Europe’s manufacturers require new methods that permit a comprehensive, cost-affordable and integrated application of circular economy principles for the digital upgrade of the entire production system. The EU-funded LEVEL-UP project will create an ascendable platform to deal with the entire lifecycle of the production process for the diagnosis and forecasting of the operation of physical assets and monitoring the restoration and reparation procedures. LEVEL-UP will include new hardware and software applications connected with IoT and data platforms. It will have a big impact on the European manufacturing sector, increasing material and resource efficiency, extending equipment lifetime and securing a return on investment. LEVEL-UP will be demonstrated in seven demo sites from different sectors.
The MANU-SQUARE project creates a European platform-enabled responsible ecosystem acting as a virtual marketplace, bringing available manufacturing capacity closer to production demand to achieve their optimal matching thus fostering, on the one hand, fast and efficient creation of local and distributed value networks for innovative providers of product-services and, on the other hand, reintroduction and optimization in the loop of unused capacity that would be wasted otherwise.
In a wider perspective the MANU-SQUARE project pursues a paradigm shift that disrupts the traditional static supply chain model and establishes dynamic value networks that can be arranged on-demand to couple the needs of buyers and the availability of sellers of manufacturing capacity. In so doing, this latter becomes an easily and efficiently tradable commodity towards lowered production costs for European companies and improved manufacturing ecosystem actual productivity.
The aim of the MASSI project is to create a solution to effectively support plants of manufacturing companies that are starting their sustainability execution journey, offering them a consultancy service and a comprehensive digital solution.
The solution will leverage on a web-based survey to evaluate the sustainability performance and to understand how and where it is convenient to apply the sustainability efforts. The collected data will be mapped on a “maturity model” that will be explored through the digital platform, allowing plants to have evidences and actionable results that allow to make data-driven decisions regarding sustainability journey and to monitor the adopted initiatives. Read more…
ONE4ALL aims to develop self-reconfigurable mobile collaborative robots embedded with IIOT devices for real-time monitorisation and interconnectivity, able to digotally replicate the processes through data-driven digital twins and controlled by a self-learning AI-based distributed and multidisciplinary decision support system. The higher objective will be to boost manufacturing plants’ transformation, especially SMEs, towards industry 5.0, reinforcing their resilience under unexpected changes in social needs. The potential of ONE4ALL will be demonstrated in two relevant environments from different sectors: agri-food and pharmaceutical.
The ROSSINI project aims to develop a disruptive, inherently safe hardware-software platform for the design and deployment of human-robot collaboration (HRC) applications in manufacturing. By combining innovative sensing, actuation and control technologies (developed by world market leaders in their field), and integrating them in an open development environment, the ROSSINI platform will deliver a set of tools which will enable the spread of HRC applications where robots and human operators will become members of the same team, increasing job quality, production flexibility and productivity.
Thanks to enhanced robot sensing capabilities, the deployment of artificial intelligence to optimise productivity and safety, and natively collaborative manipulation technologies, ROSSINI will deliver high performance HRC workcells, combining the safety of traditional cobots with the working speed and payloads of industrial robots.
Artificial intelligence (AI) systems are increasingly improving the automation of production in the manufacturing sector. But in order for these systems to be trusted and applicable when replacing human tasks in dynamic operation, they need to be safe and adjustable – to react to different situations, security threats, unpredictable events or specific environments. The EU-funded STAR project will rise to this challenge by designing new technologies to enable the implementation of standard-based, secure, safe, reliable and trusted human-centric AI systems in manufacturing environments. The project will aim to research and integrate leading-edge AI technologies like active learning systems, simulated reality systems, explainable AI, human-centric digital twins, advanced reinforcement learning techniques and cyber-defence mechanisms, to allow the safe deployment of sophisticated AI systems in production lines.
Manufacturing has seen many developments over the years, and has been an essential part of most industries. Now, smart manufacturing is set to be the next step in its evolution. This allows increased competitiveness for organisations and increased support throughout the many processes included. Unfortunately, the AI technologies currently used for smart manufacturing lack self-adaptiveness and are mostly tasked with specific predefined settings. The EU-funded TEAMING.AI project aims to make a breakthrough in smart manufacturing. By introducing a new human and AI teaming framework, manufacturing processes will be optimised: the greatest strengths of both these elements can be maximised while safety and ethical compliance guidelines are examined and maintained.
TREASURE (leading the TRansion of the European Automotive SUpply chain towards a circulaR futurE) wants to support the transition of the automotive sector towards Circular Economy (CE) trying to fill in the existing information gap among automotive actors, both at design and EoL stage. To this aim, a scenario analysis simulation tool dedicated to car electronics will be developed and tested with a set of dedicated demonstration actions. The, so implemented, scenario analysis simulation tool will have a multiple perspective.
What is artificial intelligence (AI) and how does it work? For many people, these questions are not easy to answer: this is due to the fact that many machine learning and deep learning algorithms cannot be examined after their execution. The EU-funded XMANAI project will focus on explainable AI, a concept that contradicts the idea of the ‘black box’ in machine learning, where even the designers cannot explain why the AI reaches at a specific decision. XMANAI will carve out a ‘human-centric’, trustful approach that will be tested in real-life manufacturing cases. The aim is to transform the manufacturing value chain with ‘glass box’ models that are explainable to a ‘human in the loop’ and produce value-based explanations.
Maintenance in general and predictive maintenance strategies in particular should now face very significant challenges to deal with the evolution of the equipment, instrumentation and manufacturing processes they should support. Preventive maintenance strategies designed for traditional highly repetitive and stable mass production processes based on predefined components and machine behaviour models are no longer valid and more predictive-prescriptive maintenance strategies are needed.
The Z-Break solution comprises the introduction of eight (8) scalable strategies at component, machine and system level targeting (1) the prediction occurrence of failure (Z-PREDICT), (2) the early detection of current or emerging failure (Z-DIAGNOSE), (3) the prevention of failure occurrence, building up, or even propagation in the production system (Z-PREVENT), (4) the estimation of the remaining useful life of assets (Z-ESTIMATE), (5) the management of the aforementioned strategies through event modelling, KPI monitoring and real-time decision support (Z-MANAGE), (6) the replacement, reconfiguration, re-use, retirement, and recycling of components/assets (Z-REMEDIATE), (7) synchronizing remedy actions, production planning and logistics (Z-SYNCHRONISE), (8) preserving the safety, health, and comfort of the workers (Z-SAFETY).
Manufacturing processes like glass fibre production, precision machining, additive manufacturing and high-temperature metal production are difficult to automate. Tasks are complex and cannot be fully controlled. The EU project COGNIMAN aims at solving this and making manufacturers more productive, efficient, flexible as well as sustainable. 16 partners from six countries develop a novel concept of “digital cognitive smart manufacturing”. COGNIMAN stands for “COGNitive Industries for smart MANufacturing“.
Advances in optoelectronics technologies is causing a revolution in consumer electronic goods, solar energy, communications, LED, industrial laser, and other fields. At present, the optoelectrical manufacturing is facing significant challenges in dealing with the evolution of the equipment, instrumentation and manufacturing processes they support. The industry is striving for higher customisation and individualisation, implying that systems configurations need to change more frequently and dynamically.
IQONIC will offer a scalable zero defect manufacturing platform covering the overall process chain of optoelectrical parts. IQONIC covers the design of new optoelectrical components and their optimised process chain, their assembly process, as well as their disassembly and reintroduction into the value chain. IQONIC will therefore comprise new hardware and software components interfaced with the current facilities through internet of things and data-management platforms, while being orchestrated through eight (8) scalable strategies at component, work-station and shopfloor level. The IQONIC technologies will be demonstrated in 4 demo sites covering a wide range of products and processes.
Furniture manufacturing is big business that accounts for more than 25 % of world furniture consumption. It is also going through a big transformation – from a Do It Yourself (DIY) to a Do It Together (DIT) approach. The EU-funded INEDIT project will create an ecosystem to transform the DIY approach within FabLabs into a professional DIT approach. It will capitalise on the knowledge, creativity and ideas of design and engineering conceptualised by interdisciplinary stakeholders and sometimes even new actors. To demonstrate the potential innovation around social manufacturing within the circular economy, the project will test it in four cross use cases: sustainable wood panels manufacturing and 3D-printing of wood, 3D printing of recycled plastic and ‘smartification’.
Increasing production and decreasing environmental damages is a key target in the digital transformation of production plants. The EU-funded HyperCOG project proposes an advanced industrial cyber-physical system (ICPS) that will increase production performance, limit emissions and energy consumption, and offer lifelong training in digitalisation for workers. The system is built on technological advances accessible in the market. It offers a hyper-connected network of digital nodes that can receive considerable streams of data in real-time. As a result, it can supply industrial plants with awareness and cognitive reason. The impacts of the system on productivity and environment will be verified on three use cases included in the SPIRE scope – in the steel-making, cement and chemical sectors.
Mass production led to steep decreases in the costs of goods for consumers around the world. The ‘lot size one’ (LSO) scenario – literally referring to the production of one item in one lot – seems like the least efficient manufacturing concept around. However, it turns out that mass production has a lot of downsides, including large amounts of stock of both components and finished products that end up in a secondary liquidation market, sold at drastically reduced prices. LSO and Industry 4.0 have come together to produce a product after the customer has been found, a system in which a customer orders exactly what they want and 100 % of the manufactured items are sold. The EU-funded eFactory project is developing a connected factory platform to enable the agile manufacturing and personalisation required for LSO, positioning the EU as an innovation leader on the global stage.
The digitisation of manufacturing processes can offer energy saving solutions. For instance, the optimisation or replacement of specific technologies and the application of new software tools may result in significant reductions in energy consumption. The EU-funded E2COMATION project will address the optimisation of energy usage at various stages of the manufacturing process as well as considering the whole life-cycle perspective across the value chain. To monitor, predict, evaluate and optimise the energy and sustainability impact of the behaviour of a factory, E2COMATION will develop a cross-sectoral methodological framework and a modular technological platform. For production performance forecasting, it will enable a holistic analysis of energy-related data streams, leveraging also on a life cycle conceptual paradigm applied to digital twinning of factory assets.
Manufacturing is one of the largest energy-consuming sectors and responsible for approximately a third of the global energy demand. Therefore, energy management is key to ensuring that manufacturing remains competitive as well as being sustainable as part of the global energy transition. The EU-funded DENiM project is developing an integrated toolchain for the provision of advanced digital services including secure-edge connectivity leveraging the Internet of Things (IoT), data analytics, digital twin, energy modelling and automation. Digital technologies will play a significant role by providing the ability to automatically monitor and optimise energy usage, while continuously informing users about the environmental and economic impact of decisions made at all stages of the manufacturing process.
In the fight against COVID-19, manufacturing and distributing vital medical equipment became a major challenge. Unforeseen spikes in demand for essential medical supplies have been causing a greater urgency for supply chain optimisation and for deploying innovative approaches to scale up flexible and sustainable production methods. To protect European citizens and address the needs of the healthcare sector on short notice, the EU-funded CO-VERSATILE project aims to prepare Europe for managing pandemics by elevating the adaptability and resilience of the manufacturing sector. The goal is to offer manufacturing firms readily available and customisable solutions – accessible via a cloud-based marketplace, Digital Technopole – that enables them to boost the production of medical equipment and respond quickly in times of crises.
The project will deliver a methodology and framework to facilitate information flow exchanges across the extended life cycle chain of Products, their Components, their Materials, and Chemicals data, and their related Circularity, Environmental, Social and Economic Information. This linked data will be governed by secure and reliable management standards to create a product catalog that will be easily accessible via a cloud-based platform. CircThread will implement the system in 3 demonstration clusters in Italy, Slovenia, and Spain rolled out across the entire extended life cycle chain of home appliances (incl. washing machines and dishwashers) and home energy systems (incl. boilers, solar-PV systems, and batteries) to test 7 circularity use cases and associated business models.
Digitalisation represents a new challenge for the European process industries, which need to handle an increasingly wide range of actions. Cognition capabilities will permit the sector to improve its flexibility and performance. The EU-funded CAPRI project will establish, test and demonstrate an advanced cognitive automation platform (CAP) for process industry digital transformation. The platform will help the sector increase its flexibility of operations and improve performance through different indicators and cutting-edge quality control of products and intermediate flows. The CAP will be modular and scalable, allowing the development and integration of advanced applications that address manufacturing challenges in significant process sectors such as asphalt, steel-making and pharma.
EFFRA recommendations on Factories 4.0 and Beyond (Sept 2016) clearly stated the need for development of large scale experimentation and demonstration of data-driven “connected smart” Factories 4.0 to retain European manufacturing competitiveness. BOOST 4.0 will address this need, by demonstrating in a measurable and replicable way, an open standardised and transformative shared data-driven Factory 4.0 model through 10 lighthouse factories. BOOST 4.0 will also demonstrate how European industry can build unique strategies and competitive advantages through big data across all phases of product and process lifecycle (engineering, planning, operation, production and after-market services) building upon the connected smart Factory 4.0 model to meet the Industry 4.0 challenges (lot size one distributed manufacturing, operation of zero defect processes & products, zero break down sustainable operations, agile customer-driven manufacturing value network management and human centred manufacturing).
