Data-Driven Approaches for Digital Twins of Manufacturing Processes

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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.