The Airframe ITD aims at re-thinking and developing the technologies as building blocks and the “solution space” on the level of the entire or holistic aircraft: pushing aerodynamics across new frontiers, combining and integrating new materials and structural techniques – and integrating innovative new controls and propulsion architectures with the airframe; and optimizing this against the challenges of weight, cost, life-cycle impact and durability.

InterQ project proposes a new generation of digital solutions based on intelligent systems, hybrid digital twins and AI-driven optimization tools to assure the quality in smart factories in a holistic manner, including process, product and data (PPD quality). The broad vision of InterQ project will allow controlling the quality of a smart manufacturing environment in an end-to-end approach by means of a PPD quality hallmark stored in a distributed ledger. The concepts of InterQ will be applied in three high-added value industrial applications. The main objective of InterQ project is to measure, predict and control the quality of the manufactured products, manufacturing processes and gathered data to assure Zero-Defect-Manufacturing by means of AI-driven tools powered with meaningful and reliable data. The project develops five modules: 1) Thanks to the InterQ PPD quality hallmark, the quality of the process, product and data are interlinked, integrated and time stamped. A hallmark will be created after each stage, and the quality will be traced across the supply chain. A trusted framework will be implemented using distributed ledger (InterQ-TrustedFramework module) to exchange quality information. 2) The InterQ-Process module of the project will obtain more meaningful process data for quality optimization. This data will be obtained using new sensors close to the tool and by AI-driven virtual sensors. 3) The project presents new solutions (InterQ-Product module) to predict the final quality of the processes using digital twins fed by experimental data and new digital sensors to measure the product quality. 4) The reliability of data will be checked in two layers: in real time and based on historical and statistical analysis of the data streams (InterQ-Data module). 5) Finally, InterQ-ZeroDefect module will use the reliable information about the process and product quality to improve the production for Zero-Defect-Manufacturing by means of AI-driven applications.

Europe is still lacking an efficient systemic multi-level approach that enables a recursive, cost-effective, holistic and integrated application of circular principles to the digital uplifting of factory 4.0 capital investments; addressing issues at product, process, system as well as the entire value-chain levels, integrating best practices from emerging enabling digital technologies and avoid a two speed digital transformation across industries in different sectors. LEVEL-UP will offer a scalable platform covering the overall lifecycle, ranging from the digital twins setup, modernisation actions to diagnose and predict the operation of physical assets, to the refurbishment and remanufacturing activities towards end of life. In-situ repair technologies and the redesign for new upgraded components will be facilitated through virtual simulations for increased performance and lifetime. LEVEL-UP will therefore comprise new hardware and software components interfaced with the current facilities through IoT and data-management platforms, while being orchestrated through eight (8) scalable strategies at component, work-station and shopfloor level. The actions for modernising, upgrading, refurbishing, remanufacturing, and recycling will be structured and formalised into ten (10) special Protocols, linked with an Industrial Digital Thread weaving a seamless digital integration with all actors in the value chain for improved future iterations. LEVEL-UP will be demonstrated in 7 demo sites from different sectors. The impact of LEVEL-UP to the European manufacturing industry, but also the society itself, can be sum-marised in the following (with a horizon of 4 years after project ends): (i) increase of the material and re-source efficiency by 11.5%, (ii) increased reliability by 16% of the equipment in an extended lifetime by 20%, (iii) over 50% increase of the Return on Investment (ROI), (iv) about 810 new jobs created and (v) over 80M EUR ROI for the consortium.

The final aim of Wheel Watcher project is to boost the profitability of the railway sector by means of an advanced wheel status monitoring and control system, providing railway operators with accurate and real-time information on wheel wear to drive preventive maintenance actions thus avoiding premature replacement of this equipment. Main industrial impact of the project is to achieve a wheel life increase of 50%, and an increase of 25% of distance between wheel reprofiling actions, allowing the railway operator for conducting a remote but in-depth inspection of wheels. This industrial impact involves a subsequent relevant economic impact, targeting a cost reduction of 25% in maintenance-related costs as a result of less spare wheels and less low value-added works needed. These industrial impacts rely on relevant technical progresses: a high precision mechatronic platform addressing mechanical challenges derived of its wayside placement, a remote measuring unit comprising an ad-hoc thermoelectric cell guaranteeing thermal stability under potential extreme weather conditions, and a novel laser system doubling current power density available, able to measure trains moving at high speeds in outdoor locations, and a user-friendly control software including a self-diagnosis tool. WheelWatcher will have a deep impact on project’s partners, producing an aggregate profit of 23 M€ with more than 35 new jobs expected to be created, and a positive environmental impact due to less CO2 and less scrap. Excellence of the project will allow for dramatic improvements regarding the more relevant challenges faced by railway operators, as compared to the current state-of-the-art: automated high frequency wheel measuring, enhanced quality of measurements at normal speed (100 km/h) irrespective of weather conditions, remote monitoring without costly displacements to the tracks, no need of service disruption during measuring processes, and as consequence of all, a cut on operating costs.

Smart factories are characterised by smart processes, smart machines, smart tools and smart products as well as smart logistics operations. These generate large amounts of data, which can be used for analysis and fault prevention, as well as the optimisation of the quality of manufacturing processes and products. DAT4.ZERO is a Digitally-enhanced Quality Management System (DQM) that gathers and organizes data from a Distributed Multi-sensor Network, which, when combined with a DQM Toolkit and Modeling and Simulation Layer, and further integrated with existing Cyber-Physical Systems (CPS), offers adequate levels of data accuracy and precision for effective decision-support and problem-solving – utilizing smart, dynamic feedback and feed-forward mechanisms to contribute towards the achievement of Zero Defect Manufacturing (ZDM) in smart factories and their ecosystems. The aim is to Integrate smart, cost-effective sensors and actuators for process simulation, monitoring and control; develop real-time data validation and integrity strategies within actual production lines; demonstrate innovative data management strategies as an integrated approach to ZDM; & develop strategies for rapid line qualification and reconfiguration. Deployed in 5 distinct industrial pilot lines we address the following primary objective: Develop and demonstrate an innovative DQM system and deployment strategy for supporting European manufacturing industry in realizing ZDM in highly dynamic, high-value, high-mix, low-volume production contexts, by effective selection and integration of sensors and actuators for process monitoring and control, a DQM platform with an architecture that provides reliable and secure knowledge extraction to ensure integrity of data, & strategies for advanced realtime data analysis and modeling in multiple domains and sectors that will increase quality, reduce ramp-up times and decrease time-to-market.