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Smart Manufacturing Advancement

Smart Manufacturing Advancement
智能制造进步
批准号:
1321470
负责人:
James Davis
金额:
$3.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-03-01 至 2016-02-29

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中文摘要
翻译
1321470(戴维斯)。智能制造(SM)集成了基于网络的数据和信息,包括对制造和供应链企业的所有方面的实时理解、推理、计划和管理。通过使用先进的基于传感器的数据分析、建模和实时仿真来促进SM。SM是关于改变游戏规则的潜力,以激励创新,解决生产力问题,实现新的和结构上不同的业绩目标,并推动工厂资产投资的竞争优势。遗憾的是,目前还不存在一种经济高效的基础设施,可以跨整个生产操作的控制系统集成实时制造智能和主动管理。这是智能制造领导者联盟(SMLC)的最终目标。SMLC是围绕着一系列目标而形成的,没有一家公司能够单独完成。SMLC使智能制造行业的利益相关者能够组成协作研发、实施和倡导团队,以开发促进制造智能广泛应用的方法、标准、平台和共享基础设施。SMLC是一个非营利性组织,致力于通过开发称为智能制造平台(SM Platform)的共享基础设施来克服SM系统开发和部署的障碍。SMLC活动是围绕行业和大学推动的共享基础设施的联合开发、应用和扩展而建立的,该基础设施将实现对整个经济的变革性影响、制造业创新和全球竞争力。SMLC通过追求一项任何一家公司都无法承担的综合技术来支持制造业。如果没有现代化的工业基础设施,采用SM系统在经济上是不可行的。零碎实施的过程控制和自动化系统将继续限制创新和能力。SMLC将在多个行业领域建立业务、互操作性和技术模型、演示、基础设施和项目团队。通过SM平台,SMLC将帮助公司应对汽车、食品、军事、材料、化工、石油和天然气、炼油、制药、信息技术、过程控制和自动化行业的制造相关挑战。SMLC还支持合作研究、开发和商业化。为大学、技校和中小型制造商开发智能制造教育和劳动力培训资源。
英文摘要
1321470 (Davis). Smart Manufacturing (SM) is integrating network-based data and information that comprises the real-time understanding, reasoning, planning and management of all aspects of a manufacturing and supply chain enterprise. SM is facilitated through use of advanced sensor-based data analytics, modeling and simulation in real-time. SM is about game-changing potential to energize innovation, address productivity, achieve new and structurally different performance goals, and drive the competitive advantage of investments in plant assets. Unfortunately, a cost effective infrastructure to integrate real-time manufacturing intelligence and active management across the control systems of an entire production operation does not exist today. This is the ultimate aim of the Smart Manufacturing Leadership Coalition (SMLC). SMLC has been formed around a set of goals that no one company can accomplish alone. SMLC enables stakeholders in the smart manufacturing industry to form collaborative R & D, implementation and advocacy teams for development of the approaches, standards, platforms and shared infrastructure that facilitate the broad utilization of manufacturing intelligence. The SMLC is a non-profit organization committed to overcome barriers to the development and deployment of SM Systems through development of a shared infrastructure called the Smart Manufacturing Platform (SM Platform). SMLC activities are built around joint industry and university-driven development, application and scaling of a shared infrastructure that will achieve transformational economic-wide impact, manufacturing innovation and global competitiveness. SMLC supports the manufacturing industry through pursuing a comprehensive technology that no one company can undertake. Without a modern industrial infrastructure, adoption of SM systems is not economically viable. Process control and automation systems implemented in a piecemeal fashion will continue to limit innovation and capability. SMLC will build the business, interoperability and technology models, demonstrations, infrastructure, and project teams across multiple industry segments. Through the SM Platform, SMLC will help companies address manufacturing-related challenges across the automotive, food, military, materials, chemical, oil and gas, refining, pharmaceutical, information technology, process control and automation industries. SMLC also supports collaborative research, development and commercialization. Education and workforce training resources for smart manufacturing will be developed for universities, technology schools, and small and medium manufacturers.
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会议论文
MICA: Stomasense: A New Route to the Proactive Detection and Management of Leaks within Ostomy Pouches
  • 批准号:
    MR/W029561/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $34.28万
  • 财政年份:
    2023
  • 负责人:
    James Davis
  • 依托单位:
Collaborative Research: SaTC: CORE: Small: Improving Sanitization and Avoiding Denial of Service Through Correct and Safe Regexes
  • 批准号:
    2135156
  • 项目类别:
    Standard Grant
  • 资助金额:
    $27.4万
  • 财政年份:
    2022
  • 负责人:
    James Davis
  • 依托单位:
Symposium on the Strategy for Resilient Manufacturing Ecosystems through AI
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