The main ambition of Change2Twin is to ensure that 100% of manufacturing companies in Europe have access to 100% of technologies needed to deploy a digital twin. Change2Twin will adopt the best practices developed so far in I4Ms – focus on local support provided by DIHs, keeping FSTP grants as accessible as posThe main ambition of Change2Twin is to ensure that 100% of manufacturing companies in Europe have access to 100% of technologies needed to deploy a digital twin. Specifically, we will focus on three sub-objectives: - Developing and providing a truly end-to-end service to the manufacturing SMEs where the end user receives from its local, trusted party (e.g. a DIH) a thorough analysis of the digitalization potential and a cross-border, multi-stakeholder (involving both components providers and an integrator), and ready-to-use recipe for implementation. - Providing an architecture-agnostic technology marketplace with dedicated knowledge models supporting the entity preparing the recipe for a complete solution in selecting the best components and most suitable providers. - Taking one step back to see the bigger picture and to find the minimal interoperable model facilitating modularity, composability and interchangeability of components used, regardless of the individual architectures or frameworks. Change2Twin will deliver: - A new benchmarked service model facilitating DIHs in providing support to manufacturing companies - A Pan-European marketplace populated with the state-of-the-art service providers that create coverage for end-to-end Digital Twinning solutions - A growing network of DIHs that have adopted the service model and marketplace based on a sustainable business model - An open, widely available toolbox for establishing a new marketplace consisting of software and body of knowledge gathered during the project - 4 Pilots proving the concept and 2 Open Calls for application experiments with a selection and support programme 

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.

i4Q Project aims to provide an IoT-based Reliable Industrial Data Services (RIDS), a complete suite consisting of 22 i4Q Solutions, able to manage the huge amount of industrial data coming from cheap cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. The i4Q Framework will guarantee data reliability with functions grouped into five basic capabilities around the data cycle: sensing, communication, computing infrastructure, storage, and analysis and optimization. i4Q RIDS will include simulation and optimization tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency, leading to an integrated approach to zero-defect manufacturing. The i4Q RIDS will be demonstrated in 6 Uses Cases from relevant industrial sectors and representing two different levels of the manufacturing process: machine tool providers and production companies. i4Q pan-European consortium entails Industrial partners: WHIRLPOOL (White goods manufacturer), BIESSE (Wood industrial equipment), FACTOR (Metal machining), RIASTONE (Ceramic pressing), FARPLAS (Plastic injection) and FIDIA (Metal industrial equipment); Implementers: TIAG (Industrial Communication Protocols and Standards), CESI (Machine tools, Advanced Materials, Micro-technology) and AIMPLAS (Thermoplastic and thermosetting plastic materials); Technology Providers: IBM (Information Technologies Company), ENGINEERING (Software and Services Company), ITI (Information Technologies Institute), KNOWLEDGEBIZ (Information Systems Company), EXOS (Operations Consulting Company); R&D partners: CERTH (Research Institute), IKERLAN (Technological Centre), BIBA (Research Institute), UPV (University), TUBERLIN (University), UNINOVA (Research Institute); Specialist partners: FUNDINGBOX (Exploitation), INTEROP-VLAB (Dissemination), DIN (Standardisation), LIF (Legal).

AIMS5.0, a collaborative Innovation Action aims at strengthening European digital sovereignty in comprehensively sustainable production, by adopting, extending and implementing AI-enabled hardware and software components and systems across the whole industrial value chain to further increase the overall efficiency. Vulnerability of existing supply chains in crisis shows the need for shorter supply chains and for keeping production in Europe. AI enabled fabs will be given more output and higher sustainability, which makes them more competitive on a global scale. New technologies from IoT and based on semantic web ontologies, ML and AI will help to enable the transformation from Industry4.0 to Industry5.0, to create human-centric workplace conditions and to enable the transformation of European industry to climate-friendly production. Above all, sustainability and resilience will be improved. In essence, AIMS5.0 will deliver: - AI-enabled electronic hardware components & systems for sustainable production - AI tools, methods & algorithms for sustainable industrial processes - SoS-based architectures & micro-services for AI-supported sustainable production - Semantic modelling & data integration for an open access productive sustainability platform - Acceptance, trust & ethics for explainable industrial AI leading to human-centered sustainable manufacturing 20 use cases in 9 industrial domains resulting in high TRLs will validate the project’s findings in an interdisciplinary manner. A professional dissemination, communication, exploitation and standardisation will ensure the highest impact possible. For the first time a joint approach for implementing AI and AI-enabled hardware will be developed that overarches different industrial domains. AIMS5.0 will result in lower manufacturing costs, increased product quality through AI-enabled innovation, decreased time-to-market and increased user acceptance of versatile technology offerings. They will foster a sustainable development, in an economical, ecological and societal sense and act as enablers for the Green Deal and push the industry towards Industry5.0. The innovations will leverage the experience of the 53 partners, such as renowned OEMs, Tier-1 and Tier-2 suppliers, technology and application large enterprises and SMEs, supported by academic research specialists in fields like AI, industrial hard-ware and software, decision making and management algorithms. Specific outcomes of the project are - 20% faster time to market, - Participation of disabled people in the factory environment > 5% (in relation to the total number of employees employed in production), - AI based MES capability > 10 %, - Increased user awareness and trust by 10%, - Subsequent reduction of environmental footprint for wafer transport, handling and storage > 20 %, - 50% reduction of time for monitoring industrial equipment. AIMS5.0 is a pan-European initiative to boost industrial competitiveness through interdisciplinary innovations, establishing sustainable ECS value chains and therefore contribute to European Digital Sovereignty addressing urgent issues like Security of Supply, Monitoring and Crisis Response, and Chip Shortage.

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.