Adaptive Informatics for Intelligent Manufacturing (AI2M)

智能制造自适应信息学 (AI2M)

• 批准号: EP/K014137/1
• 负责人: Andrew West
• 金额: $ 246.53万
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK014137%2F1
• 关键词: Adaptive; Informatics; Intelligent; Manufacturing; AI2M

The AI2M research cluster will bring together leading researchers and practitioners in high value manufacturing, information science, ICT, mathematical sciences and manufacturing services to address the needs for future globally competitive ICT-supported manufacturing practices and infrastructures. The cluster also leverages two distinct supply chains, automotive and aerospace and defence with associated ICT and manufacturing service providers. UK manufacturing has to migrate towards supplying innovative, high quality, variable volume solutions to a global market. Low wage competition and reduced profit margins increase the difficulty of recovering the costs of early lifecycle phases (specification, design, analysis and setup) especially for lower volume products. "Right first time" production is a necessity to survive. In the automotive domain the relatively high volume market is crippled by increased complexity, quality and customer demands for variety. The high added-value, low volume defence and aerospace domains are also under pressure from: the spectrum of product and process complexity; the harsh manufacturing and operational environments and severe safety and legislative requirements. The future of UK manufacturing depends on supply chains being able to: remove defects generated throughout manufacturing; formalise and share product and process knowledge; optimise strategy based on resource utilisation, traceability and lifecycle performance monitoring and understand the implications of design features on manufacturing and operational performance as well as the impact of new materials, components and legislation (e.g. End of Life Vehicle) and the impact of the adoption of new technologies and business models. To pay dividends both in supply chain efficiencies, compliance and new business models, companies must capture and analyse a larger range of data, faster, at lower cost and manage it better than ever before. The challenge of this project is therefore to develop an on-demand intelligent product lifecycle service system for increased yield for products and processes that can bridge the information gaps associated with inefficient supply chain integration and a lack of knowledge on product usage throughout lifecycles. Current commercial solutions are limited to "on-site" silos of information that are restricting UK manufacturing in terms of its ability to: optimise efficiency in materials, resource, energy utilisation; speed up innovation; improve the generation and exploitation of manufacturing intelligence; support supply chain collaboration throughout the product and process lifecycles, and enable new business models and technologies to be readily adopted (e.g. product service systems (PSS) supporting either product operation, usage or results oriented business models). The key research challenges to be addressed by this cluster include: Service Foundations (dynamically reconfigurable architectures, data and process integration and sematic enhanced service discovery); Service Composition (composability analyses, dynamic and adaptive processes, quality of service compositions, business driven compositions); Service Management and Monitoring (self: -configuring, -adapting, -healing, -optimising and -protecting and Service Design and Development engineering of business services, versioning and adaptivity, governance across supply chains).

AI2M研究集群将汇集高价值制造业、信息科学、信息通信技术、数学科学和制造服务领域的领先研究人员和从业人员，以解决未来具有全球竞争力的信息通信技术支持的制造业实践和基础设施的需求。该集群还利用了两个不同的供应链，即汽车、航空航天和国防，以及相关的ICT和制造服务提供商。英国制造业必须向全球市场提供创新、高质量、可变容量的解决方案。低工资竞争和降低的利润率增加了回收早期生命周期阶段（规格，设计，分析和设置）成本的难度，特别是对于小批量产品。“正确的第一次”生产是生存的必要条件。在汽车领域，由于复杂性、质量和客户对多样性的需求不断增加，相对高容量的市场受到了削弱。高附加值、低产量的国防和航空航天领域也面临着来自以下方面的压力：产品和工艺的复杂性；恶劣的生产和操作环境以及严格的安全和法规要求。英国制造业的未来取决于供应链是否能够：消除整个制造过程中产生的缺陷；使产品和工艺知识形式化并共享；基于资源利用、可追溯性和生命周期性能监控优化策略，了解设计特征对制造和运营性能的影响，以及新材料、组件和法规（例如报废车辆）的影响，以及采用新技术和商业模式的影响。为了在供应链效率、合规性和新商业模式方面获得回报，企业必须以更快的速度、更低的成本捕获和分析更大范围的数据，并比以往任何时候都更好地管理数据。因此，该项目的挑战是开发一个按需智能产品生命周期服务系统，以提高产品和流程的产量，从而弥合与低效的供应链集成和缺乏整个生命周期产品使用知识相关的信息差距。目前的商业解决方案仅限于“现场”信息孤岛，这限制了英国制造业在以下方面的能力：优化材料、资源和能源利用效率；加快创新；提高制造智能的生成和利用；在整个产品和过程生命周期中支持供应链协作，并使新的业务模型和技术易于采用（例如，支持产品操作、使用或面向结果的业务模型的产品服务系统（PSS））。该集群需要解决的关键研究挑战包括：服务基础（动态可重构架构、数据和流程集成以及语义增强的服务发现）；服务组合（可组合性分析、动态和自适应流程、服务组合质量、业务驱动组合）；服务管理和监控（自我：业务服务的配置、调整、修复、优化和保护以及服务设计和开发工程、版本控制和适应性、跨供应链的治理）。

项目成果

Evaluating the optimal location for embedded accelerometers using experimentally validated computer algorithms

使用经过实验验证的计算机算法评估嵌入式加速度计的最佳位置

• DOI: 10.1109/eptc.2014.7028395
• 发表时间: 2014
• 作者: Banwell G

Novel methodology for optimising the design, operation and maintenance of a multi-AGV system

• DOI: 10.1016/j.ress.2018.06.003
• 发表时间: 2018-10
• 期刊: Reliab. Eng. Syst. Saf.
• 作者: R. Yan;S. Dunnett;L. Jackson

Reliability of bilateral and shear components in a two-legged counter-movement jump

• DOI: 10.1177/1754337121995967
• 发表时间: 2021-02-21
• 期刊: PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART P-JOURNAL OF SPORTS ENGINEERING AND TECHNOLOGY
• 影响因子: 1.5
• 作者: Gordon, David;Hayward, Steven;West, Andrew
• 通讯作者: West, Andrew

A data-driven simulation to support remanufacturing operations

• DOI: 10.1016/j.compind.2018.11.001
• 发表时间: 2019-02
• 期刊: Comput. Ind.
• 作者: P. Goodall;R. Sharpe;A. West

A holistic approach to health and safety monitoring: Framework and technology perspective

• DOI: 10.1016/j.iot.2022.100606
• 发表时间: 2022-08
• 期刊: Internet Things
• 作者: S. Hayward;Katherine van Lopik;Andrew West