New Intralogistics warehouse system
AKAMAI team develop a novel Automated Storage and Retrieval System (ASRS) solution, managing non-standard loads efficiently in compact warehouses. This EIT project focuses on an innovative system of vertical displacement (specific elevator) combined with proprietary Autonomous Mobile Robots (AMR) to provide higher density than existing industrial solutions.
Duration 01.01.2022 - 31.12.2022, Funded by EU - EIT Manufacturing
- D. Schweers () (Project manager)
- J. Wilhelm ()
Intelligent work ergonomics using sensory exoskeletons and autonomous transport systems for enhanced human-technology interaction in automotive cargo handling
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.
01.01.2022 - 31.12.2024,
Funded by BMDV
- C. Petzoldt () (Project manager)
ROS-based Education of Advanced Motion Planning and Control
This project aims at reducing technological barriers towards using a fleet of robots in warehouses and conventional manufacturing environments. This project creates learning material 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 end-user perspective, our education materials will help 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 being fully aware of the effect of underlying hyperparameters.
01.01.2022 - 31.12.2022,
Funded by EU - EIT Manufacturing
- T. Sprodowski () (Project manager)
Palletized Loads Automatic Loading System for unmodified European Trailers to enable a Resilient Supply Chain
The manufacturing facilities in Europe are mostly fully automated with minimum touch on pallets from production all the way up to the docks but the last mile of action, i.e. loading operation remains fully manual with no flexibility to decide on how to execute this task (automated or manual). This makes it a weak link in the supply chain, which is prone to disruption (especially as learnt in COVID pandemic situation) as it is fully dependent on human presence to execute a labor intensive and less ergonomic task. Hence true supply chain resilience cannot be achieved until there is a solution developed to automatically load palletized goods with on the road (un-modified) European trailers.
The main reason why this task is still conducted manually is the non-standard trailer fleet in Europe and the lack of no automatic solution available for curtain trailers. Given that curtain trailers comprise at least 80% of on the road trailers there is a huge opportunity with high scalability for a solution.
However, currently existing solutions for automatic loading of pallets only work for loading rigid-walled trucks, which are characterized by rigid, nondeformed walls. In contrast, for loading of curtain trailers, such systems fail due to the varying conditions of curtain trailers and less defined walls resulting in these systems to crash into obstacles like carrier beams causing damaged loads or resulting in emergency stops. Consequently, this activity aims to enable an existing automatic loading solution (Nalon) of the company Duro Felguera to tackle the challenges associated with automatically loading curtain trailers from the rear side.
01.01.2022 - 31.12.2022,
Funded by EU - EIT Manufacturing
- L. Rolfs () (Project manager)
Realization of a barrier-free assistance system for the step-by-step execution of work tasks
The aim of the research project is to support people with learning, physical and/or mental disabilities in carrying out work tasks independently by means of an assistance system with a mobile device. For this purpose, an application is being developed that offers barrier-free information and instructions for individual work steps.
The assistance system is supplemented by a task portal that enables companies to create their own tasks in a database, divide them into work steps, and link them to media content.
Duration 01.01.2022 - 31.12.2022, Funded by AVIB
- B. Knoke () (Project manager)
Die Lernfabrik vermittelt Studierenden auf Grundlage des
didaktischen Konzeptes des forschenden und handlungsorientierten Lernens die Entwicklung energieautarker Produktionssysteme. Die Lernfabrik adressiert sowohl Lernziele der Produktionsplanung und -steuerung als auch der erneuerbaren Energieerzeugung, -speicherung und -nutzung. Studierende fertigen dabei reale Produkte, die in gesellschaftlichen und/ oder industriellen Anwendungen eingesetzt werden.
01.12.2021 - 31.10.2022,
Funded by BREDE-Stiftung
- M. Burwinkel () (Project manager)
Entwicklung und operativer Einsatz von Micro Digital Twins zur Betriebs- und Lebensdaueroptimierung von Windfarmen durch prädiktive Datenanalyse
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.
Duration 01.10.2021 - 30.09.2023, Funded by BMWK
- M. Stietencron () (Project manager)
Intelligent end effector component protection for safe human-robot collaboration and coexistence
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.
Duration 01.10.2021 - 30.09.2023, Funded by BMWi
- A. Heuermann () (Project manager)
Smart Learning in Logistics
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.
Duration 01.09.2021 - 31.08.2024, Funded by BMBF
- H. Duin ()
Ice detection on wind turbines using AI-assisted image processing
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.
Translated with www.DeepL.com/Translator (free version)
16.07.2021 - 31.03.2023,
Funded by Land Bremen / EFRE
- M. Kreutz () (Project manager)
Unmanned aerial system for inventory recording and quality inspection of pallet contents in indoor block warehouses
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.
Duration 01.07.2021 - 30.06.2023, Funded by BMWi
Automated Specification Tool for AGV Deployment in SMBs
In this research project, a tool is being developed to support the introduction of automated guided vehicles (AGVs). This includes a guided process and requirements analysis, in which the relevant data is determined and automatically compiled in a formated document for quotation requests. In addition, a manufacturer-independent catalog of AGVs available on the market is created, which can be automatically compared with the determined requirements in order to suggest suitable solutions to the user. Finally, the selection can be validated through the connection to a material flow simulation.
15.06.2021 - 30.09.2022,
Funded by Land Bremen / EFRE
- N. Hoppe () (Project manager)
Resource-based process management through flexible use of intelligent modules in hybrid assembly
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.
01.06.2021 - 31.05.2023,
Funded by BMWi / AiF
- J. Wilhelm () (Project manager)
- N. Hoppe ()
Development of a raw material-specific and cross-process production control in medium-sized bakeries using artificial intelligence
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.
Duration 01.06.2021 - 31.05.2023, Funded by BMWi
- A. Ait Alla () (Project manager)
Test Optimierung mittels KI-basierter Observer & Simulationen
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
Duration 01.06.2021 - 31.08.2024, Funded by BMWK
Platform for optimized, automated, and intelligent processes to order and distribute compound-feed and for the re-supply of silos
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.
01.06.2021 - 31.05.2023,
Funded by BMWK
- D. Rippel () (Project manager)
PRODUCT DATA TRACEABILITY FROM CRADLE TO CRADLE BY BLOCKCHAINS INTEROPERABILITY AND SUSTAINABILITY SERVICE MARKETPLACE
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).
Duration 01.05.2021 - 31.10.2024, Funded by EU
- M. Franke ()
Industrial Data Services for Quality Control in Smart Manufacturing
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).
Duration 01.01.2021 - 31.12.2023, Funded by H2020
- S. Wellsandt () (Project manager)
strENgtHening skills and training expertise for TunisiAN and MorroCan transition to industry 4.0 Era
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.
Duration 01.01.2021 - 31.12.2023, Funded by Erasmus+
- Z. Ghrairi () (Project manager)
AI-Driven Cognitive Robotic Platform for Agile Production environments
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.
Duration 01.01.2021 - 30.06.2024, Funded by H2020
- Z. Ghrairi () (Project manager)
- A. Heuermann ()
Development of a research and technology platform "Increasing energy efficiency in production through digitization and AI"
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:
- 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.
- Reduction of barriers that SMEs encounter when they begin to use of digitization and machine learning solutions for increase energy efficiency.
Duration 01.12.2020 - 30.11.2024, Funded by 5. Energieforschungsprogramm
- D. Bode () (Project manager)
Development of a hybrid RTT-/BLE positioning system for efficient asset tracking via mesh-based Beaconing
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.
Duration 01.11.2020 - 31.10.2022, Funded by BMWi
COgnitive Assisted agile manufacturing for a LAbor force supported by trustworthy Artificial Intelligence
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.
01.10.2020 - 30.09.2023,
Funded by H2020
Closed-loop digital pipeline for a flexible and modular manufacturing of large components
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.
Duration 01.10.2020 - 30.09.2024, Funded by H2020
- K. Burow ()
Visual Product Quality Auditing System
ViProQAS addresses a system solution that enables the operative execution of quality inspection processes by means of visual support. For this purpose, the project is rethinking the way of visualization in the sense that the representation should take place through projections on 2D and 3D objects. App-based approaches with video and AR on mobile devices (glasses or tablets), as well as approaches based on virtual reality (VR) are deliberately not pursued, since the project not only aims at visualization, but also at recording and controlling the activities. This is not possible to a sufficient extent with the hardware currently available on the market. In the project, a framework is developed which derives the information for the assistance system from the audit specifications and subsequently forwards the recorded data to the corresponding systems. The innovation here is both the focus on the area of quality processes and the connection of visualization and recording as well as the control and documentation of the processes.
01.10.2020 - 30.09.2022,
Funded by BMWK
- D. Schweers () (Project manager)
- A. Börold ()
Future Proofing of ICT Trust Chains: Sustainable Operational Assurance and Verification Remote Guards for Systems-of-Systems Security and Privacy
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.
Duration 01.09.2020 - 31.08.2023, Funded by H2020
- Z. Ghrairi ()
Development of an AR framework with extended sensor technology to support training and education in the aviation industry
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.
01.07.2020 - 30.04.2023,
Funded by BMWK
- R. Leder () (Project manager)
Optimization of the maintenance of wind turbines by using image processing methods on mobile augmented reality devices
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.
01.07.2020 - 30.06.2023,
Funded by BMWK
- M. Quandt () (Project manager)
Automobile logistics in sea and inland ports: Integrated and user-oriented control of device and load movements through artificial intelligence and a virtual training application
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.
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.
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.
01.07.2020 - 30.06.2023,
Funded by BMVI
- M. Hoff-Hoffmeyer-Zlotnik () (Project manager)
Enhanced Physical Internet-Compatible Earth-frieNdly freight Transportation answER
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.
Duration 01.06.2020 - 30.11.2023, Funded by H2020
- H. Duin ()
AI-based assistance system for concept planning in production and logistics
Intense global competition, shorter product life cycles, and an increasing number of variants require flexible and adaptable, but also economical production and logistics systems. The time-intensive planning process shall be significantly shortened by an assistance system to become faster and more cost-efficient. In the project "INSERT", a prototype of an AI-based assistance system for concept development for logistics and production planning is being developed. This assistance system supports the entire planning process and provides a platform for the development of logistics and production concepts.
15.05.2020 - 30.11.2022,
Funded by Land Bremen / EFRE
Fostering DIHs for Embedding Interoperability in CyberPhysical Systems of European SMEs
The initiative for Fostering DIHs for Embedding Interoperability in Cyber-Physical Systems of European SMEs (DIH4CPS) will help European enterprises overcome innovation hurdles and establish Europe as a world leading innovator of the Fourth Industrial Revolution. DIH4CPS will create an embracing, interdisciplinary network of DIHs and solution providers, focussed on cyber-physical and embedded systems, interweaving knowledge and technologies from different domains, and connecting regional clusters with the pan-European expert pool of DIHs.
Duration 01.01.2020 - 31.12.2022, Funded by H2020
An Open, Trusted Fog Computing Platform Facilitating the Deployment, Orchestration and Management of Scalable, Heterogeneous and Secure IoT Services and cross-Cloud Apps
The vision of RAINBOW is to design and develop an open and trusted fog computing platform that facilitates the deployment and management of scalable, heterogeneous and secure IoT services and cross-cloud applications (i.e, microservices). RAINBOW falls within the bigger vision of delivering a platform enabling users to remotely control the infrastructure that is running, potentially, on hundreds of edge devices (e.g., wearables), thousands of fog nodes in a factory building or flying in the sky (e.g., drones), and millions of vehicles travelling in a certain area or across Europe. RAINBOW aspires to enable fog computing to reach its true potential by providing the deployment, orchestration, network fabric and data management for scalable and secure edge applications, addressing the need to timely process the ever-increasing amount of data continuously gathered from heterogeneous IoT devices and appliances. Our solution will provide significant benefits for popular cloud platforms, fog middleware, and distributed data management engines, and will extend the open-source ecosystem by pushing intelligence to the network edge while also ensuring security and privacy primitives across the device-fog-cloud-application stack. To evaluate its wide applicability, RAINBOW will be demonstrated in various real-world and demanding scenarios, such as automated manufacturing (Industry 4.0), connected vehicles and critical infrastructure surveillance with drones. These application areas are safety-critical and demanding; requiring guaranteed extra-functional properties, including real-time responsiveness, availability, data freshness, efficient data protection and management, energy-efficiency and industry-specific security standards.
Duration 01.01.2020 - 31.12.2022, Funded by H2020
Protocols and Strategies for extending the useful Life of major capital investments and Large Industrial Equipment
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
Duration 01.10.2019 - 30.09.2023, Funded by H2020
- M. Franke ()
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:
- Excellent manufacturing skills and talents: adding value through an upskilled workforce and engaged students.
- Efficient manufacturing innovation ecosystems: adding value through creating ecosystems for innovation, entrepreneurship and business transformation focused on innovation hotspots.
- Full digitalization of manufacturing: adding value through digital solutions and platforms that connect value networks globally.
- Customer-driven manufacturing: adding value through agile and flexible manufacturing that meets global personalized demand.
- Socially sustainable manufacturing: adding value through safe, healthy, ethical and socially sustainable production and products.
- 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
Duration 01.01.2019 - 01.01.2026, Funded by European Institute of Innovation & Technology (E
Next Generation 12+MW Rated, Robust, Reliable and Large Offshore Wind Energy Converters for Clean, Low Cost and Competitive Electricity
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.
01.05.2018 - 31.01.2026,
Funded by EU
Mid- & Small-Sized Enterprises Competency Center Bremen
The Mittelstand 4.0-Kompetenzzentrum Bremen offers support to small and medium-sized enterprises in the Bremen region and surrounding areas, in increasing their digitalization competencies. In particular, employees and managers in the innovation clusters of maritime industry and logistics, wind energy, aerospace, automotive industry, and food and beverage industry are targeted.
The competence center provides interested companies with a range of free services, according to their needs. The entire innovation process is covered, beginning with the assessment of a company’s digitalization potentials, and continuing with the opportunity to experience applications in practice. In parallel companies are given the opportunity to prepare themselves and their employees for the digital world through trainings. If desired, the center also accompanies companies in the implementation of their digital projects to ensure success.
Duration 01.01.2018 - 31.12.2022, Funded by BMWi
- S. Wiesner () (Project manager)
Improvement of Logistics Performance with Cluster-based Decentralized Control in Material Flow Networks
The concept of decentrally controlled production and logistic systems has gained a growing importance as part of Industry 4.0. The previous research activities in this area focused mainly on the development of control algorithms for decision-making and the required information and communication technologies. An additional success factor for decentralized control has also been identified: the topology, i.e. the underlying structure of the material flow network. However, the topology has so far not been considered when developing decentralized control approaches. The project aims at quantifying the influence of the topology of a material flow network on the logistic performance. Furthermore, it is aspired to investigate how control algorithms need to be configured depending on the network structure.
16.08.2017 - 15.11.2022,
Funded by DFG