Data infrastructure for continuous production without system interruptions
Data Backbone
Project description
Access to digital process data
Do you have lots of data formats that are incompatible because they originate from different domains? Are system interruptions keeping you from automating your production platforms? There is a solution. In the Data Backbone project, fortiss is developing an infrastructure that combines data models and provides access to digital process data. The vision is to be able to automatically create the production process and the robot programming from one production order.
Seamless data exchange
The prerequisite for this vision is a process description with analyzable content that is digitally available regardless of the programming language. Data Backbone provides access to all of the data relevant to the entire value chain and ensures the uninterrupted exchange of data between the systems, thus making it possible to have a continuous, automated production environment that is efficient and transparent.
Funding
Bavarian Ministry of Economic Affairs, Regional Development and Energy (STMWI) with project support by Bayern Innovativ former ZentrumDigitalisierung Bayern (ZD.B).
Project duration
01.01.2018 - 31.03.2020
fortiss Lab
Contact
Project partner
Publications
- Toward a Knowledge-Based Data Backbone for Seamless Digital Engineering in Smart Factories In Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), pages 164–171, Vienna, Austria, 2020. Details URL DOI BIB
- A Hardware-Agnostic OPC UA Skill Model for Robot Manipulators and Tools In Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), Zaragoza, Spain, 2019. Details BIB
- OPC UA NodeSet Ontologies as a Pillar of Representing Semantic Digital Twins of Manufacturing Resources In Proceedings of the IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), pages 1085–1092, Zaragoza, Spain, 2019. Details URL DOI BIB