Imagine if different brands of smartphones could instantly understand each other's apps without any manual setup. This project does that for giant factories and supply chains. It creates a digital translator that lets machines from different companies talk to each other automatically, removing the need for expensive human programmers to bridge the gap.
Industrial companies waste significant time and money manually integrating data between different platforms and standards. This lack of interoperability slows down innovation and increases the cost of digital transformation.
An open-source, microservice-based system (Arrowhead v5.0) that automatically translates industrial data models using AI and ontology-based services.
If you are a vehicle manufacturer dealing with fragmented data from various parts suppliers — this project developed an autonomous translation system that reduces manual data handling by more than 50%. This allows for faster integration of new suppliers into the production line.
If you are a chip fabrication plant dealing with complex equipment from different vendors — this project developed a microservice-based architecture that enables secure and scalable data exchange. This reduces the time and cost needed to set up and manage interactions within the production value network.
If you are a green energy provider dealing with diverse energy conversion systems — this project developed machine-interpretable content tools that automate information flow. This increases productivity and flexibility when scaling renewable energy infrastructure.
Based on available project data, the technology aims to lower engineering costs for digitalization and automation by reducing the need for manual data handling.
Yes, the project uses a microservice-based architecture designed for secure and scalable deployment across diverse and distributed production value networks.
The project is based on the Eclipse Arrowhead platform and focuses on establishing sustainable governance of an open-source architecture.
It provides updates and semantic extensions to major industrially accepted data models to ensure international alignment and uptake.
The project runs from June 1, 2023, to August 31, 2026, with 13 total deliverables planned.
The consortium is heavily weighted toward industrial application, with 26 industry partners (57% of the total) and 16 SMEs. This strong industrial presence, spanning 11 countries, suggests the technology is being developed with direct commercial requirements in mind rather than purely academic interest.
Luleå Tekniska Universitet
Contact us to identify which of the 11 use cases matches your production environment.
