Guest Editors:
Andrea Matta, Politecnico di Milano, Italy
George Huang, University of Hong Kong, Hong Kong
Paulo Leitao, Instituto Politecnico de Bragança, Portugal
Elisa Negri, Politecnico di Milano, Italy. Email: elisa.negri@polimi.it
Aims & Scope
Cyber-Physical Systems (CPS) are populating smart factories, concretizing the vision brought forward by the
Industry 4.0 transition. They are composed of a physical and a virtual side, interacting with each other. Digital
Twins (DT) are a core element in the virtual side of the manufacturing CPS, offering a synchronized mirroring
of a physical production asset or system to enable advanced decisional support. DT promise to bring benefits,
throughout the lifecycle to support the design, development, operations, maintenance, reconfiguration and
improvement of production systems. DT are discussed for the envisioned capabilities to monitor, predict and
optimize performance, considering field synchronization and smart and sustainable manufacturing needs. In
this perspective, many are the opportunities and challenges for research, and this Special Issue aims to unveil
the novelty brought by DT for a productive and sustainable production. Original contributions are expected
to address DT applications, modelling approaches, lifecycle methodologies, and technologies to support their
deployment in manufacturing contexts featuring different automation levels, including fully automated,
semi-manual and fully manual production systems, and autonomy levels ranging from shop floor to remote
manufacturing. Ethical aspects and requirements for trustworthiness may be considered to cover issues
related to the human-in-the-loop in CPS with DT. Finally, DT modeling may address CPS resilience and self-adaptation to face critical threats in manufacturing.
Overall, the Special Issue welcomes original contributions related, but not limited, to the following topics:
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Digital Twins frameworks and methodologies for manufacturing CPS
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Data models and advanced data analytics for manufacturing Digital Twins
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Artificial Intelligence (AI) and manufacturing Digital Twins
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Simulation-based Digital Twins in manufacturing
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Digital Twins for sustainable production
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Digital Twins for manufacturing productivity
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Digital Twins for reliability and safety of production assets or systems
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Digital Twins for energy-efficient manufacturing
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Digital Twins for production planning, scheduling and control
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Digital Twins for maintenance, repair, diagnostics, and prognostics
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Digital Twins and remote manufacturing
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Digital Twins and resilient manufacturing
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Digital Twins and Cyber Security
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Digital Twins in the engineering and development phases of CPS
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Digital Twins to support all phases of lifecycle management of production assets
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Digital Twins to support the integration of shop floor and product lifecycle management
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Digital Twins and Integration of humans-in-the-loop
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Trustworthy decision-making and Digital Twins
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Case studies and industrial applications of DT in manufacturing CPS
Subject Areas
CPS, Digital Twin, Manufacturing systems, Manufacturing processes, Operations management, Maintenance management, Internal Logistics, Modelling and simulation, Control and Optimization, Industry 4.0
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