exoMATCH

Intelligent Exoskeleton/Task Matchmaking

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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.

Duration 10.10.2022 - 31.03.2023, Funded by EU

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hyBit

Hydrogen for Bremen’s industrial transformation

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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.

Duration 01.09.2022 - 28.02.2026, Funded by BMBF

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Projektlogo A META OPERATING SYSTEM FOR BROKERING HYPER-DISTRIBUTED APPLICATIONS ON CLOUD COMPUTING CONTINUUMS

NebulOuS

A META OPERATING SYSTEM FOR BROKERING HYPER-DISTRIBUTED APPLICATIONS ON CLOUD COMPUTING CONTINUUMS

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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.

Duration 01.09.2022 - 31.08.2025, Funded by EU

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Projektlogo Entwicklung eines Leitfadens für den bedarfsgerechten Einsatz AR-basierter Assistenzsysteme in der Intralogistik

AR Improve

Development of a guideline for the human-oriented use of AR-based assistance systems in intralogistics

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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.

Duration 01.09.2022 - 31.08.2024, Funded by BMWi / AiF
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Projektlogo Potentialanalyse eines multimodalen Umschlagsystems für den direkten oder indirekten Warenumschlag zwischen einer Binnenwasserstraße und mindestens einem weiteren Gütertransportsystem

MicroPorts

Potential analysis of a multimodal transshipment system for the direct or indirect transshipment of goods between an inland waterway and at least one other freight transport system

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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.

Duration 01.07.2022 - 30.06.2024, Funded by BMDV
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Projektlogo Multimodales, KI-gestütztes Informationssystem zur kognitiven Unterstützung logistischer Prozesse

AI-Consult

Multimodal, AI-supported cognitive information support system in logistics processes

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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.

Duration 01.04.2022 - 31.03.2024, Funded by BMWK
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AKAMAI ASRS

New Intralogistics warehouse system

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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

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HybridCPPS

Human Factors in Hybrid Cyber-Physical Production Systems

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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.

Duration 01.01.2022 - 31.12.2024, Funded by Universität Bremen (Zentrale Forschungsförderung)
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Projektlogo Intelligente Arbeitsergonomie mittels sensorischer Exoskelette und autonomen Transportsystemen für die erweiterte Mensch-Technik-Interaktion im Automobilumschlag
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MEXOT

Intelligent work ergonomics using sensory exoskeletons and autonomous transport systems for enhanced human-technology interaction in automotive cargo handling

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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.

Duration 01.01.2022 - 31.12.2024, Funded by BMDV
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Projektlogo ROS-based Education of Advanced Motion Planning and Control

RIEMANN

ROS-based Education of Advanced Motion Planning and Control

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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.

Duration 01.01.2022 - 31.12.2022, Funded by EU - EIT Manufacturing
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Projektlogo Automatisches Ladesystem für palettierte Ladungen für unmodifizierte europäische Auflieger

PaLA

Palletized Loads Automatic Loading System for unmodified European Trailers to enable a Resilient Supply Chain

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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.

Duration 01.01.2022 - 31.12.2022, Funded by EU - EIT Manufacturing
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Projektlogo Realisierung eines barrierefreien Assistenzsystems zur schrittweisen Durchführung von Arbeitsaufgaben
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BASDA

Realization of a barrier-free assistance system for the step-by-step execution of work tasks

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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

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Projektlogo Employees Skills for Predictive Maintenance
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Skills4PdM

Employees Skills for Predictive Maintenance

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The majority of today's organizations still heavily rely upon time-based preventive maintenance techniques. Such well established practices can be enhanced by today's ICT technologies, enabling real-time and continuous monitoring of production assets', towards identifying and preventing anomalies and failures proactively. As this may lead to money savings, predictive maintenance solutions are increasingly introduced in the industrial world. However, the prevailing skillsets of its workforce, even one of a new generation, lacks the required understanding over those soluƟons that will allow them to understand and benefit from them. Hence, Skills4PdM aims to expose interesting parties, including employees, students, professors and long-life learners, to the key enabling technologies of condition monitoring and predictive maintenance, for them to better understand the options, the technologies needed, and how to measure the effectiveness of their decisions with respect to predictive analytics. In addition to exposing key identified audiences to the principles and methods for enabling predictive maintenance policies in an organization along with the necessary education material, target groups will learn about real-world applications stemming from two concluded H2020 projects, SERENA and UPTIME, and EIT-M project RAMEN which will serve as basis for other successful maintenance applications.

The project aims to design end-to-end courses, composed of several nuggets regarding predictive maintenance in Industry 4.0, including video tutorials for theoretical presentation of the topic, hands-on exercises and testing in testbed. Skills4PdM will provide a set of educational material targeting different audiences and skillsets, enriched with the results and knowledge of past H2020 projects and applications, targeting the knowledge transfer across both the academic and industrial world via the EIT-M supporting tools as well as via PdM Teaching & Learning Factories projects.

Duration 01.01.2022 - 31.12.2022, Funded by EU - EIT Manufacturing
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Projektlogo Unified Predictive Maintenance and Scheduling System
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UPMASS

Unified Predictive Maintenance and Scheduling System

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UPMASS aims to provide a unified solution, bridging predictive maintenance to production planning and scheduling. To this end, the outcomes of the H2020 projects UPTIME and SERENA will be used as a basis to bring to the market an integrated IIoT solution covering a complete pipeline from sensor data acquisition, data analysis, and asset condition monitoring, enabling predictive maintenance strategies.

The BIBA’s task is to mature UPTIME components 1) UPTIME_SENSE and 2) UPTIME_DETECT&_PREDICT, which will be used as a local hub for IoT data acquisition and streaming predictive analytics respectively. Data exploration and Predictive maintenance expert.

Duration 01.01.2022 - 31.12.2022, Funded by EU-EIT Manufacturing
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STRATUS

Entwicklung und operativer Einsatz von Micro Digital Twins zur Betriebs- und Lebensdaueroptimierung von Windfarmen durch prädiktive Datenanalyse

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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

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INKOKON

Intelligent end effector component protection for safe human-robot collaboration and coexistence

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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

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Projektlogo Smartes Lernen in der Logistik

SMALO

Smart Learning in Logistics

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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

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Projektlogo Eis-Erkennung an Windenergieanlagen mittels KI-unterstützter Bildverarbeitung Europäische Union; Investition in Bremens Zukunft; Europäischer Fonds für regionale Entwicklung Senatorin für Klimaschutz, Umwelt, Mobilität, Stadtentwicklung und Wohnungsbau der Freien Hansestadt Bremen

EisAuge

Ice detection on wind turbines using AI-assisted image processing

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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)

Duration 16.07.2021 - 31.03.2023, Funded by Land Bremen / EFRE / PFAU (FKZ: VE0126C)
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Projektlogo Unbemanntes Luftfahrtsystem zur Bestandserfassung und Qualitätsprüfung von Paletteninhalten im Blocklager

DroneStock

Unmanned aerial system for inventory recording and quality inspection of pallet contents in indoor block warehouses

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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

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Projektlogo Ressourcenbezogene Prozessverwaltung durch flexible Nutzung intelligenter Module in der hybriden Montage
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PassForM

Resource-based process management through flexible use of intelligent modules in hybrid assembly

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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.

Duration 01.06.2021 - 31.05.2023, Funded by BMWi / AiF
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Baeckerei 4.0

Development of a raw material-specific and cross-process production control in medium-sized bakeries using artificial intelligence

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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

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Projektlogo Test Optimierung mittels  KI-basierter Observer & Simulationen

TOKIOS

Test Optimierung mittels KI-basierter Observer & Simulationen

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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

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Projektlogo Plattformlösung für optimierte, automatisierte und intelligente KI-gestützte Prozesse bei Bestellung und Distribution von Futtermitteln und Befüllungen von Silos

XCeedFeed

Platform for optimized, automated, and intelligent processes to order and distribute compound-feed and for the re-supply of silos

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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.

Duration 01.06.2021 - 31.05.2023, Funded by BMWK
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Projektlogo PRODUCT DATA TRACEABILITY FROM CRADLE TO CRADLE BY BLOCKCHAINS INTEROPERABILITY AND SUSTAINABILITY SERVICE MARKETPLACE

TRICK

PRODUCT DATA TRACEABILITY FROM CRADLE TO CRADLE BY BLOCKCHAINS INTEROPERABILITY AND SUSTAINABILITY SERVICE MARKETPLACE

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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

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Projektlogo Datendienste für die Qualitätskontrolle in Industrie 4.0

i4Q

Industrial Data Services for Quality Control in Smart Manufacturing

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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

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Projektlogo strENgtHening skills and training expertise for TunisiAN and MorroCan transition to industry 4.0 Era
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ENHANCE

strENgtHening skills and training expertise for TunisiAN and MorroCan transition to industry 4.0 Era

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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+

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Projektlogo KI-gesteuerte kognitive Roboterplattform für agile Produktionsumgebungen
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ACROBA

AI-Driven Cognitive Robotic Platform for Agile Production environments

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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

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Projektlogo Forschungs- und Technologieplattform „Steigerung der Energieeffizienz in der Produktion durch Digitalisierung und KI“
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ecoKI

Development of a research and technology platform "Increasing energy efficiency in production through digitization and AI"

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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.
  3.  

Duration 01.12.2020 - 30.11.2024, Funded by 5. Energieforschungsprogramm

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Projektlogo Kognitive Assistenz für die agile Fertigung unterstützt durch vertrauenswürdige künstliche Intelligenz
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COALA

COgnitive Assisted agile manufacturing for a LAbor force supported by trustworthy Artificial Intelligence

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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.

Duration 01.10.2020 - 30.09.2023, Funded by H2020
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Projektlogo Geschlossener digitaler Regelkreis für eine flexible und modulare Herstellung großer Komponenten

PeneloPe

Closed-loop digital pipeline for a flexible and modular manufacturing of large components

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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

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Projektlogo Future Proofing of ICT Trust Chains: Sustainable Operational Assurance and Verification Remote Guards for Systems-of-Systems Security and Privacy
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ASSURED

Future Proofing of ICT Trust Chains: Sustainable Operational Assurance and Verification Remote Guards for Systems-of-Systems Security and Privacy

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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

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Projektlogo Entwicklung eines AR-Frameworks mit erweiterter Sensorik zur Unterstützung der Berufsausbildung und –weiterbildung in der Luftfahrtindustrie

QualifyAR

Development of an AR framework with extended sensor technology to support training and education in the aviation industry

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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.

Duration 01.07.2020 - 30.04.2023, Funded by BMWK
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Projektlogo Optimierung der Instandhaltung von Windenergieanlagen durch den Einsatz von bildverarbeitenden Verfahren auf mobilen Augmented Reality-Endgeräten

compARe

Optimization of the maintenance of wind turbines by using image processing methods on mobile augmented reality devices

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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.

Duration 01.07.2020 - 30.06.2023, Funded by BMWK
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Projektlogo Automobillogistik im See- und Binnenhafen: Integrierte und anwenderorientierte Steuerung der Gerät- und Ladungsbewegungen durch künstliche Intelligenz und eine virtuelle Schulungsanwendung

Isabella2.0

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

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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.

Duration 01.07.2020 - 30.06.2023, Funded by BMVI
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Projektlogo Enhanced Physical Internet-Compatible Earth-frieNdly freight Transportation answER

ePIcenter

Enhanced Physical Internet-Compatible Earth-frieNdly freight Transportation answER

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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

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Projektlogo Fostering DIHs for Embedding Interoperability in CyberPhysical Systems of European SMEs
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DIH4CPS

Fostering DIHs for Embedding Interoperability in CyberPhysical Systems of European SMEs

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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

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Projektlogo An Open, Trusted Fog Computing Platform Facilitating the Deployment, Orchestration and Management of Scalable, Heterogeneous and Secure IoT Services and cross-Cloud Apps
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RAINBOW

An Open, Trusted Fog Computing Platform Facilitating the Deployment, Orchestration and Management of Scalable, Heterogeneous and Secure IoT Services and cross-Cloud Apps

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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

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Projektlogo Protocols and Strategies for extending the useful Life of major capital investments and Large Industrial Equipment
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LEVEL-UP

Protocols and Strategies for extending the useful Life of major capital investments and Large Industrial Equipment

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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

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Projektlogo EIT Manufacturing
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EIT Manufacturing

EIT Manufacturing

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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

  7.  

Duration 01.01.2019 - 01.01.2026, Funded by European Institute of Innovation & Technology (E

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Projektlogo Robuste, zuverlässige und große 12+MW Offshore Windenergieanlage der nächsten Generation für saubere, günstige und wettbewerbsfähige Energie
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ReaLCoE

Next Generation 12+MW Rated, Robust, Reliable and Large Offshore Wind Energy Converters for Clean, Low Cost and Competitive Electricity

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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.

Duration 01.05.2018 - 31.01.2026, Funded by EU
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Projektlogo - Kompetenzzentrum Bremen
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Mittelstand 4.0

Mid- & Small-Sized Enterprises Competency Center Bremen

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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

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Events:
Robotik erfahrbar machen
December 09, 2022, Bremen & online (hybrid)

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