Multimaterial systems combining metals with thermoplastic fiber reinforced polymer composites (TP-FRPC) are the key for light weight design in the automotive industry. However, the joining of the material partners remains main issue. Currently, no approach exists which sufficiently meets the three core requirements: weight neutrality, cost- and time efficiency and bonding strength. Technologies like adhesive bonding or bolted joints show good results for one or two of the criterions, but not for all three of them. The FlexHyJoin project aims at the development of a joining process for hybrid components, which satisfies all three criterions. Induction Joining (IJ) and Laser Joining (LJ) are combined, since they have complementary fields of application and most of all they do not require additional material and are therefore weight neutral joining methods. Thus, the full lightweight potential is preserved. Additionally, a surface texturing method for the metal is integrated in the approach, which leads to a form closure bonding, providing a high mechanical bonding performance. Finally, a main aspect of the FlexHyJoin project is to integrate the surface texturing as well as both joining methods in a single, continuous, and fully automatized pilot process with an overall process control and supervision system. This leads to a maximum of time- and cost-efficiency and will allow the future application of the approach in the mass production of automotives. The key for the automation is an online process control and quality assurance. The FlexHyJoin project provides an essential enabler technology for future mobility concepts. The final result is an innovative joining process for fiber reinforced polymers and metals, suiting the strict requirements of automotive industry and enabling the broad application of hybrid material systems.

The IOTWINS project will deliver large-scale industrial test-beds leveraging and combining data related to the manufacturing and facility management optimization domains, coming from diverse sources, such as data APIs, historical data, embedded sensors, and Open Data sources. The goal is to build a reference architecture for the development and deployment of distributed and edge-enabled digital twins of production plants and processes. Digital Twins collect data from manufacturing, maintenance, operations, facilities and operating environments, and use them to create a model of each specific asset, system, or process. These models are then used to detect and diagnose anomalies, to determine an optimal set of actions that maximize key performance metrics. IOTWINS proposes a hierarchical organization of digital twins modeling manufacturing production plants and facility management deployment environments at increasing accuracy levels: • IoT twins: featuring lightweight models of specific components performing big-data stream processing and local control for quality management operations (low latency and high reliability); • Edge twins: deployed at plant gateways and/or at emerging Multi-access Edge Computing nodes, providing higher level control knobs and orchestrating IoT sensors and actuators in a production locality, thus fostering local optimizations and interoperability; • Cloud twins: performing time-consuming and typically off-line parallel simulation and deep-learning, feeding the edge twin with pre-elaborated predictive models to be efficiently executed in the premises of the production plant for monitoring/control/tuning purposes

The EU strategic autonomy in the digital economy requires more and more data to be processed in the Cloud-Edge-IoT computing continuum, instead of only the central cloud. This requires advanced automation and intelligence of the continuum. At the same time, recent breakthroughs in AI research have shown unprecedented intelligence to handle creative tasks. Such human-like intelligence will eventually disrupt how people use the cloud and continuum. INTEND aims at bringing such human-like intelligence into the cognitive continuum, to achieve the novel concept of intent-based data operation, which is capable of: (1) continually learning how to manage and adapt heterogeneous cloud/edge resources for more efficient data processing, (2) strategic decision making across the decentralized continuum for end-to-end data security and sustainability, and (3) human-friendly interaction with data stakeholders in natural language, for effective and trustworthy human-entric data operation. To achieve this ambitious objective, INTEND builds a strong consortium of world-class AI researchers and leading companies that cover the complete supply chain of the computing continuum. The project will deliver 11 novel software tools, which integrate into an INTEND toolbox. The approach and the tools will be tested and validated on 5 vertical domains, to achieve the novel intent-based data operation for video streaming pipelines, machine data platform, 5G data infrastructure, urban data space, and robotic AI applications. An open platform will be provided to support the extension with new hardware, management tools and AI models into the cognitive continuum. The outputs pave the way of migrating EU's data industry from cloud to the continuum, and implements EC's strategy of human-centric AI in the domain of data processing and computing continuum.

The Pledger project aims at delivering a new architectural paradigm and a toolset that will pave the way for next generation edge computing infrastructures, tackling the modern challenges faced today and coupling the benefits of low latencies on the edge, with the robustness and resilience of cloud infrastructures. The project will deliver a set of tools and processes that will enable a) edge computing providers to enhance the stability and performance effectiveness of their edge infrastructures, through modelling the overheads and optimal groupings of concurrently running services, runtime analysis and adaptation, thus gaining a competitive advantage b) edge computing adopters to understand the computational nature of their applications, investigate abstracted and understandable QoS metrics, facilitate trust and smart contracting over their infrastructures and identify how they can balance their cost and performance to optimise their competitiveness and monitor their SLAs. By providing this toolset, the project will allow also third parties to act as independent validators of QoS features in IoT applications, enabling new decentralised applications and business models, thus filling a large gap in the emerging edge/IoT computing market landscape. The project validates its results through three use cases which are very relevant for the innovative edge/cloud computing concepts it plans to introduce: namely in manufacturing, mixed reality and smart cities application domains.