Periodic Reporting for period 1 - Arrowhead FPVN (Arrowhead Flexible Production Value Networks)
Okres sprawozdawczy: 2023-06-01 do 2024-05-31
Context and Motivation
The Arrowhead fPVN project operates within the framework of digital transformation in industrial settings, specifically focusing on improving production efficiency through advanced cyber-physical systems. The need for this project arises from the challenges faced by European production sectors, including high operational costs, lack of interoperability between diverse systems, and the increasing demand for scalable and flexible production capabilities. These challenges are magnified by the rapid pace of technological change and the complex data integration required in modern production environments.
Objectives
The primary objective of the Arrowhead fPVN project is to develop a framework that enables seamless interoperability of data and services across various platforms and standards through the use of advanced microservices and cyber-physical systems of systems (CPSoS). The project seeks to:
Enhance digital interoperability across different industrial domains using a domain-specific language and translation microservices.
Reduce reliance on manual interventions by implementing autonomous systems that can handle data translation and integration tasks more efficiently.
Foster collaboration and knowledge exchange between academia and industry to drive innovation in digital transformation technologies.
Integration of Social Sciences and Humanities
Incorporating social sciences and humanities into the project is vital for addressing the broader implications of technological implementation, such as workforce adaptation, ethical considerations of autonomous systems, and the socio-economic impacts of digital transformation. These disciplines contribute to a holistic approach to technology deployment, ensuring that advancements are sustainable, inclusive, and aligned with societal values and norms.
The Arrowhead fPVN project operates within the framework of digital transformation in industrial settings, specifically focusing on improving production efficiency through advanced cyber-physical systems. The need for this project arises from the challenges faced by European production sectors, including high operational costs, lack of interoperability between diverse systems, and the increasing demand for scalable and flexible production capabilities. These challenges are magnified by the rapid pace of technological change and the complex data integration required in modern production environments.
Objectives
The primary objective of the Arrowhead fPVN project is to develop a framework that enables seamless interoperability of data and services across various platforms and standards through the use of advanced microservices and cyber-physical systems of systems (CPSoS). The project seeks to:
Enhance digital interoperability across different industrial domains using a domain-specific language and translation microservices.
Reduce reliance on manual interventions by implementing autonomous systems that can handle data translation and integration tasks more efficiently.
Foster collaboration and knowledge exchange between academia and industry to drive innovation in digital transformation technologies.
Integration of Social Sciences and Humanities
Incorporating social sciences and humanities into the project is vital for addressing the broader implications of technological implementation, such as workforce adaptation, ethical considerations of autonomous systems, and the socio-economic impacts of digital transformation. These disciplines contribute to a holistic approach to technology deployment, ensuring that advancements are sustainable, inclusive, and aligned with societal values and norms.
Technical and Scientific Activities
Development of Microservice Architectures
The Arrowhead fPVN project focused significantly on developing a microservice architecture to enable flexible and scalable integration across various industrial platforms. Key activities included:
Refinement of the Eclipse Arrowhead Framework: Enhancements were made to the core system, facilitating better service registration, orchestration, and authorization, which are essential for managing complex interactions within cyber-physical systems of systems (CPSoS).
Implementation of Autonomous Mechanisms: These mechanisms were designed to handle non-interoperable properties automatically, reducing the need for human intervention and improving system resilience.
Advancement in Translation Technologies
A major technical thrust of the project was the advancement of translation technologies, which are critical for ensuring seamless data integration across diverse data models and standards:
Development of Domain-Specific Language (DSL): This language was created to aid in the modeling and translation of data, enabling a more streamlined process for integrating disparate systems.
AI and Machine Learning Techniques: The project investigated and implemented AI and ML techniques to enhance the capability of the translation services, focusing on automating the translation process and reducing errors.
Integration of Semantic Web Technologies
To enhance interoperability across different industrial standards, the project leveraged semantic web technologies:
Semantic Analysis of Data Models: This involved a detailed examination of existing industrial data models, identifying key semantic elements that could improve data interoperability.
Extension of Data Models: Based on the semantic analysis, the project proposed extensions to these models to enhance their flexibility and utility across different use cases.
Cyber-Physical Systems Development
The project contributed to the development of cyber-physical systems by creating integrated environments that combine physical operations with computational resources:
Prototyping Cyber-Physical Systems (CPSoS): Prototypes were developed to demonstrate the practical application of the project’s frameworks and architectures in real-world industrial settings.
System of Systems (SoS) Approach: This approach was essential for managing complex systems integration, where multiple subsystems interact to perform integrated and coordinated functions.
Main Achievements
Technical Enhancements
Microservice Architecture: Successfully developed and deployed a microservice architecture that is now part of the Eclipse Arrowhead framework, significantly improving scalability and flexibility in industrial IT environments.
Autonomous Translation Services: Achieved a significant reduction in the need for manual data handling by implementing machine-based translation micro-services, which facilitate more than 50% of the necessary translations autonomously.
Scientific Contributions
Advanced Data Model Interoperability: The project’s efforts in semantic technology and AI-enhanced translations have led to a more robust framework for data interoperability across different industrial sectors, impacting how data models are constructed and utilized.
Publications and Intellectual Contributions: The project has generated numerous scientific papers and contributions to standards, particularly in the areas of data model standardization and the application of semantic technologies in industrial settings.
Prototypes and Demonstrations
Implementation of CPSoS: Demonstrated the viability of complex system integrations through prototypes that showcase the real-world application of microservice architectures and autonomous systems in industrial environments.
Development of Microservice Architectures
The Arrowhead fPVN project focused significantly on developing a microservice architecture to enable flexible and scalable integration across various industrial platforms. Key activities included:
Refinement of the Eclipse Arrowhead Framework: Enhancements were made to the core system, facilitating better service registration, orchestration, and authorization, which are essential for managing complex interactions within cyber-physical systems of systems (CPSoS).
Implementation of Autonomous Mechanisms: These mechanisms were designed to handle non-interoperable properties automatically, reducing the need for human intervention and improving system resilience.
Advancement in Translation Technologies
A major technical thrust of the project was the advancement of translation technologies, which are critical for ensuring seamless data integration across diverse data models and standards:
Development of Domain-Specific Language (DSL): This language was created to aid in the modeling and translation of data, enabling a more streamlined process for integrating disparate systems.
AI and Machine Learning Techniques: The project investigated and implemented AI and ML techniques to enhance the capability of the translation services, focusing on automating the translation process and reducing errors.
Integration of Semantic Web Technologies
To enhance interoperability across different industrial standards, the project leveraged semantic web technologies:
Semantic Analysis of Data Models: This involved a detailed examination of existing industrial data models, identifying key semantic elements that could improve data interoperability.
Extension of Data Models: Based on the semantic analysis, the project proposed extensions to these models to enhance their flexibility and utility across different use cases.
Cyber-Physical Systems Development
The project contributed to the development of cyber-physical systems by creating integrated environments that combine physical operations with computational resources:
Prototyping Cyber-Physical Systems (CPSoS): Prototypes were developed to demonstrate the practical application of the project’s frameworks and architectures in real-world industrial settings.
System of Systems (SoS) Approach: This approach was essential for managing complex systems integration, where multiple subsystems interact to perform integrated and coordinated functions.
Main Achievements
Technical Enhancements
Microservice Architecture: Successfully developed and deployed a microservice architecture that is now part of the Eclipse Arrowhead framework, significantly improving scalability and flexibility in industrial IT environments.
Autonomous Translation Services: Achieved a significant reduction in the need for manual data handling by implementing machine-based translation micro-services, which facilitate more than 50% of the necessary translations autonomously.
Scientific Contributions
Advanced Data Model Interoperability: The project’s efforts in semantic technology and AI-enhanced translations have led to a more robust framework for data interoperability across different industrial sectors, impacting how data models are constructed and utilized.
Publications and Intellectual Contributions: The project has generated numerous scientific papers and contributions to standards, particularly in the areas of data model standardization and the application of semantic technologies in industrial settings.
Prototypes and Demonstrations
Implementation of CPSoS: Demonstrated the viability of complex system integrations through prototypes that showcase the real-world application of microservice architectures and autonomous systems in industrial environments.
A successful execution of the Arrowhead fPVN concept and approach is expected to impact the overall engineering costs for digitalisation and automation solutions. The potential impact of the project can be summarized to the following:
Microservice Architecture: The project successfully developed a scalable and flexible microservice architecture integrated within the Eclipse Arrowhead framework. This architecture supports dynamic deployment and autonomous utilization of services across different industrial sectors.
Translation Services: The creation of autonomous machine-based translation microservices, which can handle over 50% of necessary translations without human intervention, represents a significant technical achievement. These services utilize advanced AI and machine learning techniques to improve accuracy and efficiency.
Semantic Web Technologies: The extension of industrial data models using semantic web technologies has enhanced the interoperability across different platforms. This includes the development of a domain-specific language (DSL) to facilitate easier and more reliable data modeling and translation.
Cyber-Physical Systems Prototypes: Demonstrations of CPSoS prototypes provided tangible proof-of-concepts that illustrated the practical application and benefits of the project's technological advancements in real-world settings.
Microservice Architecture: The project successfully developed a scalable and flexible microservice architecture integrated within the Eclipse Arrowhead framework. This architecture supports dynamic deployment and autonomous utilization of services across different industrial sectors.
Translation Services: The creation of autonomous machine-based translation microservices, which can handle over 50% of necessary translations without human intervention, represents a significant technical achievement. These services utilize advanced AI and machine learning techniques to improve accuracy and efficiency.
Semantic Web Technologies: The extension of industrial data models using semantic web technologies has enhanced the interoperability across different platforms. This includes the development of a domain-specific language (DSL) to facilitate easier and more reliable data modeling and translation.
Cyber-Physical Systems Prototypes: Demonstrations of CPSoS prototypes provided tangible proof-of-concepts that illustrated the practical application and benefits of the project's technological advancements in real-world settings.