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DMaaST

Financiado

Innovative modelling and assessment capabilities through MaaS for Manufacturing...

Innovative modelling and assessment capabilities through 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 4 layers: The data layer establishe... ver más

30/04/2028

IDENER RESEARCH &...

6M€

Presupuesto del proyecto: 6M€

Líder del proyecto

IDENER RESEARCH & DEVELOPMENT AGRUPACION DE I... No se ha especificado una descripción o un objeto social para esta compañía.

Ver 13 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-04-28

HORIZON-CL4-2023-TWIN-TRANSITION-01-07: Achieving resiliency in value netwo... ExpectedOutcome:Projects are expected to contribute to the following outcomes:

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13 Participantes

IDENER RESEARCH & DEVELOPMEN...

1.35M€ | Lider

GND

213.75K€ | Participante

IND YAZILIM BILISIM TEKNOLOJ...

638.75K€ | Participante

PROSPEH DOO

588.75K€ | Participante

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Duración del proyecto: 48 meses Fecha Inicio: 2024-04-28
Fecha Fin: 2028-04-30

Líder del proyecto

IDENER RESEARCH & DEVELOPMENT AGRUPACION DE I... No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 6M€

Descripción del proyecto 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 4 layers: The data layer establishes a foundation for mapping manufacturing ecosystem information using ontologies and decentralized knowledge graphs, ensuring a trusted cross-organization real-time data integration. Next, 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. 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. The fourth layer focuses on presenting the information in a user-friendly manner with timely scoreboards. Additionally, a dedicated module will assess the production's circularity and sustainability and considering products traceability through the EU-DPP. Therefore, the sustainability and remanufacturing opportunities of the production process will 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.

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