EdgeAI is as a key initiative for the European digital transition towards intelligent processing solutions at the edge. EdgeAI will develop new electronic components and systems, processing architectures, connectivity, software, algorithms, and middleware through the combination of microelectronics, AI, embedded systems, and edge computing. EdgeAI will ensure that Europe has the necessary tools, skills, and technologies to enable edge AI as a viable alternative deployment option to legacy centralised solutions, unlocking the potential of ubiquitous AI deployment, with the long-term objective of Europe taking the lead of Intelligent Edge. EdgeAI will contribute to the Green Deal twin transition with a systemic, cross-sectoral approach, and will deliver enhanced AI-based electronic components and systems, edge processing platforms, AI frameworks and middleware. It will develop methodologies to ease, advance and tailor the design of edge AI technologies by co-ordinating efforts across 48 of the brightest and best R&D organizations across Europe. It will demonstrate the applicability of the developed approaches across a variety of vertical solutions, considering security, trust, and energy efficiency demands inherent in each of these use cases. EdgeAI will significantly contribute to the grand societal challenge to increase the intelligent processing capabilities at the edge.

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.

European industry has been very competitive on the global markets by utilizing highly efficient Artificial Intelligence (AI) tools and massively producing high quality products. The advent for mass customization has been stressing the capability of modularization and flexibility of production processes through the incorporation of AI technologies. However, the communication between the different automation systems has not been yet accomplished efficiently since they lack interoperability and are restricted to their own system of coordination. The MAS4AI proposal proposes a system that allows the deployment and synchronization of different AI agents in manufacturing for autonomous modular production and human assistance. The MAS4AI system will be heavily driven by large industrial cases and will aim towards digitalising European industry with AI tools according to the Industry 4.0 paradigm. MAS4AI will develop its overall ambition by the means of four Scientific and Technological objectives namely: a) Multi-Agents-System (MAS) for distributing AI components in different hierarchy layers, customers and suppliers for realising refurbishment activities, b) AI agents using Knowledge-based Representation with Semantic Web Technologies, c) AI Agents for hierarchical planning of production processes, d) model-based Machine Learning (ML) AI agent. MAS4AI research and technological activity will be strongly driven by a set of industrial use cases which will be then used as demonstrators. The demonstrators involve important industrial sectors of high value added for Europe, namely AI technologies used for automotive, contract manufacturing, bicycle industry, bearings production and wood processing industry.

The vision of RECLAIM is to demonstrate technologies and strategies to support a new paradigm for refurbishment and re-manufacturing of large industrial equipment in factories, paving the way to a circular economy. Its ultimate goal is to save valuable resources by reusing equipment instead of discarding them. RECLAIM will support legacy industrial infrastructures with advanced technological solutions with built-in capabilities for in-situ repair, self-assessment and optimal re-use strategies. It will establish new concepts and strategies for repair and equipment upgrade and factory layouts’ redesign in order to gain economic benefits to the manufacturing sector. The technological core of RECLAIM is a novel Decision Support Framework that guides the optimal refurbishment and re-manufacturing of electromechanical machines and robotics systems. The framework uses IoT sensors, novel prediction, and process optimisation techniques to offer machine lifetime extension and thus increased productivity. Innovative fog computing and augmented reality techniques are combined with enhanced health monitoring and failure inspection and diagnosis methodologies that enhance the effective use of materials, improve maintenance capabilities and eventually, drastically increase the return of investments (ROI). RECLAIM re-use approach also fosters servicing and upgrading of legacy equipment. For that, European machinery industry will move from an equipment-based business to a value-added business, where equipment servicing and equipment knowledge are main business drivers. RECLAIM solution will be demonstrated in five real industrial environments to evaluate the lifecycle of the industrial equipment and show the feasibility of the approach for integration and scale-up to other industrial sectors. Having RECLAIM technology available, drastically increased efficiency, lifetime extension and high economic benefit will be achieved and a significant step towards 100% re-use will be made.

Europe has a lack of intellectual property in integrating artificial intelligence (AI) into digital applications. This is critical since the automatization reached saturated levels and AI in digitisation is an accepted approach for the upcoming transformation of the European industry. The potential of AI in economy and society is by far not enough exploited. Potential users of AI are not sufficiently supported to facilitate the integration of AI into their applications. Enabling of performance, industry and humanity by AI for digitising industry is the key to push the AI revolution in Europe and step into the digital age. Existing services providing state of the art machine learning (ML) and artificial intelligence solutions are currently available in the cloud. In this project, we aim to transfer machine learning and AI from the cloud to the edge in manufacturing, mobility and robotics. To achieve these targets we connect factories, processes, and devices within digitised industry by utilizing ML and AI for human machine collaboration, change detection, and detection of abnormalities. Hence, we gain knowledge by using existing data and arrange them into a processable representation or collect new data. We use this knowledge to change the semantics and the logical layer with a distributed system intelligence for e.g. quality control, production optimization. In AI4DI, we define a 7-key-target-approach to evaluate the relevance of AI methods within digitised industry. Each key target represents a field of activity and the corresponding target at the same time, dividing systems into heterogenous and homogenous systems, and evolving a common AI method understanding for these systems as well as for human machine collaboration. Furthermore, we investigate, develop and apply AI tools for change detection and distributed system intelligence, and develop hardware and software modules as internet of things (IoT) devices for sensing, actuating, and connectivity processing.