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Adaptive Informatics for Intelligent Manufacturing (AI2M)

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:
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
项目状态:
已结题
起止时间:
2013 至 --

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项目成果

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中文摘要
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英文摘要
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).
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
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
期刊:
影响因子: --
作者: [Banwell G]
通讯作者: Banwell G
DOI: 10.1016/j.ress.2018.06.003
发表时间: 2018-10
期刊: Reliab. Eng. Syst. Saf.
影响因子: --
作者: [R. Yan;S. Dunnett;L. Jackson]
通讯作者: R. Yan;S. Dunnett;L. Jackson
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
DOI: 10.1016/j.compind.2018.11.001
发表时间: 2019-02
期刊: Comput. Ind.
影响因子: --
作者: [P. Goodall;R. Sharpe;A. West]
通讯作者: P. Goodall;R. Sharpe;A. West
7
    Embedded Integrated Intelligent Systems for Manufacturing
    • 批准号:
      EP/P027482/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $204.92万
    • 财政年份:
      2017
    • 负责人:
      Andrew West
    • 依托单位:
    CAREER: Probing the Extremes of Star Formation: A Census of Very Low-Mass Stars and Brown Dwarfs in the Local Neighborhood
    • 批准号:
      1255568
    • 项目类别:
      Continuing Grant
    • 资助金额:
      $78.36万
    • 财政年份:
      2013
    • 负责人:
      Andrew West
    • 依托单位:
    Intelligent embedded components for enhanced supply chain observability and traceability: INTELLICO
    • 批准号:
      EP/J501748/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $42.47万
    • 财政年份:
      2012
    • 负责人:
      Andrew West
    • 依托单位:
    Using White Dwarf-M Dwarf Pairs to Probe the Magnetic Activity and Angular Momentum Evolution of Low-Mass Stars
    • 批准号:
      1109273
    • 项目类别:
      Continuing Grant
    • 资助金额:
      $42.97万
    • 财政年份:
      2011
    • 负责人:
      Andrew West
    • 依托单位:
    海外基金