Juli Ramon posted this Technology Call
Account Manager at GCCIR
ERTRAC Global Competitiveness Working Group posted this Technology CallSeeking research & innovation centres of excellence in material data spaceMaterial data space addresses a digital infrastructure providing all relevant information on materials and structural parts over the full life-cycle (from cradle to grave: e.g. mining, processing, manufacturing, usage, ageing, repair, recycling,….). As such it can be understood as enabler for knowledge-based material development, for a circular economy approach as well as for advanced monitoring concepts for parts and products. Fundamental research is needed on: • Cost-efficient integration of sensing and communication functions into materials • Processing of such smart materials towards components and products • System integration (powering, data management, data processing & analysis,…) • Ensuring the reliability incl. Repair concepts Based on the data generated by sensorised materials and products made out of it • degradation and ageing mechanism of materials and structures should be derived by applying machine learning concepts, • novel structural health monitoring should be realised and implemented, • advanced recycling and re-use concepts should be investigated.
ERTRAC Global Competitiveness Working Group posted this Technology CallSeeking research & innovation centres of excellence in virtual product developmentVirtual product development as key enabling technology for maintaining global competitiveness of EU industry: • Fast and efficient product development in a digitalized and connected industry • Creation of digital twins for development, production, maintenance and recycling • Minimization of consumption of energy and resources during entire product life cycles Research needed focusing on long term sustainability and affordability: • Efficient tools and methods for handling and development of large, complex systems of systems Material characterization and modelling of next generation composite materials (additive manufacturing) • Creation of new classes of models for digital twins considering the statistical nature of components and systems due to stochastic processes • Creation of tools and methods for development, validation, diagnosis and maintenance of flexible/adaptive/self-learning controllers and systems • Coupling of virtual and real development tools and methods along the development and supply chain (secure and fast data exchange, data plausibility, in the loop data analysis) • Total product life cycle GHG and LCA assessment as part of digital twins • User & environment-product compatibility (EMC, radiation, NVH, LEAR, toxicity...)