H2020
Europeo
ExpectedOutcome:The digital transformation of the European manufacturing industry depends on the availability and uptake of high quality, efficient, affordable and optimised systems, such as those offered by cloud infrastructures, simulation-based twin technologies, data driven approaches. However, there is a low uptake in Europe for such technologies, for example in the case of cloud computing only 1 company in 4 apply it and only 1 in 5 for SMEs[1].
Projects are expected to contribute to the following outcomes:
Support the transition towards industrial digitalisation;Increase speed of innovation by optimising the use of existing research results and facilitating uptake of new projects results;Design digital tools for industry (e.g. cloud systems, simulation-based twin technologies, data driven approaches, AI-based and reinforcement learning solutions) to enhance efficiency and product quality, as well as to increase the capability for better and faster reaction to market changes;Contribute to the development of advanced material modelling solutions in particular for manufacturing industry;Enhance data interoperability and new type of services related to...
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ExpectedOutcome:The digital transformation of the European manufacturing industry depends on the availability and uptake of high quality, efficient, affordable and optimised systems, such as those offered by cloud infrastructures, simulation-based twin technologies, data driven approaches. However, there is a low uptake in Europe for such technologies, for example in the case of cloud computing only 1 company in 4 apply it and only 1 in 5 for SMEs[1].
Projects are expected to contribute to the following outcomes:
Support the transition towards industrial digitalisation;Increase speed of innovation by optimising the use of existing research results and facilitating uptake of new projects results;Design digital tools for industry (e.g. cloud systems, simulation-based twin technologies, data driven approaches, AI-based and reinforcement learning solutions) to enhance efficiency and product quality, as well as to increase the capability for better and faster reaction to market changes;Contribute to the development of advanced material modelling solutions in particular for manufacturing industry;Enhance data interoperability and new type of services related to the data analysis, simulations and/or visualisation techniques in each stage of the material value chain (design, processing, manufacturing, etc.) using FAIR data principles.
Scope:Digital tools can enable industry to control manufacturing processes and address issues more efficiently and effectively as they run and update the production plant, while improving key product and production performance indicators such as yield and throughput.
Proposals under this topic have to
design robust digital tools integrating materials modelling and materials process development for industry;promote use and adaptation of existing tools and process developments that are applicable to different sectors;contribute also to the development of simulation and optimisation methods to facilitate more efficient design space exploration via experimentation, thereby reducing physical testing and improving quality;enhance efficiency of the manufacturing process;improve process and product quality;improve decision making efficiency, quality and understanding, while at the same time maintaining low operational costs. Interconnection between processes and other industries is also in the scope, as there is an increased integration of different domains and disciplines in complex workflows. To overcome the problem, proposals have to address interoperability by implementing available data standards like MODA, CHADA and ontologies like EMMO, as well as cooperation with the Industry Commons developments.
The proposed use cases for the developed tool should demonstrate the business case and how more sustainable solutions are achieved in the market, for example by reducing waste and/or emissions during production. A Life Cycle Assessment should be included to estimate the environmental improvement, together with a Life Cycle Cost assessment to demonstrate the lower operational costs.
Proposals submitted under this topic should include a business case and exploitation strategy, as outlined in the introduction to this Destination.
Specific Topic Conditions:Activities are expected to achieve TRL 6 by the end of the project – see General Annex B. – see General Annex B.
Cross-cutting Priorities:Artificial IntelligenceDigital Agenda
[1]https://ec.europa.eu/eurostat/statistics-explained/index.php/Cloud_computing_-_statistics_on_the_use_by_enterprises.
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La ayuda es de ámbito europeo, puede aplicar a esta linea cualquier empresa que forme parte de la Comunidad Europea.
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