DMAAST: Innovative modelling and assessment capabilities through Manufacturing as a Service (MaaS) for manufacturing ecosystem resiliency.

DMaaST aims to enhance the manufacturing ecosystem’s resiliency and capability of self-adaptation in response to external events. It is achieved through a Smart Manufacturing Platform comprising four layers:

 

• The data layer establishes a foundation for mapping manufacturing ecosystem information using ontologies and decentralized knowledge graphs, ensuring a trusted cross-organization realtime data integration.
• A layer with a two-level cognitive digital twin is created, with the low-level DT modelling two use cases’ manufacturing services production line; and the high-level DT modelling the main stages of use-cases’ sectors value chains. The resulting DTs will use human expertise-knowledge, data-driven algorithms and physical modelling to provide a reliable and robust DT of the manufacturing ecosystem. T
• The next layer employs the data and modelling layer’s information to present a multi-objective distributed decision support system algorithm combining multi-objective techniques and the latest trends in Federated Deep Learning. This makes DTs actionable models and provides the necessary information to make optimal production decisions.
• A final layer focuses on presenting the information in a user-friendly manner with timely scoreboards.

 

Additionally, a dedicated module assesses the production’s circularity and sustainability and considering products traceability through the EU-DPP. Therefore, the sustainability and remanufacturing opportunities of the production process are to be improved. The project ensures scalability, providing information for replicating and trying new manufacturing processes thanks to the manufacturing services digital warehouse while assessing risks and opportunities for improvement. DMaaST innovations enable the manufacturing ecosystem to adopt the Manufacturing as a Service concept by smoothly evolving all the technologies from a TRL3 to a consolidated TRL6 in 2 use cases in key sectors, aerospace and electronics.

Our main tasks

• Data availability and definition of Knowledge Assets.
• SMAP architecture and MO-DDSS foundation.
• Multi-objective optimisation and DDSS fitting for use cases scenarios and validation.
• Manufacturing services digital warehouse.
• DMaaST’s Modules integration in the cloud for efficient interconnectivity.
• Working framework definition, KPIs and team alignment.
• Technical and scientific management.
• Project monitoring, Quality Control and Risk management.
• Administrative and financial management.

Partners

EEIP ENERGY EFFICIENCY IN INDUSTRIAL PROCESSES | GND TECHNOLOGY | HOLOSS | IDENER.AI | IND BILISIM | JPB SYSTEME | KAMSTRUP | KAT KARLSRUHE INSTITUTE OF TECHNOLOGY | PROSPEH

Start date – finish date

05 / 2024 - 04 / 2028