Data-Driven Approaches for Digital Twins of Manufacturing Processes

这项合作研究旨在推进制造过程的数字孪生，以优化工厂分布，增强制造能力，并实现更好的决策。数字孪生是一个物体或环境的数字再创造。数据收集、分析、模拟和其他数据驱动的功能可以帮助优化其物理对应项。计算研究可以通过机器学习和现代软件工程技术进行，其中利用数据来指导决策和控制。该项目旨在全面理解数字孪生范式中的工程任务，以及如何使用数据分析、机器学习和深度学习来完成这些任务。这些研究包括设计和部署i)基于机器学习的过程、设备和环境舒适度控制系统；Ii)能源和设备使用的数据驱动模拟；Iii)根据资产和环境的实时图像对环境变量（如温度和湿度）进行几何重构和映射的可视化；Iv)数据集成，在数字孪生的软件组件之间管理和交换数据；V)使用历史数据计算资产健康状况和剩余使用寿命的预测性维护；Vi)边缘雾方法，其中计算资源和存储以联合方式从本地处理器卸载到网络中的服务器，以提高响应时间。在这个合作研究项目中，加拿大团队将把他们的专业知识与波尔图大学的葡萄牙团队结合起来，共同推进数字孪生领域的知识和技术。这项研究将通过提供一种渐进和结构化的方法来实现数字孪生自动化，从而使智能工厂和先进制造业等多种加拿大行业在自动化过程中受益。拟议的研究还将提供巨大的HQP培训机会，并为加拿大工业提供制造过程的工程数字孪生专家，这一领域被认为在未来5到10年内需求量很大。

This collaborative research aims to advance the digital twins of manufacturing processes to optimize plant distribution, enhance manufacturing, and enable better decision-making. A digital twin is a digital re-creation of an object or environment. Data collection, analytics, simulation, and other data-driven capabilities can help optimize its physical counterpart. Computational studies can be performed with machine learning and modern software engineering techniques, where data are leveraged to guide decision-making and control. This project aims to comprehensively understand the engineering tasks within the digital twin paradigm and how to approach such tasks using data analytics, machine learning, and deep learning. Such studies include designing and deploying i) machine-learning-based control systems for processes, equipment, and environmental comfort; ii) data-driven simulation for energy and equipment usage; iii) visualization of geometric reconstruction and mapping of environment variables, such as temperature and humidity, from real-time images of assets and the environment; iv) data integration to manage and exchange data between software components of digital twins; v) predictive maintenance using historical data to calculate assets' health and remaining useful life; vi) edge-fog approaches, where computing resources and storage are offloaded from local processors to the servers in the network in a federated manner, to improve response time. In this collaborative research project, the Canadian team will combine its expertise with the Portuguese team at the University of Porto to advance knowledge and technology in the digital twin domain. This research will benefit a diverse Canadian industry, such as smart factories and advanced manufacturing, in their automation processes by providing a progressive and structured approach to realizing digital twin automation. The proposed research will also stage a tremendous HQP training opportunity and equip Canadian industries with experts in engineering digital twins for manufacturing processes, a sector believed to be in high demand in the next five to ten years.