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.

Improving industrial energy efficiency at European Manufacturing level requires the integration of energy data with advanced optimization techniques to guide a company decision making. E2COMATION intends to address the optimization of energy usage, at multiple hierarchical layers of a manufacturing process as well as considering the whole life-cycle perspective across the value chain. To this purpose, it aims at providing a cross-sectorial methodological framework and a modular technological platform to monitor, predict, evaluate impact of the behavior of a factory across energy and the life-cycle assessment dimensions, in order to adapt and optimize dynamically not only its real-time behavior over different time-scales, but also its strategic and sustainable positioning with respect to the complex supply and value chain it belongs to. Its major objectives are: - Holistic analysis of energy-related data streams for production performance forecasting; - Life-cycle conceptual paradigm applied to digital twinning of factory assets; - Factory-level integrated multi-objective optimization architecture; - Modular and scalable automation platform for distributed monitoring and supervision; - Comprehensive simulation environment enhanced with energy and environmental performance; - Energy Aware Planning and Scheduling tool (EAP&S); - Life Cycle Assessment and Costing tool (LCAC) integrated in a company Decision Support System; - Sustainable Computer Aided Process Planning (s-CAPP); - LCA-driven supply chain management (SCM) and business ecosystem. For E2COMATION to be successful, it is fundamental that the effectiveness of its methodological approach and technological framework is proved in complex industrial scenarios, involving several factories of different sectors. This will be achieved by implementing the project platform in 2 completely different value chains, the food and drink one and the woodworking one, with 5 concurrent industrial use-cases.

DENiM develops an interoperable digital intelligence platform enabling a collaborative approach to industrial energy management. DENiM provides an integrated toolchain to provision advanced digital services including secure edge connectivity leveraging IoT, data analytics, digital twin, energy modelling and automation culminating in the delivery of continuous energy impact assessment, together with energy control and optimisation across existing production facilities, processes and machines. DENiM identifies skills gaps and develops training to build competences to support energy sustainability in smart manufacturing processes through the seamless integration of digital technologies, education and training activities. In view of considering the human factor, DENiM will consider existing and future regulations from a data protection, legal, ethical and energy policy perspectives, which informs the DENiM technological developments and pilot site interventions. In essence, DENiM accelerates energy efficiency transformation in manufacturing systems by enabling the right information and right technology to be available at the right time and in the right form, made accessible to the right people, empowering smart energy efficient decision-making within factories and across entire value chains. DENiM leverages the concept of process integration, taking a holistic approach to energy efficient manufacturing systems management and considers the interactions between the business, technology, infrastructure, and the workforce through the use of engineered systems that integrate both operational technologies and information technologies to accurately identify and map energy flows across the complete manufacturing value chain facilitating the integration of energy efficiency into existing business processes through digitalisation. This will result in a significant reduction of energy across diverse industrial sectors with substantial cost savings derived from optimised operation

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.