Publicação
An agent-based approach for implementing asset administration shell type 3
| Resumo: | Industry 4.0 (I4.0) is driving the digitization of traditional manufacturing systems toward intelligent and adaptable factories, where industrial assets are interconnected and capable of collaborating inside and cross-company to enhance production processes. A key enabler of this transition is the Asset Administration Shell (AAS), a standardized digital representation of an asset that encapsulates all asset-relevant information throughout its lifecycle. The AAS ensures that all assets, regardless of their type or function, can be seamlessly integrated into the I4.0, facilitating interoperability and efficient data exchange across the entire production network. However, traditional AAS solutions, namely AAS Types 1 and 2, cannot address all the requirements of modern industry environments. To overcome these limitations, the AAS Type 3 (proactive) extends beyond the conventional functionalities of traditional AAS by incorporating more sophisticated features, enabling I4.0 components to operate with greater autonomy, intelligence, and collaborative capabilities. While traditional AAS implementations (Types 1 and 2) have been widely studied and adopted, research on AAS Type 3 is still in its early stages, lacking formal specifications and standardized guidelines for its design and implementation. In this context, this article presents an agent-based AAS approach for implementing AAS Type 3, offering readers a clearer, more accessible description of its features and practical applicability while encouraging further research and development in this emerging topic. For this purpose, several experiments were performed to gain a better understanding of its application in industrial scenarios. Additionally, the article discusses the experiences, insights, limitations and future perspectives derived from the design and experimental implementation of the agent-based AAS approach. |
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| Autores principais: | Sakurada, Lucas |
| Outros Autores: | De la Prieta, Fernando; Leitão, Paulo |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | Industry 4.0 (I4.0) is driving the digitization of traditional manufacturing systems toward intelligent and adaptable factories, where industrial assets are interconnected and capable of collaborating inside and cross-company to enhance production processes. A key enabler of this transition is the Asset Administration Shell (AAS), a standardized digital representation of an asset that encapsulates all asset-relevant information throughout its lifecycle. The AAS ensures that all assets, regardless of their type or function, can be seamlessly integrated into the I4.0, facilitating interoperability and efficient data exchange across the entire production network. However, traditional AAS solutions, namely AAS Types 1 and 2, cannot address all the requirements of modern industry environments. To overcome these limitations, the AAS Type 3 (proactive) extends beyond the conventional functionalities of traditional AAS by incorporating more sophisticated features, enabling I4.0 components to operate with greater autonomy, intelligence, and collaborative capabilities. While traditional AAS implementations (Types 1 and 2) have been widely studied and adopted, research on AAS Type 3 is still in its early stages, lacking formal specifications and standardized guidelines for its design and implementation. In this context, this article presents an agent-based AAS approach for implementing AAS Type 3, offering readers a clearer, more accessible description of its features and practical applicability while encouraging further research and development in this emerging topic. For this purpose, several experiments were performed to gain a better understanding of its application in industrial scenarios. Additionally, the article discusses the experiences, insights, limitations and future perspectives derived from the design and experimental implementation of the agent-based AAS approach. |
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