Over the last years, production has been shifted from mass production to customization. The conventional production lines, traditionally focused on one product variant or one family of products do show their limitations to cope with the new needs. Moreover, unprecedented worldwide events, such as the recent pandemic crisis, indicated even more the need for flexible production systems that can rapidly switch production to a totally different one (e.g. automotive manufactures had to produce respirators, facemasks etc.). As a response, MASTERLY aims to develop flexible robotic solutions, constituting of modular grippers combined with state-of-the-art robotic technologies, such as mobile, high and low payload industrial and collaborative robots and smart cranes, enhanced with AI driven advanced control and perception capabilities that will allow them to act autonomously, handling a large variety of parts varying in size, shape and material, while being acceptable by both genders of workforce. The developments will focus around the following 5 pillars: 1) Innovative, efficient and low consumption systems for storage, retrieval, conveying and pick-and-place using a multi-disciplinary approach combining technologies 2) Robust handling devices and systems, with integrated –AI driven- advanced control 3) User-friendly interfaces for robot/machine control and programming 4) Interoperable S/W and H/W interfaces 5) Industrial Pilot Cases for work piece handling in full production line The technologies will be tested for flexibility, efficiency & user acceptance in three use cases from different productions sectors, aiming to demonstrate production line and cross sector applicability and adaptability: Elevators manufacturing, focusing on the assembly of electrical cabinets of lifts (KLEEMANN), Sportswear, focusing on warehouse logistics and packaging (DECATHLON) and Aeronautics production, focusing on production of large composite panels of aircraft wings (AERNNOVA).

AGIMUS aims to deliver an open-source breakthrough innovation in AI-powered agile production, introducing solutions that push the limits of perception, planning, and control in robotics, enabling general-purpose robots to be quick to set-up, autonomous and to easily adapt to changes in the manufacturing process. To achieve such agile production, AGIMUS leverages on cutting-edge technologies and goes beyond the state-of-the-art to equip current mobile manipulators with a combination of (i) an advanced task and motion planner that can learn from online available video demonstrations; (ii) optimal control policies obtained from advances in reinforcement learning based on efficient differentiable physics simulations of the manufacturing process; as well as (iii) advanced perception algorithms able to handle objects and situations unseen during initial training. Along the way, optimization of energy efficiency and the use of 5G technology will support further pushing the limits of autonomy. The AGIMUS solutions and their impact will be demonstrated and thoroughly stress-tested in 3 testing zones, as well as 3 industrial pilots in Europe, under numerous diverse real-world case studies and scenarios (different tools, environments, processes, etc.). In every step, and from the very beginning, AGIMUS will go beyond current norms and involve a wide range of stakeholders, starting from the production line itself, to identify the essential ethical-by-design principles and guidelines that can maximise acceptance and impact.

There is a common saying in industry, and more generally in engineering "fast, cheap, reliable, choose two out of three". This saying captures the trade-off between resources, production time, quality and performance that are inherent in every manufacturing process. Conceptually, different points on this "Paretto front" correspond to different production choices and directly affect the competitiveness of an industry. OPTIMAI aims to redefine and optimize this Pareto front through integrating several enabling technologies in a common framework. The starting point for OPTIMAI is the smart instrumentation of production with AI-enabled sensors for quality inspection and monitoring, integrated on a secure middleware layer. To ensure data integrity and traceability, OPTIMAI foresees distributed ledger technology combined with a cyber-security module. Collected data are analysed using AI models for the early detection of defects and upstream causes of deficiencies. OPTIMAI also explores the virtualization of production using digital twins of processes and sensors that combined with AI models trained on production data, form a simulation engine for exploring production scenarios and optimizing production planning. Another innovative point is the rapid reconfiguration of production equipment via automated feedback from quality control results or via a context aware Augmented Reality ecosystem for fast and informed decision making. At the same time OPTIMAI put emphasis on establishing a comprehensive ethics and regulatory framework for the deployment of its technology realizing that a production line is a workplace and as such concerns related to privacy, protection of human rights and safety have to be carefully addressed before any kind of technology is adopted. OPTIMAI foresees an extended pilot phase on three industrial sites covering a representative sample of European industry in order to maximize its impact and facilitate commercial uptake.

The EFPF project realises a federated smart factory ecosystem by primarily interlinking 4 smart factory platforms, from the FoF-11-2016 cluster, through an open and interoperable Data Spine. The federation of the 4 base platforms is complemented by industrial platforms, collaboration tools and smart factory systems, specifically selected to support connected factories in lot-size-one manufacturing. The federated EFPF platform delivers enhances value and reduces the barrier to innovation by providing seamless access to services and solutions that are currently dispersed. In parallel the platform provides the necessary infrastructure, tools and support for novel service creation and validations by third parties. Further, by fostering healthy competition in the smart factory ecosystem, the EFPF platform will ensure that the needs of the evolving smart manufacturing industry are met for the long term. The EFPF federation is offered to the manufacturing and logistic companies as an open platform; to utilise the offered functionality, experiment with innovation approaches and develop custom solutions based on specific needs. The project demonstrates the power of federation through 3 embedded large scale pilots focusing on lot-size-one manufacturing and sustainable value networks in diverse sectors, while a cross sectorial circular economy pilot is also incorporated. The results of the pilots are openly made available as lessons learned and best practices. The creation of the European smart factory ecosystem is supported by offering interested companies technical and financial support. A dedicated budget is allocated to offer financial support to companies who are interested in using the EFPF platform to enhance their businesses. The project is primarily composed of SMEs who have the agility to quickly react and adopt innovative solutions, while experience and market reach of big players such Siemens and others is also leveraged.

Data and services have become the key factor in manufacturing processes. The need to react on dynamically changing market demands is dramatically rising. One of the most imperative problems so far is to connect supply chain data and services between enterprises and to connect value chain data within a factory, so that it can meaningfully support decision-making. COMPOSITION will create a digital automation framework (the COMPOSITION IIMS) that optimizes the manufacturing processes by exploiting existing data, knowledge and tools to increase productivity and dynamically adapt to changing market requirements. This technology acts as the technical operating system for business connections between factories and their suppliers. Furthermore, it opens a new space for third party entities to actively interact in the supply chain, e.g., by providing services to improve cycle time, cost, flexibility or resource usage. In addition to the supply chain improvements, also the processes inside the company will be addressed and optimized. Data across the (multi sided) company internal value chain is integrated by an Integrated Information Management System (IIMS) with optimisation and modelling tools for resource management including innovative, multi-level, real-time cross-domain analytics including a Decision Support System. The technology will be based on extending existing FI-WARE and FITMAN catalogues and LINKSmart® Middleware and adapt the concept of Industrial Data Space. COMPOSITION will implement, demonstrate and validate the system in two multi-sided pilots that show the modularity, scalability and re-configurability of the platform across multiple application domains. The first pilot in the biomedical device domain focuses on the integrated information management system in a multi-sided manufacturing process. The second pilot concentrates on the interaction between different companies using the COMPOSITION ecosystem with the agent-based marketplace for collaboration.