DIGITbrain Project Documentation
Compared to the digital twin concept, which is already increasingly used by manufacturing companies today, the DIGITbrain concept will go one step further by developing the 'Digital Product Brain' which will store data throughout the entire life cycle of a production line or a machine.
By collecting all this data, it will be possible to customise and set-up machines / production assets for very specific manufacturing tasks whenever needed. This will enable a new manufacturing model, called Manufacturing-as-a-Service (MaaS), which will allow for on-demand production of much more specialised products, even in smaller quantities and still in an economically profitable way.
For full project details visit digitbrain.eu.
Using this documentation
The primary aim of this documentation is to serve as a point of reference for providers and consumers of the different assets in DIGITbrain. These include Microservices, Algorithms, Model and Data, as well as Behaviours and Processes.
Please navigate using the available navigation bars. Information on the fields available on each asset is available in detail or as an overview.
If you're new to DIGITbrain, refer to Getting Started.
Several examples are already running on the DIGITbrain platform. A complete specification has been provided for each of these examples and it is hoped that these can serve as a guide for users looking to describe their own assets for use in the DIGITbrain platform.
See these examples here. Note that some field names may appear differently.
Much of the information presented across this site is automatically updated based on the schemas defined in the Asset Metadata Registry, which stores information about the different assets that are created on the platform. It is up-to-date with the very latest production version.
This documentation is served from a GitHub repository.
Learn about contributing to this documentation by reading our contributor's guide.
If you experience any issues using this site, please let us know by creating an issue on GitHub.