Development of a cloud-based digital twin simulation platform for flexible manufacturing systems with IoT technology

利用物联网技术开发基于云的柔性制造系统数字孪生仿真平台

• 批准号: 560996-2020
• 负责人: Yang, Sheng
• 金额: $ 1.46万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=723667
• 关键词: Development; cloud; based; digital; twin

Digital twin (DT) technology has received widespread attention in Canada's manufacturing sector. This technology creates a digital replica of a corresponding physical object, such as a human, device, system, or process that mirrors the actual process with full knowledge of its historical performance. It has the advantage of enabling agility and convergence of understanding to support more effective decision-making and resource allocation optimization. However, existing DT simulation platforms are either monolithic and function-oriented or dedicated in a captive software suite. Moreover, most digital twin platforms are standalone and only support offline simulation. Since DT is not a catch-all solution, educating a workforce on the new technology to see if it might be of value bears a significant risk for small-to-medium enterprises (SMEs). The COVID-19 pandemic has added an enormous challenge and disruption in current manufacturing plant operations due to strict quarantine rules and reduced human activities. There is an urgent demand for an online DT platform to monitor all the processes, capture real-time performance, and provide remote control. This project includes a partnership between the digital manufacturing team (Drs. Yang and Defersha) at the University of Guelph and Aleo Canada Inc. (Montreal, Quebec). The primary goal of this collaborative research is to develop a cloud-based DT platform that reliably captures the real-time performance of a flexible manufacturing system (i.e., a computerized and reconfigurable production unit to manufacture a variety of parts) with Internet of Things (IoT) sensor data. The expected impacts and outcomes of this project include: 1) develop the ability to conduct real-time monitoring and control of a flexible manufacturing system to support economic recovery; 2) develop the pipeline and infrastructures for cloud-based DT simulation with IoT technology; 3) provide a new testbed for AI algorithms and closed-loop control; and 4) provide training of HQP in digital transformation, IoT, Artificial Intelligence (AI), and cloud services. The expected outcomes of the proposed project will positively contribute to Canada's digital economy and post-pandemic recovery.

Digital Twin（DT）技术在加拿大的制造业中受到广泛关注。该技术创建了相应物理对象的数字复制品，例如人，设备，系统或过程，该物体，设备，系统或过程以完全了解其历史性能反映实际过程。它具有使敏捷性和理解的融合以支持更有效的决策和资源分配优化的优势。但是，现有的DT仿真平台在圈养软件套件中是单片的，并且以功能为导向或专用。此外，大多数数字双胞胎平台都是独立的，唯一的支持离线模拟。由于DT不是全能的解决方案，因此对新技术的劳动力进行教育，以查看它是否具有价值有显着的小型企业（中小企业）。由于严格的隔离规则和人类活动减少，COVID-19大流行在当前的制造工厂运营中增加了巨大的挑战和破坏。迫切需要在线DT平台监视所有流程，捕获实时性能并提供遥控器。该项目包括在圭尔夫大学和加拿大阿里奥大学（魁北克，魁北克）的数字制造团队（Yang和Defersha）之间的合作伙伴关系。这项协作研究的主要目标是开发一个基于云的DT平台，该平台可靠地捕获灵活的制造系统的实时性能（即，具有计算机化和可重新配置的生产单元，以使用物联网（Iot）传感器数据制造各种零件）。该项目的预期影响和结果包括：1）发展对灵活制造系统进行实时监控和控制以支持经济复苏的能力； 2）使用物联网技术开发用于基于云的DT模拟的管道和基础架构； 3）为AI算法和闭环控制提供新的测试床； 4）在数字化转型，物联网，人工智能（AI）和云服务方面提供HQP培训。拟议项目的预期结果将对加拿大的数字经济和大流行后的复苏做出积极贡献。