A data -driven Automatic Model Generation Methodology of Digital Twin models for cost-effective adaptability and scalability in manufacturing systems

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This paper proposes a systematic methodology for the automatic generation of Digital Twin (DT) models in manufacturing systems with the purpose to support cost-effective adaptability and scalability. The methodology integrates multiple steps to ensure the efficient creation of DT models that accurately represent manufacturing physical systems. The methodology is tested in the Industry 4.0 Lab at the School of Management of Politecnico di Milano, showcasing its assumptions of modularity and reusability and the capability to support multiple reconfigurations of manufacturing systems. Future work will focus on rapid integration of services into DT models and the achievement of fully functional DT systems.