In AITeach, an innovative system is being developed that automates the creation of assembly sequence plans and instructions for assembly assistance systems in the context of work preparation in variant-rich assembly. For this purpose, an innovative software system is to be developed that analyzes sensor data using intelligent algorithms and AI methods with regard to the demonstrated activities. The goal is the automatic recognition of manual assembly work steps, an easy-to-understand preparation and presentation of the recognized activities by means of text-based instructions as well as a visualization based on a digital twin.

In Port2Connect (Intelligent Port Logbook for the Efficient and Sustainable Use of Port Infrastructure), a digital port logbook is being developed that increases the transparency and visibility of processes in the port and enables automatic planning and optimisation with artificial intelligence.

processes in the port and enables automatic planning and optimisation with artificial intelligence. Through the intelligent monitoring and assistance system, ships are digitally accompanied and monitored during their stay in the port. In particular, this is intended to achieve the goals for more efficient use as well as sustainable protection against damage to the existing port infrastructure and an improvement in the climate by reducing emissions.

The port logbook is being developed as an example for 2,200 metres of the Stromkaje in Bremerhaven. Various requirements are placed on such a system. These include the recording and allocation of emissions as well as the location of the berths directly on the Weser, which are exposed to the river current and the tidal range caused by the tides. Furthermore, in order to use the berths efficiently, ships in the port must be moved more frequently. In addition, large container ships in particular pose an increased risk of damage to the infrastructure, and it is precisely these ships that account for a large proportion of the port’s total emissions.

In the SYDITIL project, a systemic digital twin (DT) for logistics is being developed. The technological basis is Σ, a language and method for describing complex socio-technical systems, and the WorldLab software. Based on the application scenarios warehouse logistics and port logistics the DT will be developed and evaluated. The intended solution will help to continuously improve the logistics processes. For this purpose, the DT is constantly updated with data gathered from the logistics systems and simulates possible scenarios as well as forecasts upcoming risks. If necessary, the DT sents alerts to control and monitoring systems to optimize logistics operations. In addition, the visualization of simulation and forecast results supports decision-making for future planning.

Stress and scarce attention are the main causes of work accidents in industry, and they affect workers' performance. Physical, physiological, and psychological aspects related to stress have already been studied through the analysis of biometric data, but they have never been integrated with neuro-physiological/psychological data. No-Stress captures the motion of workers while performing manufacturing tasks by real-time collecting neurophysiological parameters and interacting with voice assistance to gather reactions and feedback. The causes of stress will be identified by analyses of workers' data to redesign the work environment with a human-centred approach focused on worker well-being. The project will develop a monitoring system for manufacturing companies to improve their working conditions and production efficiency. The use case providers with worker assistance sector operators will work to redesign manufacturing contexts focusing on workers.

The European production landscape is facing major challenges that require sustainable and robust, but at the same time, highly efficient production systems that have the ability to respond to significant changes at high speed.

The global objective of the project RaRe2 is to create a flexible and resilient ecosystem platform enabled by the interaction of many European organizations that cooperate in the fast reconfiguration of process chains through collaborative systems and adaptable workforce upskilling.

In the project, digital twins of production and logistics systems augmented with forecasting, reconfiguration and optimization functions will be developed at different hierarchical levels along the entire value chain. In addition, methods for flexible and robust workforce planning will be developed. In the next step, the developed methods will be integrated in an ecosystem platform.

This research has been funded by the European Union's Horizon Europe Framework Programme (HORIZON) under project reference HORIZON-CL4-2022-TWIN-TRANSITION-01.

Despite the increasing automation and digitalization of industrial processes, manual activities are still indispensable for companies, as many tasks cannot be fully automated for technical or financial reasons. Ergonomic injuries are one of the most common categories of workplace injuries today. It is estimated that more than 30% of accidents resulting in days away from work are due to ergonomic-related injuries. To reduce physical strain on employees, exoskeletons can be used to assist with non-ergonomic movements. However, selecting the right exoskeleton for a job requires expertise in work planning and medicine, which is not always available, especially in small and medium-sized companies.

In order to quickly and cost-effectively find an initial selection of a suitable exoskeleton, a mobile application is developed with the help of which the performed work steps can be evaluated by video analysis with AI methods and special stresses are described. Subsequently, a suitable exoskeleton for the activity can be suggested on the basis of the stresses identified.

The application is evaluated in terms of ease of use and output results in laboratory tests and compared with currently used checklist-based methods.

The hyBit project plays a important role in the realization of the EU's goal of a climate-neutral economy by means of green hydrogen in a holistic energy transition. The overarching question of the project is: How can climate neutrality be achieved through the targeted technical, economic, ecological, legal and social design of hydrogen hubs? In five steps, pilot applications are defined via flexible modeling of logistics systems that run on hydrogen. For this purpose, transformation paths, infrastructure concepts and roadmaps will first be developed and simulated. The results and simulation performance will be made available to a central transformation platform, which will combine them with the results of other issues beyond mobility and logistics.

NebulOuS will introduce an appropriate meta-operating system that encompasses brokerage capabilities across the cloud computing continuum. Specifically, it will enable the emergence of ad-hoc fog brokerage ecosystems that exploit IoT/edge and fog nodes, in parallel to multi-cloud resources to cope with the requirements of hyper distributed applications. Such applications will be managed by NebulOuS considering the full life-cycle support of edge and cloud resources to enable hosting nodes across organisational units of the same or different business entities or reach private datacentres of telecom providers, constituting ad-hoc cloud computing continuums. BIBA will integrate the research on distributed digital twins and their orchestration in the cloud computing continuum and employ the NebulOuS solution in an application scenario in the domain of crisis management and communication.

Intelligent and interactive AR-based assistance systems have great potential for supporting intralogistics work processes. Still, they have only been used occasionally in this form in practice, especially in SMEs.

The object of the AR Improve research project is intelligent and interactive AR assistance systems that combine current AR hardware with sensor technology and image-processing methods.

By providing an interactive guide, which is being developed together with SMEs, decision-makers can make well-founded decisions about the needs-based and human-oriented use of AR assistance systems without detailed knowledge of AR technology.

Fablabs Erasmus+ project aims to develop training material for supporting Fablab users, and Fablab Tutors/Teachers, including contents for design, coding and manufacturing with main focus on IoT, 5G, AI/Big Data and Blockchain technologies. The main project objectives include:

 

• Development of learning and teaching strategies and concept/guidelines for FabLabs mainly oriented to IoT related technologies like Blockchain and AI/Big Data.
• Development of didactic methods covering several target groups (University degree studies and general public), - development of learning material (blended learning including e-learning, face-to-face, workshops).
• Development of curriculum for training of design, manufacturing of prototypes using IoT, and AI/Big data technologies applied to industry or similar.
• Organization of training activities for tutors.
• Test of the learning material and tutorial during testing initiatives (courses).
• Optimization of learning content for tutors.

  Within this project the learning content will be developed with a learner-centered approach and using case studies from selected branches of industry (examples) to let learners understand the industrial/practical relevance of the topic and show the linkage of principles and methods with relevant applications. Testing courses/workshops will be run at different targets (from apprenticeship to University level) for integrated testing, assessment and optimization of developed tools and contents.

 

Freight transport in Germany today is mainly carried out by road and rail. However, the further increase in transport volumes is pushing the systems to their limits, as evidenced by increased congestion and more frequent delivery delays, among other things. Another challenge is the high environmental impact of road and rail transport. A lower-emission alternative and supplement to land-based transport is water-based freight transport by inland waterway vessel. The increased use of this mode of transportation requires the provision of additional decentralized transhipment points (so-called MicroPorts) for the intelligent and efficient linking of land- and water-based freight transport.

The aim of the project is the technical design of a network of decentralized transhipment hubs for linking land- and waterborne freight transport. The basic idea is to use existing infrastructure, especially bridges, for the installation of the MicroPorts. Based on this, a simulation-based evaluation will be carried out to assess the new transhipment concept's economic efficiency and sustainability. The expected project results thus provide the basis for the planning and implementation of decentralized transhipment points for combined land- and water-based freight transport in the future.

The aim of the project is to develop an off-the-shelf system for the most intuitive access possible to complex information through natural and low-threshold communication in combination with optical image recognition processes. At the same time, it shall provide experienced users with direct, fast and contactless access to a wide range of functions. For data protection reasons, personal image and voice data will be processed by an integrated computing unit.

Many production processes in industry are changing towards cyber-physical systems in which physical and computational elements as well as human operators are interconnected. As a result, human work in production is undergoing profound changes toward collaboration with automated and autonomous systems and their monitoring. In such hybrid cyber-physical production systems (CPPS), the quality of collaboration and interaction between the human operator and technical systems is a key success factor.

Hybrid CPPS require an integrated system design consisting of technical, organizational and human-centered viewpoints to ensure their successful implementation and usability. Consequently, the goal of the project is to contribute to the integration of human factors in hybrid CPPS. Interdependencies between the quality and performance of human work and the design of hybrid CPPS are determined and used to derive design principles for planning and redesign of work systems.

A demonstrator is to be built that serves as a platform for conducting studies with participants within a model hybrid CPPS. It contains several workstations that represent different processing steps and can be used flexibly as manual or automated workstations. Thus, different variants of hybrid CPPS can be modeled and investigated with regard to their effects on the system performance and on the operators. The results are used to determine the underlying relationships between different design variants and key figures for system performance and the perception of work.

The cargo handling environment in ports is characterized by the handling of heavy and large loads, in which humans are essential despite the progress of automation. In the specific application of automobile handling, the vehicles are prepared for the respective target market in technical centers. For this purpose, tires and trailer couplings, for example, have to be moved and mounted by humans. In addition, there is a large number of additional car parts that have to be picked and, in some cases, assembled in an overhead position. As a result, a high physical strain is placed on the employees, which leads with increasing age to a degree of physical impairment. Within the scope of the project MEXOT, the challenges identified are addressed with a socio-technical development approach. To this end, the use of exoskeletons is targeted, aiming to research on intelligent work ergonomics, which examines human-machine interaction in combination with exoskeletons and automated guided vehicles (AGVs). Motion sensors will be integrated into a passive exoskeleton to track the movement patterns of the employees. First, this information is used to enrich data for an external incentive system that rewards employees for wearing the exoskeleton correctly and integrates gamification approaches to increase motivation. In a second step, the data and process information are used to activate or deactivate individual "elastomeric muscles", aiming at a higher wearing flexibility for activities that do not require physical support. In the third step, the movement information of the exoskeleton will be used to develop a sophisticated pick- and assembly-by-motion concept, which, in combination with the camera system of the AGV, enables the registration of individual work steps in picking and assembly. For the AGV, further research is conducted on increasing productivity and reducing the workload of employees through process-specific and worker-individualized material supply. Moreover, voice- and gesture-based functionalities are implemented for human-machine interaction with the AGV.

This project aims at reducing technological barriers towards using a fleet of robots in warehouses and conventional manufacturing environments. In this project, learning material was created to upskill university students and professionals in advanced autonomous navigation concepts, specifically how to leverage existing open-source software libraries on mobile robot platforms. From the end-user perspective, our education materials helped industries using mobile robot solutions to perform complex debugging/maintenance without overly relying on their third-party supplier. This will save time spent tuning motion planning libraries without fully understanding the effect of underlying hyperparameters. In the second phase, the project will be expanded to include collaboration with manipulators to implement an efficient interface based on ROS2 components using the e.DO Cube from COMAU as an example. Based on this example, additional learning units related to ROS2 will be created to establish a fully comprehensive intralogistics scenario.

Herausforderungen bei der Nutzung von Cloud-Technologien und verteiltem Edge Computing für eine tragfähige IoT-Plattform bestehen darin, hochaufgelöste Daten verfügbar zu machen und zu verarbeiten und dort mit KI-Modellen zu verknüpfen. Der Modellbildung kommt hierbei eine besondere Bedeutung zu, da das Verhalten der Systeme in einem komplexen Varianten-raum beschrieben werden muss, und es dabei auch kontinuierliche Veränderungen über eine Lebensdauer von 20 Jahren zu berücksichtigen gilt. Klassische IoT Plattformen und Strukturen, wie sie bereits u.a. in der Windenergiebranche eingesetzt werden, können die Dynamik des tatsächlichen Lebenszyklus von komplexen Produktsystemen wie Windenergieanlagen (WEA) nur unzureichend abbilden. Insbesondere unter Einbeziehung eines modularen WEA-Ansatzes ist die monolithische Errichtung von digitalen Zwillingen nicht ausreichend. In diesem Vorhaben soll daher ein flexibles, dezentrales Konzept für sogenannte „Micro Digital Twins“ (MDTs) entwickelt und gemeinsam mit dem Verbundpartner Nordex implementiert werden. Dabei wird besonderes Augenmerk auf universelle Anwendbarkeit in der Domäne und eine hohe Anpassungsfähigkeit des Konzeptes an die Weiterentwicklung des Standes der Technik gelegt.

The aim of the project is to develop an intelligent modular end effector-component protection system for safe and intuitive cooperation between humans and robots. The protection system is to consist of a protective cocoon that is installed on the end effector and encloses both it and the component. In this way, people in the direct cooperative working area of the robot can be protected from hazards. Integrated sensors will detect people in the vicinity at an early stage and automatically keep them at a distance. With the help of intelligent control strategies, robot movements should be possible both spontaneously (avoidance) and anticipatory, so as not to interrupt processes unnecessarily. Interaction modules are to intuitively indicate the robot's next movement so that the movement can be anticipated and also serve as an interface for the input of commands. Thus, for the first time, humans can also react intuitively to the robot, which increases work safety and minimizes safety-related interruptions.

In the project, a vocational education and training platform for employees in logistics is to be developed on the basis of an existing platform (MARIDAL), which enables demand-oriented and individual learning and offers flexible learning paths. The platform envisaged in this project is a digital learning ecosystem. AI is used to personalise the user experience and support learning. In addition, certificates can be issued - also for external persons.

Icing on rotor blades of wind turbines leads to downtimes every year and thus to considerable financial losses. The "EisAuge" project aims to develop a camera-based ice detection system to reduce these downtimes. The captured RGB and infrared images are analyzed by modern artificial intelligence (AI) methods to determine the current icing condition on the turbine rotor blades. The captured images and the model outputs are then stored in a cloud solution.

BIBA is developing the camera system in this project. The goal here is a camera system that can capture sharp, detailed images both during the day and at night. In addition, BIBA is supporting the development of the AI algorithms in the project. For this purpose, modern methods of image processing, like for example Convolutional Neural Networks (CNNs), are utilized. The focus here is in particular on the transferability of the models to new wind turbines.

In this project we develop an unmanned aerial system (UAS) for automatic inventory recording and quality inspection of pallet contents in indoor block warehouses. The UAS should be able navigate autonomously through the block warehouse without the need for separation from humans or other autonomous systems. Calculations that require large computational effort, like e.g. image processing, are shifted to a mobile server, which is positioned inside the warehouse and comes with its own Wi-Fi network. This enables the use of more cost-effective drones and improves the scalability of the system.

The PassForM research project creates a modularly reconfigurable assembly station. It allows for a more flexible design of manual and hybrid assembly stations and systematic automation, which improves scalability, re-usability and responsiveness to market developments. Bidirectional information and control instruction exchange enable and ease the integration of the modular assembly stations into existing assembly organizations. For this purpose, a material supply module, a conveyor module and a robot module are implemented. The goal is to unite the opposing requirements of productivity and flexibility in the assembly area of medium quantities. The project will fill the gap between manual and highly automated processes. The performance of the modular, hybrid assembly system will be evaluated and based on application scenarios in variant assembly groups.

The production of bakery products poses great challenges to process control in order to achieve consistent end product quality, as the main components of the products are natural products. The properties of the natural products strongly depend on the parameters during the growth and harvesting of the raw materials as well as their preliminary processes. The production of baked goods involves the product-specific combination of ingredients and the mechanical production of a dough, which is mostly kneaded. Low quality is often the result of incorrect expert assessment of raw material quality and the selected process parameters. A particular problem here is the process transitions or transfers between the process steps of dough preparation, work-up, fermentation phase, pre-baking phase, intermediate storage and post-baking phase. The aim of the project is to increase the product quality of baked goods. This should be achieved by developing a raw material-specific and cross-process production control system that uses artificial intelligence to improve coordination of the production processes while taking into account the specific parameters of the semi-finished products. The improved coordination of the production processes allows the reduction of rejects (resource conservation and traceability) as well as the planning/calculation of achievable product qualities based on quality raw material models to increase the specific process quality.

TOKIOS aims to integrate innovative methods and techniques from the fields of statistics and artificial intelligence into the integration and system tests of aircraft. The addressed methods are to be applied to offline data, which is of the order of magnitude of big data.

The tasks of the BIBA focus on the development of data integration solutions and the analysis framework in order to be able to set up and use analysis chains in an interoperable manner. Furthermore, the analysis tool is geared towards the needs of the test engineer for future test processes

Agriculture must increasingly address issues of sustainability and quality management. In this context, feed is also becoming increasingly important from a cost perspective. The goal of the project is the realization of a cloud platform for farmers, traders, and feed producers to individually configure feed, produce it according to demand and deliver it just-in-time. In addition to the integration of weather-dependent demand and price forecasts, the focus is on the development of a simulation-based supply chain control with optimization of the life cycle assessment.

TRICK will provide a complete, SME affordable and standardised platform to support the adoption of sustainable and circular approaches: it will enable enterprises to collect product data and to access to the necessary services on a dedicated marketplace, open to third party solutions. TRICK demo will be run in 2 highly complex and polluting domains: textile-clothing as main pilot and perishable food for replication. BIBA focusses on the adaption of the B2B marketplace to the needs for a Circular information management (CIM).

i4Q will provide a complete solution consisting of sustainable IoT-based Reliable Industrial Data Services (RIDS) able to manage the huge amount of industrial data coming from 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 optimisation; based on a microservice-oriented architecture for the end users. With i4Q RIDS, factories will be able to handle large amounts of data, achieving adequate levels of data accuracy, precision and traceability, using it for analysis and prediction as well as to optimise the process quality and product quality in manufacturing, leading to an integrated approach to zerodefect manufacturing.

i4Q Solutions will efficiently collect the raw industrial data using cost-effective instruments and state-of-the-art communication protocols, guaranteeing data accuracy and precision, reliable traceability and time stamped data integrity through distributed ledger technology. i4Q Project will provide simulation and optimisation tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency and optimal manufacturing quality.

BIBA focuses on: 1) the creation of data quality guidelines for manufacturing and 2) the extension of its software QualiExplore to support a) production data quality knowledge, and b) production line certification under the aspect of data quality. The extension of QualiExplore includes the integration of a digital assistant (conversational AI).

ENHANCE aims at strengthening the cooperation between 3 EU and 4 PC universities across recent research outcomes related to MPQ 4.0. From a capacity-building perspective, this consortium will improve the capacity of HEI in PC with innovative programmes. It will develop new competencies and skills to transfer later to socio-economic partners. ENHANCE will guarantee the sustainability of the consolidated learning programs and materials through the creation of 2 new DIH in PC.

ACROBA project aims to develop and demonstrate a novel concept of cognitive robotic platforms based on a modular approach able to be smoothly adapted to virtually any industrial scenario applying agile manufacturing principles. The novel industrial platform will be based on the concept of plug-and-produce, featuring a modular and scalable architecture which will allow the connection of robotic systems with enhanced cognitive capabilities to deal with cyber-physical systems (CPS) in fast-changing production environments. ACROBA Platform will take advantage of artificial intelligence and cognitive modules to meet personalisation requirements and enhance mass product customisation through advanced robotic systems capable of self-adapting to the different production needs. A novel ecosystem will be built as a result of this project, enabling the fast and economic deployment of advanced robotic solutions in agile manufacturing industrial lines, especially industrial SMEs. The characteristics of the ACROBA platform will allow its cost-effective integration and smooth adoption by diverse industrial scenarios to realise their true industrialisation within agile production environments. The platform will depart from the COPRA-AP reference architecture for the design of a novel generic module-based platform easily configurable and adaptable to virtually any manufacturing line. This platform will be provided with a decentralized ROS node-based structure to enhance its modularity. ACROBA Platform will definitely serve as a cost-effective solution for a wide range of industrial sectors, both inside the consortium as well as additional industrial sectors that will be addressed in the future. The Project approach will be demonstrated by means of five industrial large-scale real pilots, Additionally, the Platform will be tested through twelve dedicated hackathons and two Open calls for technology transfer experiments.

Research on digitalization and applications of artificial intelligence are advancing rapidly. In spite of excellent results, which are published by publicly funded research projects, there is still a lack of systematic solutions for SMEs that provide robustness and reproducibility of AI and at the same time take the restrictions of software and infrastructure in SMEs into account. The overall aim of the ecoKI project is to close this gap. ecoKI supports SMEs by developing an infrastructure for increasing energy efficiency through AI technologies. ecoKI pursues the following goals:

 

1. Make digitization and AI modules(solutions) more generic in the area of energy efficiency. Moreover, make the available AI and digitalization solutions as easy as possible to use.
2. Reduction of barriers that SMEs encounter when they begin to use of digitization and machine learning solutions for increase energy efficiency.

 

The aim of this project is the development of a cost-efficient and easily deployable indoor positioning system. In contrast to existing approaches a hybrid approach is pursued: Established protocols like Bluetooth Low Energy (BLE) and WiFi RTT (round-trip-time) are combined into a mobile hybrid device. A key factor for deploying BLE-based indoor localization inside shop floors, is the utilization of a mesh network, whereby the BLE-Beacons are also connected to each other. This way the range of the BLE signal can be significantly improved, opening the possibility to cover large areas present in shop floors. Furthermore the project aims to implement real-time approaches for data retention on edge-computing platforms with intuitive user interfaces for industrial use.

Humans are at the center of knowledge-intensive manufacturing processes. They must be skilled and flexible to meet the requirements of their work environment. The training of new workers in these processes is time consuming and costly for companies. Many industries suffer from the shortage of skilled workers caused, e.g. by the demographic change. A second challenge for the manufacturing sector is the continuous competition through high quality products. COALA will address both challenges through the innovative design and development of a voice-first Digital Intelligent Assistant for the manufacturing sector. The COALA solution will base on the privacy-focused open assistant Mycroft. It integrates prescriptive quality analytics, AI system to support on-the-job training of new workers, and a novel explanation engine - the WHY engine. COALA will address AI ethics during design, deployment, and use of the new solution. Critical components for the adoption of the solution are a new didactic concept to reach workers about opportunities, challenges, and risks in human-AI collaboration, and a concurrent change management process. Three use cases (textile, white goods, liquid packaging) will evaluate the results in common manufacturing processes with significant economic relevance. We expect to reduce the failure cost in manufacturing by 30-60% with the prescriptive quality analytics feature and the assisted worker training. For the change over time we expect a reduction of 15% to 30% by shortening the worker training time.

The manufacturing of large-scale parts needs the implementation of holistic data management and integrated automation methodology to achieve the desired levels of precision using modular and more flexible equipment. Large-part manufacturing is characterised by a high level of required customisation (built-customer specific). Furthermore, the manufacturing of complex and large-scale parts involves a variety of subassemblies that must be manufactured and assembled first.

This high degree of personalisation implies a great effort in the design and the posterior verification after manufacturing, to achieve high precision. Nevertheless, this customised product-centric design requires an optimisation of the resources of the workshop (i.e. workers, machines, devices) for a responsive, reconfigurable and modular production. In addition, there is the worker-centric approach: performing key labour-intensive tasks while maintaining the industry-specific knowledge and skills of the workers.

PENELOPE proposes a novel methodology linking product-centric data management and production planning and scheduling in a closed-loop digital pipeline for ensuring accurate and precise manufacturability from the initial product design. PENELOPE is built over five pillars for developing a common methodology and vision deployed in four industrial-driven pilot lines in strategic manufacturing sectors: Oil&Gas, Shipbuilding, Aeronautics and Bus&Coach; with potential replicability to further industrial sectors. Moreover, it will be set up a pan-European network of Didactic Factories and showrooms, providing training and upskilling capabilities enabling the workforce transition towards Industry 4.0 and multi-purpose testbeds, for assisting the dedicated industry adaption. PENELOPE envisions to highly-increase EU manufacturing sector competitiveness by increasing production performance, quality and accuracy while ensuring workers’ safety and resource efficiency.

ASSURED’s vision is to introduce a ground-breaking policy-driven, formally verified, runtime assurance framework in the complex CPS domain. As the demand for increasingly autonomous CPSs grows, so does the need for certification mechanisms to ensure their safety. Current methods towards software and system validation requires exhaustive offline testing of every possible state scenario PRIOR to fielding the system. In this context, novel assurance services ensure that the control output of such controllers does not put the system or people interacting with it in danger, especially in safetycritical applications as the ones envisaged in the ASSURED Demonstrators. ASSURED leverages and enhances runtime property-based attestation and verification techniques to allow intelligent (unverified) controllers to perform within a predetermined envelope of acceptable behaviour, and a risk management approach to extend this to a larger SoS. ASSURED elaborates over the coordination of deployed TEE agents in horizontal scope, encompassing numerous technologies applicable to everything from edge devices to gateways in the cloud. Such technologies DICE for binding devices to firmware/software, trusted execution environments, formal modelling of protocols and software processes, software attestation, blockchain technology for distributed verification of transactions between system elements and controlflow attestation techniques for enhancing the operational correctness of such devices. In this frame, we consider the mutual verification of system components in distributed multi-operator environments. Our approach ensures a smooth transition and advancement beyond current strategies where security management services are considered in an isolated manner relying on traditional perimeter security and forensics in a “catch-and-patch” fashion without dwelling on the safety of the overall network as a whole, to holistic network security services capable of minimizing attack surfaces.

The project “QualifyAR” is to saims Accordingly, the use of digital and individual learning environments is pursued, intended to improve learning success and prepare the later use of digital assistance systems in the productive process. In cooperation with Radisumedia GmbH, an AR-based learning environment with context-sensitive information provision and automated learning success and quality testing is being developed. Using an AR framework and predefined process databases, teachers should be able to map teaching tasks independently digitally.

In the funded project "compARe", an AR-based technical assistance system is developed that uses image processing methods to support service technicians in the maintenance of wind turbines. The project will focus on tasks that only allow defect detection by comparing the current status with a previously documented status or a target status. Thus, the system can help avoid damage to the WTG and increase maintenance measures' efficiency.

Employing AI-based image processing methods, such as Convolutional Neural Networks (CNN), defects in components can be detected, classified, and evaluated. Furthermore, the comparison of component states based on historical data is possible. Mobile assistance systems have proven to be very promising for the support of service technicians in wind energy. The use of these computing-intensive image processing methods on mobile devices is a challenge. However, it offers great potential in combination with mobile Augmented Reality (AR) technology. In this way, virtual information on the change of component conditions can be provided directly about the components concerned in the field of vision of the service technicians.

Motivation

The results from Isabella generate first improvements of the initial situation and show further starting points for additional improvement. Our motivation is to take up these points and further improve the logistic performance of the control algorithm and to optimize it according to the specific situations. Moreover, an extension of the applicability of the control algorithm to the transhipment processes at different transport modes offers great potential to further improve the overall performance. In addition, it must not be ignored that the introduction of the solution approaches will be accompanied by radical changes in the work situations for the employees. Therefore, we are furthermore motivated to integrate the employees into the development of the new solutions such that overall we gain better acceptance of the final solution.

Objective

The aim is to optimize the parameterization of the control algorithm and to extend the approach regarding multi-criteria optimization so that the optimization performance can be further improved taking into account the prevailing situation such as terminal filling level, vehicle mix, personnel availability, etc. A further goal is the systematic extension of the control algorithm to the processes for loading and unloading the modes of transport (ship, train and truck) and the creation of a virtual training application. It will take up the psychological aspects regarding work and organization that result from the process redesigns, facilitate the changeover for the employees and finally ensure the acceptance of the new solution.

Approach

By means of event-discrete simulation, we will investigate the performance of the control algorithm under different environmental conditions and parameter settings. To this end we will use methods of sensitivity analysis and artificial intelligence and aim to draw conclusions between performance, terminal situation and parameter settings. As a result, it will be possible to adjust the control algorithm to the respective terminal situation and to increase the predictability of the operative processes. In addition, new data analysis methods and artificial intelligence approaches will be applied to systematically derive relevant process parameters from operationally acquired data, such as the duration of individual process steps or track utilisation. For the extension of the applicability of the control system to the modes of transport (train, ship, truck), a concept for data reception in ships and railway wagons will be designed. To this end, we will consider ad-hoc and mesh networks in combination with suitable radio standards such as WLAN, Bluetooth or LoRa.

ePIcenter will create an interoperable cloud-based ecosystem of user-friendly extensible Artificial Intelligence-based logistics software solutions and supporting methodologies that will enable all players in global trade and international authorities to co-operate with ports, logistics companies and shippers, and to react in an agile way to volatile political and market changes and to major climate shifts impacting traditional freight routes. This will address the ever-increasing expectations of 21st century consumers for cheaper and more readily available goods and bring in Innovations in transport, such as hyperloops, autonomous/robotic systems (e.g. “T-pods”) and new last-mile solutions as well as technological initiatives such as blockchain, increased digitalisation, single windows, EGNOS positional precision and the Copernicus Earth Observation Programme.

The vision of LEVEL-UP is the development of a holistic operational and refurbishment framework applicable both to new and existing manufacturing equipment to achieve dynamic utilisation and maintenance with upgraded remedial actions for sustainability. The LEVEL-UP solution will be demonstrated in the operational environment of Vertical Lathes, Milling machines, Presses, woodworking, Pultrusion, Extrusion, Inspection and CNC equipment to achieve (i) increased efficiency, (ii) extended lifetime and reliability, and (iii) increased ROIC. To do so, LEVEL-UP will offer a scalable platform covering the overall lifecycle, ranging from the digital twins setup to the refurbishment and remanufacturing activities towards end of life.

The precondition of the sketched vision is the achievement of the interoperability from the data till the service layer. BIBA will provision the semantic mediator for the lifecycle of large industrial equipment. The connections between the data aggregator with the higher ontologies and the Knowledge base will be achieved through semantic models and ontologies

The manufacturing industry is facing major challenges due to increasing global competition, low-cost production in developing countries and scarce raw materials. EIT Manufacturing is an initiative of the European Institute of Innovation and Technology (EIT), in which BIBA is one of 50 core partners.

EIT Manufacturing’s mission is to bring European manufacturing actors together in innovation ecosystems that add unique value to European products, processes, services – and inspire the creation of globally competitive and sustainable manufacturing. To do so, the initiative has six strategic objectives:

 

1. Excellent manufacturing skills and talents: adding value through an upskilled workforce and engaged students.
2. Efficient manufacturing innovation ecosystems: adding value through creating ecosystems for innovation, entrepreneurship and business transformation focused on innovation hotspots.
3. Full digitalization of manufacturing: adding value through digital solutions and platforms that connect value networks globally.
4. Customer-driven manufacturing: adding value through agile and flexible manufacturing that meets global personalized demand.
5. Socially sustainable manufacturing: adding value through safe, healthy, ethical and socially sustainable production and products.
6. Environmentally sustainable manufacturing: adding value by making industry greener and cleaner.

   EIT Manufacturing aims for the following goals by 2030:

   • Create and support 1000 start-ups

   • 60% of manufacturing companies adopt sustainable production practices

   • EUR 325 million investment attracted by EIT Ventures

   • 50 000 people trained and up- or re- skilled

   • Create 360 new solutions

   • 30% of material use is circular

 

Offshore wind energy is a key technology for generating renewable energies. Due to its complex processes regarding installation, operation and service, and therefore relatively high costs, offshore wind energy converters still cannot compete with today’s energy market prices. To create a competitive offshore WEC with a Levelised Cost of Electricity (LCoE) target of €35/MWh ReaLCoE takes a holistic approach and scrutinises costs in each link of the value chain.

As a key element of ReaLCoE, BIBA focusses on the digitisation of future offshore WECs and their adhered value chain. Besides the integration of sensors and the implementation of a condition-based monitoring system, the digital representation of the WECs through a digital twin (“product avatar”) takes a major part in BIBAs contribution to ReaLCoE. Building on this, a concept for predictive maintenance will be developed and realized. Furthermore, BIBA will develop optimised logistic and installation concepts and will conduct various performance simulations for a further reduction of supply chain and installation costs. To validate the concept, a technology platform for a first prototype of a digitised 12+MW turbine as well as a pre-series array of 4-6 WEC will be installed, demonstrated and tested.