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).

Next-generation industrial systems promise to deliver unprecedented excellence not only in terms of performance, but also explainability, trustworthiness and transparency. To achieve this new objectives, state-of-the-art concepts of artificial intelligence (AI), edge-to-cloud (E2C) computing, blockchain, and visualisation need to be de-risked and applied. Motivated by this, TALON aims at sculpturing the road towards the next Industrial revolution by developing a fully-automated AI architecture capable of bringing intelligence near the edge in a flexible, adaptable, explainable, energy and data efficient manner. TALON architecture consists of three fundamental pillars: a) an AI orchestrator that coordinates the network and service orchestrators in order to optimise the edge vs cloud relationship, while boosting reusability of datasets, algorithms and models by deciding where each one should be placed; b) a lightweight hierarchical blockchain schemes that introduce new service models and applications under a privacy and security umbrella; and c) new digital-twin empowered transfer learning and visualization approaches that enhance AI trustworthiness and transparency. It combines the benefits of AI, edge and cloud networking, as well as blockchain and DTs, optimized by means of a) new key performance indicators that translate the AI benefits into insightful metrics; b) novel theoretical framework for the characterisation of the AI; c) blockchain used to deliver personalised & perpetual protection based on security, privacy and trust mechanisms; d) AI approaches for automatically and co-optimising edge and cloud resources as well as the AI execution nodes; e) semantic AI to reduce the learning latency and enhance reusability; and f) digital twins that visualize the AI outputs and together with human-in-the-loop approaches. All the technological breakthroughs are demonstrated, validated and evaluated by means of proof-of-concept simulation and four real-world pilots.

The goal of the project is the creation of the MaaSAI System, a comprehensive digital system to automate and facilitate interactions between suppliers of manufacturing systems (Providers) and manufacturing companies (Consumers) in a Manufacturing-as-a-Service (MaaS) ecosystem, in an agile, efficient and transparent manner. The MaaSAI System aims to revolutionise manufacturing and remanufacturing processes by facilitating access to flexible and decentralised capacities, through the MaaS business model, that extends the principles of the service-oriented economy to the manufacturing industry. The MaaSAI System aims to contribute to the reduction of the heavy investments that manufacturers have traditionally had to make to set up and maintain their operations. With an emphasis on production flexibility, agility and responsiveness, the MaaSAI System enables on-demand sustainable manufacturing, optimising resource utilisation, facilitating the transition towards circular facilities, and promoting better integration of the value chain. The MaaSAI project leverages on Explainable Artificial Intelligence (xAI) in autonomous agents to negotiate the use of manufacturing capacities between providers and consumers in a dynamic MaaS ecosystem. The incorporation of secure, and real-time data exchange between participating companies ensures fast response times and improves collaboration. The MaaSAI System represents a paradigm shift that enables manufacturers to access and utilise on-demand production capabilities, further increasing efficiency and lowering barriers to entry for SMEs. The MaaSAI project will deploy 3 main key exploitable results: 1) MaaSAI Cloud MaaS Marketplace; 2) MaaSAI Provider Suite; 3) MaaSAI. The MaaSAI System will be demonstrated in 5 Pilots of European companies from different sectors: metal machining, gears and mechanical power transmission, biomaterials, food processing, and electronics manufacturing.