Several products embed different types of electronic components, and they are even more fundamental in some of the European strategic markets (e.g. automotive). However, reference producers come from extra-EU countries in the far-east side of the world (e.g. China and Taiwan). Trying to cope with all these challenges and the current semiconductors crisis, the European Commission (EC) published (and in some cases is still working on) specific EU strategies/directives for automotive, e-waste (e.g. Digital Product Passport) and, specifically, semiconductors (e.g. European Chips Act). However, trying to make these sectors more sustainable, circular and resilient, it is mandatory to boost both EoL strategies (e.g. sorting, reuse, remanufacturing and recycling) and intra-EU production through innovations and investments. The current international scenario represents a good chance to decouple the European economy from both natural resource depletion (e.g. Critical Raw Materials - CRMs) and dependency from extra-EU supplies of strategic products. In order to better prove what the benefits are of a joined circular/resilient use of secondary resources, the automotive and mass electronics sectors have been identified as the reference contexts for establishing a set of innovative solutions. To this aim, the CIRC-UITS project will focus on demonstrate the improvement to the circularity of automotive and mass electronics sectors by reuse of semiconductors from different sources, as well as support the reuse & remanufacturing of semiconductors into new (high added-value) components and products in these sectors.

Despite the indisputable benefits of AI, humans typically have little visibility and knowledge on how AI systems make any decisions or predictions due to the so-called “black-box effect” in which many of the machine learning/deep learning algorithms are not able to be examined after their execution to understand specifically how and why a decision has been made. The inner workings of machine learning and deep learning are not exactly transparent, and as algorithms become more complicated, fears of undetected bias, mistakes, and miscomprehensions creeping into decision making, naturally grow among manufacturers and practically any stakeholder In this context, Explainable AI (XAI) is today an emerging field that aims to address how black box decisions of AI systems are made, inspecting and attempting to understand the steps and models involved in decision making to increase human trust. XMANAI aims at placing the indisputable power of Explainable AI at the service of manufacturing and human progress, carving out a “human-centric”, trustful approach that is respectful of European values and principles, and adopting the mentality that “our AI is only as good as we are”. XMANAI, demonstrated in 4 real-life manufacturing cases, will help the manufacturing value chain to shift towards the amplifying AI era by coupling (hybrid and graph) AI "glass box" models that are explainable to a "human-in-the-loop" and produce value-based explanations, with complex AI assets (data and models) management-sharing-security technologies to multiply the latent data value in a trusted manner, and targeted manufacturing apps to solve concrete manufacturing problems with high impact.

MAASiveTraditional value chains are facing challenges due to the fast-moving markets, customer demands, and unpredictable manufacturing and logistics. To address these challenges, Manufacturing as a Service (MaaS) is introduced as a concept that utilizes existing resources in a value network by connecting manufacturers to service providers on demand through a connected network. The MAASive project aims to develop models of value networks that enable companies to recover from unforeseen external events by connecting to new services and reconfiguring value networks utilizing internal and external manufacturing services. MAASive will provide a toolkit for industry, which will consist of a blend of existing methods and technology applied in the MaaS context, and new models and technology developed as part of the project. Four distinct aspects are addressed in the MAASive project to increase resilience in value networks: network building, impact assessment, reorchestration of networks, and value network operation. The overall aim of MAASive is to increase value network resilience by enabling manufacturers to rapidly respond to unforeseen external events or sudden changes in supply or demand, utilizing manufacturing as a service. MAASive uses an iterative approach to develop technical solutions and identify potential technology risks early on. The project is focused on creating a toolkit from a human-centered perspective and involving professionals and workers in requirement and scenario definition. The iterative approach follows three loops focusing on 1) model foundations, 2) impact simulation and scenarios, and 3) network orchestration and operation. The results of MAASive will be developed in two use case demonstrators. The results of MAASive will contribute to companies being more resilient towards external, unforeseen events, by being able to utilize services in a value network better and faster, while also increasing utilization of network resources.

Real-Time Simulations (RTS) are widely recognised as a means to support the validation process of systems and procedures up to the highest operational readiness levels and are therefore widely used to support V3 validation campaigns in the SESAR context. With the development of new airspace users and new Air Traffic Management (ATM) concepts in recent years and those expected in the near future (e.g. U-Space, Advanced Air Mobility (AAM)), V&V processes have become increasingly complex, with increasing demands on the infrastructures for validating these concepts and operational conditions. A wider diffusion of interoperability between specialised simulators could support the need for improved ATM V&V infrastructures to demonstrate the achievement of validation objectives related to future European ATM concepts. The VISORS project aims at supporting a wide diffusion of interoperability standards among ATM validation platforms. An economic analysis of performing validation processes for ATM/AAM/U-space interoperability concepts and solutions through a multi-site validation architecture will be performed. An experimental demonstration test will be defined and performed to collect data for this analysis. The simulation facilities of different partners of the project will be connected through a prototype platform to develop state-of-the-art interoperability solutions. The security aspect related to data exchange between this platform will be assessed. Furthermore, the impact of this distribution of actors involved in validation activities on state-of-the-art HP assessment methodologies will be evaluated, also considering the possible remote execution of related measurements.

The AI REGIO project aims at filling 3 major gaps currently preventing AI-driven DIHs from implementing fully effective digital transformation pathways for their Manufacturing SMEs: at policy level the Regional vs. EU gap; at technological level the Digital Manufacturing vs. Innovation Collaboration Platform gap; at business level the Innovative AI (Industry 5.0) vs Industry 4.0 gap. POLICY. Regional smart specialization strategies for Efficient Sustainable Manufacturing and Digital Transformation (VANGUARD initiative for Industrial Modcernisation) are so far insufficiently coordinated and integrated at cross-regional and pan-EU level. SME-driven >AI innovations cannot scale up to become pan-EU accessible in global marketplaces as well as SME-driven experiments remain trapped into a too local dimension without achieving a large scale dimension. Regional vs. EU Gap. TECHNOLOGY. Digital Manufacturing Platforms DMP and Digital Innovation Hubs DIH play a fundamental role in the implementation of the Digital Single Market and Digitsing European Industry directives to SMEs, but so far such initiatives, communities, innovation actions are running in a quite independent if not siloed way, where very often Platform-related challenges are not of interest for DIHs and Socio-Business impact not of interest for DMP. DMP vs. DIH Gap. BUSINESS. Many Industrial Data Platforms based on IOT Data in Motion and Analytics Data at Rest have been recently developed to implement effective Industry 4.0 pilots (I4MS Phase III platforms). The AI revolution and the new relationship between autonomous systems and humans (Industry 5.0) has not been properly addressed in I4MS so far. AI I5.0 vs. I4.0 Gap. AI REGIO is following the 4 steps for VANGUARD innovation strategy (learn-connect-demonstrate-commercialize) by constantly aligning its methods with the AI DIH Network initiative and its assets with I4MS/DIH BEinCPPS Phase II and MIDIH / L4MS Phase III projects. AI REGIO: Industry 5.0 for SMEs