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IMPROVE

Financiado

Cerrado

Innovative Modeling Approaches for Production Systems to raise validatable effic...

Innovative Modeling Approaches for Production Systems to raise validatable efficiency The rise of the system complexity, the rapid changing of consumers demand require the European industry to produce more customized products with a better use of resources. The main objective of IMPROVE is to create a virtual Facto... ver más

31/08/2018

TH OWL

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto

TECHNISCHE HOCHSCHULE OSTWESTFALENLIPPE 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 13 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2018-08-31

FoF-08-2015: ICT-enabled modelling, simulation,...

I+D

Cerrada hace 9 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

13 Participantes

TH OWL

508.00K€ | Lider

EURICE EUROPEAN RESEARCH AND...

307.29K€ | Participante

TRANSITION TECHNOLOGIES PSC...

56.27K€ | Participante

Xcelgo

279.82K€ | Participante

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Duración del proyecto: 36 meses Fecha Inicio: 2015-08-03
Fecha Fin: 2018-08-31

Líder del proyecto

TECHNISCHE HOCHSCHULE OSTWESTFALENLIPPE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 4M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The rise of the system complexity, the rapid changing of consumers demand require the European industry to produce more customized products with a better use of resources. The main objective of IMPROVE is to create a virtual Factory of the Future, which provides services for user support, especially on optimization and monitoring. By monitoring anomalous behaviour will be detected before it leads to a breakdown. Thereby, anomalous behaviour is detected automatically by comparing sensor observation with an automatically generated model, learned out of observations. Learned models will be complemented with expert knowledge because models cannot learn completely. This will ensure and establish a cheap and accurate model creation instead of manual modelling. Optimization will be performed and results will be verified through simulations. Therefore, the operator has a broad decision basis as well as a suggestion of a DSS (Decision Support System), which will improve the manufacturing system. Operator interaction will be done by a new developed HMI (Human Machine Interface) providing the huge amount of data in a reliable manner. To reach this aim, every step of the research process is covered by a minimum of two experienced consortium partners, who conclude the results of the project using four demonstrators. The basis for IMPROVE are industrial use-cases, which are transferable to various industrial sectors. Main challenges are reducing ramp-up phases, optimizing production plants to increase the cost-efficiency, reducing time to production with condition monitoring techniques and optimise supply chains including holistic data. Consequently, the resource consumption, especially the energy consumption in manufacturing activities, can be reduced. The optimized plants and supply chains enhance the productivity of the manufacturing during different phases of production. Furthermore, the industrial competitiveness and sustainability in EU will be strengthened.

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