Factories of the Future FoF ICT in WP Danuta Seredynska DG CONNECT, European Commission Complex Systems & Advanced Computing (A3)

Transcription

1 Factories of the Future FoF ICT in WP 2015 Danuta Seredynska DG CONNECT, European Commission Complex Systems & Advanced Computing (A3)

2 Obj.FoF-8: Modelling, simulation, analytics & forecasting CAx Simulation & Tools Product Planning Product Design Production Planning Rampup Production Use of Product Service Digital Factory CAD Virtual Training CAE (CFD, FEA, ) CAPP CAM DMU Courtesy DFKI 2013

3 Obj.FoF-8: WP2015 ICT-enabled modelling, simulation analytics and forecasting technologies Collaborative projects in 2 areas + 1 CSA: a)r&i Actions: What do we asking for? Industry driven R&D including validation/demonstration i.innovative modelling, simulation, analytics and forecasting for manufacturing at large Modelling and simulation methods involving multiple phenomena (physical, mechanical, energetic, chemical, ); including multi-scale and integrated discrete/continuous models, multidisciplinary and multiobjective design optimisation tools taking holistic approach ii.integrated modelling, simulation and information management systems Integrated information management systems for production systems and advanced CAx, modelling, simulation and decision support toolboxes for new manufacturing processes b) Support Action (CSA) Build R&D constituencies & develop research and innovation agendas Stimulate EU-US collaboration on R&I related to modelling and simulation

4 Obj.FoF 8 ICT enabled modelling, simulation, analytics and forecasting technologies Scope Targets: Modelling and simulation methods for multiple i.r&i Actions: Small projects, phenomena 100% EU funding Innovative modelling, simulation, Experiments analytics integrating virtual and forecasting and physical world tools in real time for manufacturing at large: Knowledge based systems with advanced analytics and Modelling and simulation decision tools for support multiple tools and which interconnected exploit 'big data' from smart sensors phenomena including discrete/continuous models with experiments combining Expected virtual Impact: and physical world in real-time Integrated Increased knowledge-based productivity during systems design covering and ramp-up the complete phase product for life-cycle higher based mass on customisation advanced analytics, capacity smart to access decision on-demand support tools and scalable self-learning manufacturing capabilities processes exploiting and 'big data'. services through agreed standard. Improved cost efficiency and accuracy, reliability and speed of simulation techniques for manufacturing.

5 Obj.FoF 8 ICT enabled modelling, simulation, analytics and forecasting technologies Scope Targets: Standardisation activities related to info/knowledge exchange, data sharing, semantics, etc Minimum 2 comprensive and complementary industrial use cases ii.r&i Actions: Large projects, 100% EU funding Integrating modelling, simulation and information management systems: Development of integrated information management systems for product-process-production systems embedded in their socio-economic context Development Expected Impact: of advanced CAx, modelling, simulation and decision support Reduced toolboxes time tailored to production for novel and manufacturing optimised supply processes, chain e.g. laser-based enabled and by increased additive manufacturing tool interoroperability and data integration Enhanced interoperability of integrated product and factory design systems and global state monitoring enabling new type of services related to data analysis, simulations and visualisation techniques at each manufacturing stage.

6 Obj. FoF9 I4MS: ICT Innovation I4MS Phase II for Manufacturing SMEs Key role of SMEs in value chains: users and suppliers SME need more than s 150 application experiments along value chains clustered Clustered around networks of competence centres Open Calls for experiments during course of projects

7 Obj. FoF9 WP2015 Main features Facilitate the adoption of advanced ICT in manufacturing domain Two types of Innovation experiments: Application experiments provide first users with novel products and services and assist them in the application in their respective environments Assessment experiments support suppliers of innovative high-tech equipment to assess and validate their prototypes or products in production-like environments Experiments should be clustered in large projects to better exploit EU-added value and achieve critical mass. Financial support to third parties (additional suppliers and users) which will be selected during the projects.

8 Obj. FoF9 WP2015 ICT Innovation for Manufacturing SMEs (I4MS) Collaborative projects in 3 areas + 1 CSA: a)innovation Actions: i.highly flexible and near-autonomous robotics systems (application experiments) ii.hpc Cloud-based modelling, simulation and analytics services for modelling multiple interconnected phenomena (application experiments) Integrating multiple tools across the process chain, exploiting big data, integrating novel mobile interfaces for data management and decision support for real-time responses and addressing security and privacy issues iii.integration of CPS modules in manufacturing processes and process chain(application or assessment experiments) Increase automation in production SMEs and create novel value added services linked to process surveillance and maintenance b) Support Action (CSA) Network of Innovation multipliers to reinforce investment in research and innovation

9 THANK YOU DG CONNECT (FoF on DAE Web): Horizon 2020 on the web: Factories of the Future (FoF) on the web: