Europe’s 23 million smaller businesses represent 99 % of all businesses and account for around three-quarters of all jobs. Small businesses and mid-caps are a key part of the economy. The EU-funded KITT4SME project is developing scope-tailored and industry-ready hardware, software and organisational kits for European SMEs and mid-caps. The aim is to deliver these as a modularly customisable digital platform that can seamlessly introduce artificial intelligence in their production systems. The project will ensure that the kits are widely distributed to a wide audience of SMEs and mid-caps in Europe. What is more, the seamless adoption of the kits will be facilitated with the use of factory systems like ERP, as well as IoT sensors and wearable devices, robots and other factory data sources.
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…
In recent years, there has been a shift from mass production to customization, presenting new challenges for conventional production lines that were traditionally focused on a single product variant or product family. These limitations have become increasingly evident considering the changing needs of modern production. To address these challenges, MASTERLY aims to develop flexible robotic solutions that can overcome these limitations. The process will involve the use of modular grippers combined with cutting-edge robotic technologies such as mobile, high, and low payload industrial and collaborative robots, and smart cranes. These solutions will be enhanced with AI-driven advanced control and perception capabilities that will enable them to operate autonomously, handling a wide range of parts varying in size, shape, and material, and being acceptable to workers of all genders. The proposed technologies will be tested for flexibility, efficiency, and user acceptance in three use cases from different productions sectors (elevators assembly, sportswear warehouse logistics/packaging and aeronautics production), aiming to demonstrate production line and cross sector applicability, scalability, and adaptability.
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).
Factories with a high interoperability level require an extendable platform to reach the goal of zero defects. The EU-funded ZDMP project is combining state-of-the-art zero-defect technological approaches based on commercial-grade or open-source software, with built-in software for any gaps, and with an open development approach and app store. It will allow end users to connect their system to benefit from the features of the platform, including product and production quality assurance. ZDMP can also simplify processes by connecting existing (and new) devices and sensors, while enabling connections to related information systems and operational assets. The platform will be piloted in the automotive, machine tool, electronics, and construction sectors.