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.

数字孪生（DT）技术在加拿大制造业受到广泛关注。该技术创建相应物理对象的数字复制品，例如人，设备，系统或过程，反映实际过程，并充分了解其历史性能。它的优势在于能够实现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。然而，现有的DT仿真平台要么是单片和面向功能的，要么是专用于一个专属软件套件。此外，大多数数字孪生平台都是独立的，只支持离线模拟。由于DT不是一个包罗万象的解决方案，因此对劳动力进行新技术教育，看看它是否有价值，这对中小型企业（SME）来说是一个重大风险。由于严格的检疫规定及人类活动减少，COVID-19大流行为现有制造工厂的营运带来巨大挑战及干扰。迫切需要一个在线DT平台来监控所有过程，捕获实时性能并提供远程控制。该项目包括圭尔夫大学的数字化制造团队（Yang博士和Defersha博士）与Aleo Canada Inc.之间的合作。（蒙特利尔，魁北克）。这项合作研究的主要目标是开发一个基于云的DT平台，该平台可以可靠地捕获柔性制造系统的实时性能（即，用于制造各种部件的计算机化和可重新配置的生产单元）与物联网（IoT）传感器数据。该项目的预期影响和成果包括：1）开发对柔性制造系统进行实时监测和控制的能力，以支持经济复苏; 2）开发基于物联网技术的基于云的DT模拟的管道和基础设施; 3）为AI算法和闭环控制提供新的测试平台; 4）为HQP提供数字化转型、物联网、人工智能和云服务方面的培训。拟议项目的预期成果将为加拿大的数字经济和疫情后复苏做出积极贡献。