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<p>The paper presents an innovative methodology, development and implementation of a Digital Twin prototype based on a 5 Dimensional Digital Twin architecture combined with a Service-Oriented Architecture and the <i>Industry 4.0 Component</i> concept. The proposed implementation method separates <i>the business logic (Digital Twin Brain)</i>, which enables the Digital Twin to perform its new <i>capabilities</i>, from the <i>domain logic</i>, which provides the <i>specific functionalities</i> of the Digital Twin. The specific functionalities are provided and implemented by the Asset Administration Shell Industry 4.0 Components available as stateless services, which allowed the creation of a base of shared technical functionalities among multiple Digital Twins. The methodology and architecture were validated using real data from an industrial packaging line, where the Digital Twin leverages <i>machine learning-based</i> algorithms to improve efficiency and reduce production downtime. The obtained results confirm the flexibility and scalability of the proposed architecture, which allows not only predictive maintenance but also seamless integration of additional domain functions. The proposed methodology contributes to the efficient deployment of Digital Twins in multidisciplinary environments and lays the foundation for a wide range of applications in the context of Industry 5.0.</p>

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Design and validation of a flexible framework for developing generic digital twins for Industry 4.0

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月24日

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  • Rudolf Pribiš,
  • Peter Drahoš,
  • Štefan Kozák,
  • Lukáš Beňo,
  • Erik Kučera,
  • Oto Haffner,
  • Martin Pajpach

摘要

The paper presents an innovative methodology, development and implementation of a Digital Twin prototype based on a 5 Dimensional Digital Twin architecture combined with a Service-Oriented Architecture and the Industry 4.0 Component concept. The proposed implementation method separates the business logic (Digital Twin Brain), which enables the Digital Twin to perform its new capabilities, from the domain logic, which provides the specific functionalities of the Digital Twin. The specific functionalities are provided and implemented by the Asset Administration Shell Industry 4.0 Components available as stateless services, which allowed the creation of a base of shared technical functionalities among multiple Digital Twins. The methodology and architecture were validated using real data from an industrial packaging line, where the Digital Twin leverages machine learning-based algorithms to improve efficiency and reduce production downtime. The obtained results confirm the flexibility and scalability of the proposed architecture, which allows not only predictive maintenance but also seamless integration of additional domain functions. The proposed methodology contributes to the efficient deployment of Digital Twins in multidisciplinary environments and lays the foundation for a wide range of applications in the context of Industry 5.0.