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
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+
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
Context-dependent, Al-based interface for multimodal human-machine interaction with technical logistics systems
Technical logistics systems are becoming increasingly flexible, which means that configuration and control activities of such systems are becoming more complex and require highly qualified IT experts. In contrast, there is a growing shortage of skilled workers in the IT sector. The KoMILo research project aims to counteract this challenge by developing an intuitive and intelligent user interface enabling lower-qualified employees to perform complex tasks on a specialist level. By designing a context-dependent and cross-industry applicable framework that provides the employee process- and situation-dependent suggestions, the project eases the handling of various complex configuration and control operations. Based on basic system functionalities, a digital twin as well as artificial intelligence, interaction and configuration options are generated for the employee by combining and analyzing system, process and employee-related data. In addition to touch input, voice assistants enable multimodal interaction. The framework for the context-dependent, multimodal human-machine interaction is evaluated by using the cellular conveyor system, a driverless transport system as well as a collaborative robotic system.
07.12.2020 - 30.05.2022,
Funded by EFRE: Europäischer Fonds für regionale Entwicklung
Development of a research and technology platform
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 eproducibility 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
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.
Duration 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
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
Development of an AR framework with extended sensor technology to support training and education in the aviation industry
The complexity of the tasks of technical professions in the aviation industry is high. Research is therefore being conducted into new approaches to knowledge transfer for both training and continuing education. The QualifyAR research project aims to support the training of apprentices in aircraft construction. Especially in aircraft construction, the highest demands are placed on training. Accordingly, the use of digital and individual learning environments is being pursued with emphasis in order to improve learning success on the one hand and to prepare the later use of digital assistance systems in the productive process on the other. The QualifyAR project is dedicated to the development of an AR-based qualification system with integrated process step recognition and automated quality control. By means of an AR-framework and on the basis of predefined process databases, teachers should be able to digitally map even complex teaching tasks and to tailor them, taking into account the individual technologie-portfolio. Information and insights of the system are transmitted to the student via a human-system interface using AR-projection in a context-sensitive way. In this project BIBA is researching image-based process step recognition and the use of an IoT construction kit with a focus on signal processing, in order to be able to assess the quality of the task execution on the basis of 2D/3D image data as well as 1D process data, such as torques of cordless screwdrivers, by means of artificial intelligence. The project is realized together with our project partner Ubimax GmbH.
Duration 01.07.2020 - 30.06.2022, Funded by BMWi
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 BMWi
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
Automated Adaption of Condition-Based Maintenance methods for Manufacturing Systems
Maintaining the proper functionality of manufacturing machines is a crucial factor in the automotive industry. Highly efficient maintenance systems are needed to stay competitive. In the course of the ongoing digitalization, new possibilities arise, to further improve condition-based maintenance systems (CBM). Conventional condition monitoring systems demand a high level of domain expertise and manual tuning when implemented on individual machines.
The exhausting task of manually adapting condition-based maintenance systems for individual machines, which is typically done by multiple specialists from different areas, is set to be mostly automated. For this purpose, a machine learning based methodology will be developed to select suitable diagnostic and prognostic methods automatically.
A set of machine learning tools and conventional stochastic methods for time series analysis shall be combined to a learning algorithm in such a way, that the quality of the prognostics and its capability to detect anomalies in a manufacturing process can be improved over time. The core of the approach will be a meta-learning system for automatic selection and optimization of prognostic models via self-collected experience data. This way, the usual manual adaptation workload is set to be reduced.
01.07.2020 - 30.04.2022,
Funded by BAB
Self-learning software platform for 3D-printer farms for individualized mass production using the examples of shoes
The use of 3D printers has been established as a recognized manufacturing process in recent years. In addition to rapid prototyping, the economical production of small series even of quantity 1 and the spatial decoupling of development and production/distribution are decisive advantages of this process. In addition to a large number of different product types, 3D printing also offers the possibility of printing highly individualized shoes in one piece. By creating printer farms that require only a small amount of space and installation effort, decentralized production/distribution sites can be created almost anywhere. In order for these to work optimally, it is necessary to develop largely automated quality control loops that support the operators in detecting and avoiding misprints.
01.07.2020 - 30.06.2022,
Funded by EFRE: Europäischer Fonds für regionale Entwicklung
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
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 - 14.05.2022,
Funded by BAB
Mobiles Inspektionssystem für Weichdichtungen mit pseudometrischen Freiformflächen
There is a wide range of possible applications for sealants, with the greatest added value being achieved in the automotive and aircraft industries. The aim of the project is to develop a mobile documentation and inspection system for the application and evaluation of sealants with pseudometric freeform surfaces. The system is to be developed on the basis of the application and quality inspection of soft gaskets and is also to be used in various other applications. By using deep learning algorithms, a "universal" inspection system for soft seals will be developed, which can be continuously re-trained and offers high reliability. The system is to be designed as a mobile system, which is worn on the body in direct human-technology interaction and operated in real time.
Duration 01.03.2020 - 28.02.2022, Funded by ZIM
Text-based intelligent cooperation platform for print products
Das Gesamtziel des Vorhabens besteht in der Entwicklung und prototypischen Implementierung eines intelligenten Systems zur gezielten Anforderungsermittlung sowie optimalen Kundenberatung und -information bei der Suche nach Druckdienstleistungen. Der Kunde soll befähigt werden jederzeit, schnell und einfach und ohne jegliches Vorwissen den perfekten Partner für seine Problemstellung zu finden. Anhand der Spezifikationen soll das System dem Kunden alle möglichen Druckereien inkl. der Preisvorhersagen entsprechend der Nachfragemenge liefern. Zudem soll eine Plattform geschaffen werden, welche die Kunden und Anbieter zusammenbringt. Neben einer Beschleunigung sowie Vereinfachung des Prozesses sind darüber hinaus Kostenreduktionen für den Kunden sowie bessere Margen für bspw. spezialisierte Druckereien zu erwarten, da Unterbeauftragungen vermieden und Markttransparenz geschaffen werden.
Duration 29.01.2020 - 31.12.2021, Funded by EFRE: Europäischer Fonds für regionale Entwicklung
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
Customer-specific Sustainable Logistics
Durch den Online-Handel gewinnt die Konsumentenlogistik zunehmend an Bedeutung, speziell im Bereich der sog. „letzten Meile“. Besonders herausfordernd ist dabei die Lebensmittellogistik, da es sich hierbei oft um zeitkritische Transporte handelt und sowohl spezielle Transportverpackungen für gekühlte oder tiefgekühlte Produkte notwendig sind als auch zusätzliche Verpackungen für die kundenindividuelle Kommissionierung verwendet werden müssen. So ergibt sich ein Konsumentendilemma, bei dem der Komfort einer Online-Bestellung inklusive Lieferung den hierdurch entstehenden CO2-Emissionen und Verpackungsabfällen gegenüberstehen. Bis dato gibt es jedoch keine Möglichkeit, dem Konsumenten die direkten und indirekten Auswirkungen seines Handelns im Moment der Bestellung aufzuzeigen, sodass eine bewusste Wahl nachhaltiger Optionen heute noch nicht möglich ist.
01.01.2020 - 31.12.2021,
Funded by Zentrale Forschungsförderung Universität Bremen
Ein Meta-Lern-Ansatz zur Selektion geeigneter Prognoseverfahren für eine vorausschauende Instandhaltung in digitalisierten Produktionssystemen
Die Wettbewerbsfähigkeit des produzierenden Gewerbes basiert in Hochlohnländern auf einem hohen Automatisierungsgrad. Eine effiziente Sicherstellung der technischen Verfügbarkeit einzelner Maschinen und Anlagen ist daher von großer Bedeutung.
Vorausschauende Instandhaltungsstrategien sollen auf Basis der Vorhersage von Maschinenausfällen höhere Verfügbarkeiten, stabilere Produktionsprozesse und Kostenreduktionen ermöglichen und damit zu einer erhöhten Leistungsfähigkeit von Produktionssystemen beitragen.
Das Auftreten von Maschinenausfällen ist aufgrund der inhärenten strukturellen und betrieblichen Komplexität moderner Produktionssysteme jedoch schwer vorherzusagen. Zudem werden die dazu erforderlichen Modelle in der Regel für einen spezifischen Anwendungsfall entwickelt und sind nicht generalisierbar.
Ziel des Projektes ist es daher, ein System zu entwickeln, dass eine automatisierte Auswahl geeigneter Modelle ermöglicht. Die Ergebnisse der Prognosemodelle sollen schließlich für eine integrierte Produktions- und Instandhaltungsplanung und -steuerung genutzt werden.
01.01.2020 - 31.12.2021,
Funded by DFG
Quality-oriented production control and optimization in food production
The project develops a digitalised, quality-based production planning and control system for food production. The system focus on an optimal use of raw materials (e.g. reduction of the storage time of sensitive raw materials). The development should lead to a better operating grade of the production facilities and an optimization of their energy consumption as well as to an optimized bin management and especially to an increase of the product quality (taste). In order to achieve the objectives, raw material-specific quality-time profiles will be analysed and integrated in an IT-based procedure for quality-oriented production planning and control, which will be implemented as a prototype by the project partner.
Fondsn.: PFAU AZ 59210/2
01.01.2020 - 31.12.2021,
Funded by PFAU
Development of a self-learning eKanban-System using autonomous sensor modules
Within the scope of this research project, an eKanban system will be developed which implements the advantages of modern, intelligent industry 4.0 solutions and at the same time remains economical for companies in terms of integration and ongoing operation. These include low-cost, autonomous sensor modules that are easy to install and have low power consumption to enable complete inventory monitoring. The eKanban system itself is linked via a cloud to machine learning processes, which enable continuous learning of material demand behaviour and thus continuous optimisation of material provision in terms of replenishment time.
Duration 01.01.2020 - 31.12.2021, Funded by BMWi
BIM-based assistance system for laying electrical cables by means of true-to-scale projection of circuit diagrams
As part of the project, an assistance system is being developed that supports continuous digitalization of the electrical installation using augmented projection. The assistance system is a mobile stand solution with a motorized turntable for the projection unit, which projects planning information in the correct scaling, position and orientation on the wall / ceiling / floor. This allows an overall impression to be created and markings and symbols to be transferred manually. For this purpose, the system is equipped with a 2D / 3D scan component to localize its own position as well as corresponding image-based object recognition for real and symbolic light switches, windows, doors, sockets etc. according to DIN standard 15015-2. This allows planning deviations to be recorded and the correct execution of the planning content to be checked. An essential aspect is the development of a CAD engine for the correct perspective and true-to-scale representation. The entire system is optimized for use on construction sites and is accordingly protected against dust and splash water.
Duration 01.01.2020 - 31.12.2022, Funded by BMWi
Assistance system for optimized noise protection planning and AR-based representation of a planning status of railway lines
The transport of goods by rail is to be doubled by 2025/2030. This creates an increase in freight traffic on the rail network, which is to be countered by building and renewing routes. The route planning is a complex and lengthy process, in which site inspections with public participation are necessary. To make this procedure easier, an AR assistance system is to be created as part of the project for the realistic visualization and auralization of planning statuses. The assistance system has two focal points: an indoor display that projects the planning status in 3D on a flat surface using an AR device and an outdoor display that projects the 3D planning status directly into the landscape using an AR device. For the implementation, automatically generated 3D data from the route planning software Korfin © are integrated via a 3D engine and adapted to the respective display purpose. With regard to auralization, research is being carried out to normalize the background noise when trains pass through to a level that is harmless to perceive, but provides a good impression of the effectiveness of the noise protection wall.
01.01.2020 - 31.12.2021,
Funded by ZIM
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
Gamified AI Assistance System for Support of Manual Assembly Processes
In this research project, a novel assistance system for manual assembly stations based on artificial intelligence will be developed. On the one hand, the system monitors the assembly process and verifies the quality of the completed product, and, on the other hand, it considers and individually supports the employee when working at the manual workstation. The system will analyse the sensory information collected at the assembly station using image processing and machine learning methods with regard to the ergonomic and production-related work situation of the employee. This enables the newly developed assistance system to adapt to the individual needs of the employee in order to improve his work situation through specific support as well as motivation and training strategies. Furthermore, by monitoring both progress and assembly components, the system will increase the efficiency and quality of the manual assembly process.
01.06.2019 - 31.03.2021,
Funded by EFRE: Europäischer Fonds für regionale Entwicklung
Complementary application of mathematical and discrete-event models to solve complex planning and control problems in offshore construction logistics
Offshore construction logistics pose an exceptionally challenging problem in terms of planning and control. Generally, one can differentiate two approaches: event-discrete simulations as well as mathematical or stochastic optimizations. By themselves, both methods provide their own advantages and disadvantages in terms of computational time, level of detail und optimality.
This project aims to investigate new ways for the complementary utilization of both types of methods in the context of offshore construction logistics. Under the basic assumption that despite formal differences, both types of models describe the same elements of the real world system, this project aims to develop a method to convert in between or to generate each kind of model with its own level of aggregation/abstraction based on a more basic description of the real world system. Consequently, the advantage of both types of models can be used complementary within computer aided planning and control methods.
01.04.2019 - 30.09.2021,
Funded by DFG
LNG Armaturen Set
Development of a sensitive valve set for high-volume ship to ship LNG transfer
The project aims at the development of a system which can be used on a large number of different ship types and thus leads to a significantly higher level of safety, installability and maintainability while at the same time reducing costs. The task of BIBA is to develop an Augmented Reality (AR) solution that can be used for maintenance and service purposes alongside the valve set.
By means of a combination of a commercial data goggle, a camera and an embedded PC, an easily configurable application solution is created. This solution should be able to identify the existing components, to read out the corresponding status information both visually and via radio, and to supply the users with maintenance information and checklists.
The AR solution will be developed to support technicians in operation, installation and maintenance of the sensitive LNG valve set. By means of image processing and object recognition techniques, the first step is to collect information on the condition of the valves. Subsequently, an AR-User Interface will be developed, which acts as an assistance system for the users.
01.03.2019 - 28.02.2021,
Funded by BMWi
Safety process system for cryogenic fluid transfer
The handling of cryogenic fluids (e.g. liquefied natural gas) bears major risks with regard to operational safety. If the liquid leaks during a transfer process (e.g. fueling of ships), large amounts of gas can quickly be produced which are highly flammable and explosive. Therefore, an appropriate safety system for process monitoring is necessary.
The aim of the project is to improve operational safety during the LNG transfer process by means of a redundant optical monitoring system. This system should be able to both detect fittings, ship superstructures, and people automatically and to perform an automated visual inspection of the correct coupling.
The multi-camera system consists of a wide-angle, a zoom and an infrared camera and can therefore react to a wide variety of environmental conditions (day, night, weather influences). It automatically monitors the LNG transfer process. By using Deep Machine Learning, the object recognition of fittings, ship superstructures and people is made possible, which is necessary for monitoring the danger zone.
01.03.2019 - 28.02.2021,
Funded by BMWi
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
Dynamic Production Network Broker
Fully dynamic cross-company production networks that adapt to individual customer orders are a core vision in the Industry 4.0 sector. Production capacities are sometimes required at very short notice, e.g. in the area of drawing and special parts. Reasons are the failure of company owned machines or machines of a supplier, the complete failure of a supplier or also a sudden increase on the demand side. In these cases, however, there are barriers to a rapid response, such as finding one or more suppliers with free capacities or the high manual effort required to integrate new suppliers into existing ordering and logistics processes.
The "Dynamic Production Network Broker" is intended to support the dynamic formation of production networks by means of a modular service system. This includes the matching of supply and demand for short-term availability of production capacities while at the same time ensuring the necessary transport capacities, the short-term onboarding of suppliers, i.e. rapid integration production, logistics and quality assurance and the possibility of making complex assembly activities compatible for outsourcing. The latter should be achieved by means of an assistance system that is based on Augmented Reality (AR) technologies.
BIBA will contribute to the project by developing an ontological description of machine capabilities and requirements, including a semantic mediator with the necessary interfaces to other information systems. Moreover, we will develop a concept for generic service-based business models and their evaluation on the basis of the project results.
Together with the industrial partners, the crucial points for designing a production network broker are worked out and on this basis four use cases are defined. For these four use cases, "Minimal Viable Products", i.e. prototypical solutions that can be implemented quickly, are developed in individual modules and later integrated into a continuous process.
01.01.2019 - 31.12.2021,
Funded by BMBF / PTKA
Intelligent Information Technologies for Process Optimization and Automation in Inland Ports
In Binntelligent, digital services as well as intelligent processes, procedures and information technologies for the optimization of trimodal logistics and transhipment processes in inland ports and the improved collaboration between inland and seaports are designed, implemented and evaluated in the field of application. It creates a cross-company visibility and transparency of decision-relevant information that allows predicting events in the supply chain.
For this purpose, an information system for (semi-) automated information distribution, operative process support and predictions will be developed. In addition to event predictions, forecasting capability in inland ports is achieved by simulation-based optimization of trimodal transhipment, which processes real-time real data and enables adaptability in synchro-modal freight traffic. Binntelligent considers logistics processes for containers and bulk goods in inland ports as well as the pre- and post-carriages. The planned technologies are designed for use in the Weser and Mittelland Canal shipping areas with the ports of Hanover, Braunschweig, Bremen and Bremerhaven and will subsequently be implemented for application-oriented testing and evaluation.
Duration 01.10.2018 - 30.09.2021, Funded by BMVI
DigiLab4U: Open Digital Lab 4 You (Serious Gaming in laboratory-based teaching)
Real laboratory infrastructures are personnel and cost-intensive and are generally only available to the respective research institution. In contrast, purely virtual laboratories offer advantages in terms of security, scalability, remote access and cost efficiency. However, simulations and purely virtual environments cannot replace the success of real laboratory environments, as these require and promote different knowledge.
In the research project Open Digital Lab for You (DigiLab4U for short), real laboratories are digitised, linked with virtual components and the synergies between the two approaches are explored. Augmented Reality can help to close the gap between the "virtual" and "real" experience. Methods of engineering education and serious gaming are combined using learning analytics, mixed/ augmented reality and open badges to form a unique holistic approach in a hybrid learning and research environment.
DigiLab4U provides location-independent access to a digitised and networked learning and research environment. Multi-user scenarios as well as individual self-directed learning will be supported. For example, students of the HFT Stuttgart can access laboratories at the BIBA and the University of Parma. The exchange of experiences in research and teaching is promoted beyond the boundaries of individual institutes. As the long title Open Digital Lab for Yousuggests, the inclusion of further laboratories is planned. There is a considerable need for research on this forward-looking approach from a technical, didactic and organisational point of view.
Duration 01.10.2018 - 31.03.2022, Funded by BMBF
Intelligent pumping and lock control
Sufficient water levels in harbor areas (dock harbors) are of great importance for the efficiency of ports. In many cases, such water levels can only be ensured by the energy-intensive use of pumping stations. An intelligent, integrated network and control of the lock and the associated pumping stations enable an increase in energy-efficiency and easier integration of renewable energies in the port operations to ensure the smooth envelope of goods in tide-free port facilities.
Water levels in dock harbors must be kept at a nearly constant level. For this purpose, the water demand of the locks and other water losses are compensated by the water supply of pumping stations and other water inputs. Due to the number of ships in the lock or a large number of associated and external parameters, a complex control problem arises. This is further complicated by the fact that many of the parameters depend on temporal influencing factors. In the research project, Tide2Use uses initially existing information sources are to be brought together and visualized.
On this basis, it should be automatically recognized whether a water level increase in the harbor basin makes sense. Data from the Automatic Identification System (AIS) as well as data from the National Single Window (NSW), level readings and weather data are to be considered. It is intended to design and develop an adaptive system that can determine regularities in traffic - in particular, small-scale shipping - from AIS tracks and to take them into account in planning.
The aim is to create an assistance system that supports the lock operator. It is intended to recommend a time period to the navigator and lock operator, where the lock gate can be used for natural irrigation of the port without affecting shipping traffic and taking all risks into account. With intelligent and continuous networking of shipping traffic, the lock operation and the associated pumping stations will work more efficiently.
01.10.2018 - 30.09.2021,
Funded by BMVI
Multi-Criteria Optimization of Position and Configuration of 3D Sensors through Virtual Reality for Flexible Automation Solutions in Logistics
The design of flexible handling robots and autonomous vehicles for logistic processes is a great challenge due to heterogeneous objects, variable environmental conditions and complex properties of the 3D sensor technology.
In the VirtuOS project, a freely available online tool is being developed with which application scenarios in virtual space can be freely configured and 3D sensor data realistically simulated. The objective of the project is the development and integration of a multicriteria optimization, which delivers application-specific optimal sensor configurations depending on different optimization criteria. SMEs such as automation companies, system integrators and suppliers of sensors and image processing solutions can thus be supported in the selection and configuration of sensors for new working stations or robots.
Duration 01.06.2018 - 28.02.2021, Funded by AiF
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 - 31.10.2021,
Funded by H2020
Robust Industriell Transformasjon
Wettbewerbsvorteile können nicht nur für Produkte mit reduzierten Kosten sowie verkürzten Design- und Produktionszyklen erzielt werden, sondern auch durch das Erschließenneuer Geschäftsmodelle, wie der Weiterentwicklung klassischer Produkte zu Produktservice-Systemen. Das vom Norwegischen Forschungsrat geförderte Projekt "Robust Industrial Transformation" (RIT) unterstützt den mittelständischen Bootsbau dabei diesen Paradigmenwechsel erfolgreich zu meistern. Im Vordergrund steht die Entwicklung eines breiten Spektrums neuer Lösungen zur Erschließung neuer Wertschöpfungspotenziale, wie bspw. die Anpassung der Prozesse in der Entwurfsphase oder die Entwicklung neuer Produktkonzepte auf Basis realer Gebrauchs-Daten? Aufbauend auf den Ansätzen zur Datenakquise und -verarbeitung aus vorangegangenen Forschungsprojekten, bereitet RIT die Daten der Bootshersteller auf, um ihn gezielt frühen Phasen der Produktentwicklung bereitzustellen. So sollen die Bootshersteller unter Anderem in die Lage versetzt werden, große Mengen an Produktdaten in Bezug auf spezifische Designanforderungen zu analysieren und diese zusammen mit anderen Daten strukturieren und visualisieren zu können.
Duration 01.03.2018 - 31.12.2021, Funded by Norwegian Research Council
Collaborative robot-robot-human interaction for fruit laying
Depending on flexibility and capacity requirements, placing fruit on conveyors is either completely manual or fully automated in large plants. Affiliated to the process is a quality control and a final packaging. Against this background, large rationalization potentials for medium flexibility and capacity requirements can be identified by partial automation. The aim of the project is the development of a collaborative fruit lay-up system, which is freely scalable in terms of both employee and robot use and can support automated handling, quality control and packaging. The system should be universally applicable and can be adapted quickly to different types of fruit depending on the season. An essential feature is an intuitive work organization between human and robot.
01.01.2018 - 15.04.2021,
Funded by BMWi
- Kompetenzzentrum Bremen
Das Mittelstand 4.0-Kompetenzzentrum Bremen bietet u. a. kleinen und mittleren Unternehmen in der Region Bremen und umzu Unterstützung bei der Steigerung ihrer Digitalisierungskompetenzen. Insbesondere Fach- und Führungskräften in den Innovationsclustern Maritime Wirtschaft und Logistik, Windenergie, Luft- und Raumfahrt, Automobilwirtschaft sowie Nahrungs- und Genussmittelwirtschaft sollen für die Digitalisierung sensibilisiert, qualifiziert und zu "Digitalen Botschaftern" ausgebildet werden.
Duration 01.01.2018 - 31.12.2022, Funded by BMWi
Maritime Regional Network for Integrated Digital Working and Learning
The objective of MARIDAL is the establishment of a regional, industry-related maritime transfer network that will initiate activities in the sense of a "digital pilot" on the subject of digital learning on ships and in the port, as well as qualifying for the digitized port world. The main application areas are the digitized maritime supply chain, the Smart Shipping, and the Digital Port. The focus is on the small and medium-sized enterprises (SMEs) in the maritime sector, as the resources for linking pedagogy and didactics, technology and organizational development are often lacking in SMEs and there is a need for learning from and among themselves. As a result, synergies can be exploited and competencies for the development of intensified knowledge transfer and qualification concepts can be built commonly.
Duration 01.12.2017 - 30.11.2021, Funded by BMBF
Development of a fully automatic fermenter with automatic determination of the fermentation state
In industrial bakery production, a lot of time is spent on determining the optimum fermentation state by baking experts. Achieving the optimal fermentation state purely on the fermentation time and ensuring compliance with the machine-side fermentation and cooling parameters is thus impossible in both branch operation and in industrial operation according to current state of development. The project develops a novel fermentation system (fully automatic proofer) with integrated measuring technology and a special software solution, that detects the current maturity automatically and reproducibly without having to interrupt the fermentation process. The system should be cost-effective, adaptable (large product range) and easy to use. Additionally, the system should be able to specify process leveling.
01.10.2017 - 15.07.2021,
Funded by BMWi
Unified Predictive Maintenance System
UPTIME aims to design a unified predictive maintenance framework and an associated unified information system in order to enable the predictive maintenance strategy implementation in manufacturing industries. As products become more complex due to evolution of technology, high quality and reliability have become issues of high significance. To reach the required levels of availability, maintainability, quality and safety of production machinery, while considering the system as a whole, and throughout the entire production lifecycle, manufacturing companies are increasingly considering turning to predictive maintenance, by utilising the capabilities of condition monitoring.
The UPTIME predictive maintenance system will incorporate information from heterogeneous data sources, e.g. sensors, to more accurately estimate the process performances. Therefore, UPTIME will extend and unify the new digital, e-maintenance services and tools in order to exploit the full potential of predictive maintenance management, sensor-generated big data processing, e-maintenance support, proactive computing and the four levels of data analytics maturity (Monitor, Diagnose and Control, Manage and Optimize). The UPTIME system will be deployed and validated through implementation in three business cases: white goods home appliances – dryer drum, steel industry – cold rolling machine and construction of production systems – transportation jigs.
Duration 01.09.2017 - 28.02.2021, Funded by H2020
Interactive robotic system for unloading of sea containers
The unloading of containers is one of the last non-automated activities in a highly-engineered transport chain. A significant proportion of imported and exported containers are emptied or loaded in seaports. Existing automatic and semi-automatic systems do not meet the requirements of port operators due to high investment costs, high commissioning times and adaptations to the infrastructure and have a very low degree of dissemination. The objective of the IRiS project is the development of a new, mobile robot for improving the efficiency of transhipment processes at seaports. The robot should be able to be deployed in a very short time without any major adjustments to the existing operational infrastructure. In order to be able to meet disturbing situations as quickly and effortlessly as possible, an intuitive human-robot interaction interface is developed.
01.09.2017 - 30.04.2021,
Funded by BMVI
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
An adaptive simulation-based optimisation approach for the scheduling and control of dynamic manufacturing systems
The planning and control of production processes has a significant influence on the performance of a job shop manufacturing system. The job shop production is subject to dynamic influences (e.g. faults caused by machine failures or rush orders), which have to be considered for the production planning and control. Common methods are therefore normally divided into modules for calculating plans and modules for operational control. In general, optimisation only takes place at the strategic planning level, while detailed planning is carried out on the basis of simple, static dispatching rules. This allows the generation of schedules in short computation times, but generally no optimal schedules based on the current state of the production system are generated.
Results of the 1st phase
In the first phase of the Brazilian-German cooperation project, a simulation-based optimisation method for controlling dynamic job shop production has been developed. The classical approach of simulation-based optimisation was extended in such a way that the dynamics of job shop manufacturing are taken into account and the optimisation of planning decisions and control rules is always based on the current system state. The developed method was evaluated considering the job shop production of a Brazilian producer of mechanical parts.
Objectives of the 2nd phase
In the second project phase, a method for the integrated control of inventory, production and maintenance processes has to be developed in order to map the current status of a production system in more detail. This means that maintenance orders can be scheduled for the machines in addition to the existing method and the inventory stocks can be taken into account for planning and control.
Initially, methods for planning maintenance jobs (Germany) and methods for inventory control (Brazil) using up-to-date system data will be developed in parallel. Subsequently, both approaches will be combined to an integrated inventory, production and maintenance control method, which will then be evaluated in a real scenario using data from the industry partner Rudolph Usinados as well as by scenarios from the literature.
01.04.2016 - 31.03.2021,
Funded by DFG
January - July 2021, Bremen
Einstieg in die Datenverarbeitung und -auswertung
11. März 2021, online
Rückverfolgbarkeit von Produkten und Shopfloor Digitalization
25. März 2021, online