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leArning and robuSt deciSIon SupporT systems for agile mANufacTuring environment...

leArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments With a multidisciplinary consortium combining key skills in AI, manufacturing, edge computing and robotics, ASSISTANT aims to create intelligent digital twins through the joint use of machine learning (ML), optimization, simulatio... ver más

31/10/2023

INSTITUT MINESTELE...

6M€

Presupuesto del proyecto: 6M€

Líder del proyecto

INSTITUT MINESTELECOM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 14 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-10-31

ICT-38-2020: Artificial intelligence for manufac... Specific Challenge:State-of-the-art AI technologies need to be integrated with advanced manufacturin...

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14 Participantes

INSTITUT MINESTELECOM

865.72K€ | Lider

ESMENA

SIEMENS AKTIENGESELLSCHAFT

459.50K€ | Participante

ATLAS COPCO AIRPOWER NV

247.50K€ | Participante

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Duración del proyecto: 39 meses Fecha Inicio: 2020-07-22
Fecha Fin: 2023-10-31

Líder del proyecto

INSTITUT MINESTELECOM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 6M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto With a multidisciplinary consortium combining key skills in AI, manufacturing, edge computing and robotics, ASSISTANT aims to create intelligent digital twins through the joint use of machine learning (ML), optimization, simulation and domain models. The resulting tools permit to design and operate complex collaborative and reconfigurable production systems based on data collected from various sources such as IoT devices. ASSISTANT targets a significant increase in flexibility and reactivity, products/processes quality, and in robustness of manufacturing systems, by integrating human and machine intelligence in a sustainable learning relationship. ASSISTANT provides decision makers with generative design based software for all manufacturing decisions. Rather than writing ad hoc code for each manufacturing sector, it provides a set of intelligent digital twins that self adapt to the manufacturing environment. It promote a methodology that enhances generative design with learning aspects of AI thanks to the data available in manufacturing. ASSISTANT aims to synthesize predictive/prescriptive models adjusted to the shop floor for each decision levels. Digital twins will be used as oracles by ML in order to converge towards models in phase with reality. This means that rather than writing specific code to cover a restricted set of goals/scenarios/hypotheses for a manufacturing system and a decision level, ASSISTANT will aim at learning models that can be used by standard optimization libraries. In this context, ML is used to predict parameter values, characterize parameters uncertainty, and acquire physical constraints. ASSISTANT will experiment this methodology on a significant panel of use cases selected for their relevance in the current context of the digital transformation of production in major manufacturing sectors undergoing rapid transformations like the energy, the industrial equipment, and automotive sectors which already make extensive use of digital twins.

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