with formulating application cases and scenarios to inform the core research developments. The generic models and methods developed will be instantiated, tested and verified using laboratory based testbeds and industrial pilots (S5). It is our intention that - within the framework of the work programme - the research is regularly reviewed, prioritised and and flexibly funded across the 4 years, guided by our Industrial Advisory Board.

社会复杂性和重大挑战，如气候变化、粮食安全和人口老龄化，增长速度超过了我们设计下一代制造基础设施的能力，这些基础设施能够提供产品和服务来应对这些挑战。该计划旨在通过提出一个革命性的“弹性制造系统”新概念来解决这一差距，该概念将允许未来的制造业务作为基于动态资源需求和供应的服务提供，从而将制造业开放给完全不同的业务和成本模型。弹性制造系统概念借鉴了材料的弹性/塑性行为的类似概念，以允许确定制造系统的可逆缩放程度的方法和开发具有高度弹性的系统的方法。该方法建立在最近使用的弹性计算资源分配的方法，并借鉴集体决策，认知系统智能和上下文感知设备和仪器的网络的原则。其结果将是制造系统能够以具有成本效益和可预测的方式提供具有可变数量和需求概况的高质量产品。我们将这项工作集中在特定的高度监管的英国工业部门-航空航天，汽车和食品-因为这些行业传统上是有限的能力，以规模产出迅速和成本效益，因为监管的限制。研究将遵循一个系统的方法概述，以确保基础和变革性研究的综合计划的影响活动的支持。这项工作将从制定应用案例和场景开始，为核心研究发展提供信息。开发的通用模型和方法将使用基于实验室的试验台和工业试点进行实例化、测试和验证（S5）。我们的目的是-在工作计划的框架内-在我们的工业咨询委员会的指导下，在4年内定期审查，优先考虑和灵活资助研究。

项目成果

Controlling Swarms toward Flocks and Mills

控制蜂群走向羊群和磨坊

• DOI: 10.1137/21m1404314
• 发表时间: 2022
• 期刊: SIAM Journal on Control and Optimization
• 影响因子: 2.2
• 作者: Carrillo J

Gradient-augmented Supervised Learning of Optimal Feedback Laws Using State-Dependent Riccati Equations

使用状态相关 Riccati 方程的最优反馈定律的梯度增强监督学习

• DOI: 10.1109/lcsys.2021.3086697
• 发表时间: 2022
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Albi G

Collisionless and Decentralized Formation Control for Strings

字符串的无碰撞和分散编队控制

• DOI: 10.48550/arxiv.2102.13621
• 发表时间: 2021
• 作者: Choi Y

Moment-Driven Predictive Control of Mean-Field Collective Dynamics

• DOI: 10.1137/21m1391559
• 发表时间: 2021-01
• 期刊: SIAM J. Control. Optim.
• 作者: G. Albi;M. Herty;D. Kalise;C. Segala

The Effect of Robot Adoption on Profit Margins

• DOI: 10.1109/tem.2023.3260734
• 发表时间: 2024
• 期刊: IEEE Transactions on Engineering Management
• 影响因子: 5.8
• 作者: Y. Chen;C. Velu;D. McFarlane