PSPS: Project

	QualifyAR 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. Contact persons: A. Rohde (Project manager) Funded by: BMWi Duration: 01.07.2020 - 30.06.2022 See project's publications See project's page
	compARe 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. Contact persons: M. Quandt H. Stern W. Zeitler Funded by: BMWi Duration: 01.07.2020 - 30.06.2023 See project's publications See project's page
	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 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. Contact persons: M. Hoff-Hoffmeyer-Zlotnik (Project manager) N. Jathe T. Sprodowski Funded by: BMVI Duration: 01.07.2020 - 30.06.2023 See project's publications See project's page
	AutoCBM 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. Contact persons: H. Engbers (Project manager) S. Leohold Funded by: BAB Duration: 01.07.2020 - 30.04.2022 See project's publications See project's page
	PrintAI 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. Contact persons: M. Trapp (Project manager) M. Kreutz Funded by: EFRE: Europ�ischer Fonds f�r regionale Entwicklung Duration: 01.07.2020 - 30.06.2022 See project's publications See project's page
	INSERT 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. Contact persons: L. Steinbacher M. Veigt Funded by: BAB Duration: 15.05.2020 - 14.05.2022 See project's publications See project's page
	MARGO Optimizing Material Flow with AGVs in Ring Gear Production Due to a lack of skilled workers and external cost pressure, small and medium-sized production companies are also forced to optimize and automate processes. A great potential lies in logistic processes, which often take place manually and thus require productive working time of skilled personnel. For small and medium-sized companies, however, the initial investment for automated processes represents a major hurdle. The MARGO project aims to use OPIL, an open platform for the integration of logistics processes, to demonstrate the optimisation potential of internal logistics processes in a simple and fast way by means of simulations and to prove the feasibility by means of a pilot test. Thus the risk of an investment is reduced to a minimum and the complete integration can be planned more easily. For the identification of optimizations, the production environment of a ring gear manufacturer is mapped in a 3D simulation environment, which is part of OPIL. In this way, different application scenarios of automated guided vehicles (AGV) can be evaluated and compared quickly and cost-effectively. A prototype AGV will be integrated into the cloud based IoT platform. This allows existing processes to be combined with new material handling processes. Contact persons: L. Rolfs (Project manager) N. Hoppe Funded by: H2020 Duration: 01.03.2020 - 31.01.2021 See project's publications See project's page
	SealingQuality 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. Contact persons: J. Arango Castellanos Funded by: ZIM Duration: 01.03.2020 - 28.02.2022 See project's publications See project's page
	KiNaLog 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. Contact persons: M. Trapp (Project manager) Funded by: Zentrale Forschungsf�rderung Universit�t Bremen Duration: 01.01.2020 - 31.12.2021 See project's publications See project's page
	MetaMaintain 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. Contact persons: H. Engbers (Project manager) S. Leohold Funded by: DFG Duration: 01.01.2020 - 31.12.2021 See project's publications See project's page
	Manufaktur 4.0 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 Contact persons: A. Rohde (Project manager) L. Steinbacher Funded by: PFAU Duration: 01.01.2020 - 31.12.2021 See project's publications See project's page
	X-Kanban 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. Contact persons: A. Ait Alla (Project manager) M. Kreutz Funded by: BMWi Duration: 01.01.2020 - 31.12.2021 See project's publications See project's page
	WireWizard 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. Contact persons: M. Lütjen (Project manager) W. Zeitler Funded by: BMWi Duration: 01.01.2020 - 31.12.2022 See project's publications See project's page
	RailAR 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. Contact persons: R. Leder (Project manager) Funded by: ZIM Duration: 01.01.2020 - 31.12.2021 See project's publications See project's page
	AxIoM 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. Contact persons: C. Petzoldt (Project manager) T. Beinke D. Keiser Funded by: EFRE: Europ�ischer Fonds f�r regionale Entwicklung Duration: 01.06.2019 - 31.03.2021 See project's publications See project's page (http://www.efre-bremen.de)
	OffshorePlan 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. Contact persons: M. Lütjen (Project manager) N. Jathe D. Rippel Funded by: DFG Duration: 01.04.2019 - 30.09.2021 See project's publications See project's page
	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. Contact persons: H. Stern (Project manager) R. Leder Funded by: BMWi Duration: 01.03.2019 - 28.02.2021 See project's publications See project's page
	LNG Safety 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. Contact persons: H. Stern (Project manager) N. Jathe Funded by: BMWi Duration: 01.03.2019 - 28.02.2021 See project's publications See project's page
	EIT Manufacturing EIT Manufacturing 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 Manufacturings 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 Contact persons: P. Klein J. Wilhelm Funded by: European Institute of Innovation & Technology (E Duration: 01.01.2019 - 01.01.2026 See project's publications See project's page (http://www.eit-manufacturing.eu)
	DPNB Dynamic Production Network Broker Motivation 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. Goal 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. Procedure 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. Contact persons: E. Broda (Project manager) M. Hoff-Hoffmeyer-Zlotnik S. Wiesner Funded by: BMBF / PTKA Duration: 01.01.2019 - 31.12.2021 See project's publications See project's page (dpnb.de)
	VirtuOS 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. Contact persons: A. Börold (Project manager) L. Panter Funded by: AiF Duration: 01.06.2018 - 28.02.2021 See project's publications See project's page
	ReaLCoE 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 todays 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. Contact persons: J. Uhlenkamp (Project manager) A. Ait Alla M. Kreutz S. Oelker A. Sander M. Stietencron Funded by: H2020 Duration: 01.05.2018 - 31.10.2021 See project's publications See project's page (realcoe.eu)
	CooPick 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. Contact persons: J. Arango Castellanos (Project manager) M. Lütjen A. Rohde Funded by: BMWi Duration: 01.01.2018 - 15.04.2021 See project's publications See project's page
	F.I.T. Gaerautomat 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. Contact persons: J. Arango Castellanos A. Rohde Funded by: BMWi Duration: 01.10.2017 - 15.07.2021 See project's publications See project's page
	IRiS 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. Contact persons: T. Beinke (Project manager) N. Hoppe C. Petzoldt L. Rolfs J. Wilhelm Funded by: BMVI Duration: 01.09.2017 - 30.04.2021 See project's publications See project's page (http://www.iris-projekt.de)
	CBS 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. Contact persons: S. Schukraft D. Wagner-Kampik Funded by: DFG Duration: 16.08.2017 - 15.11.2022 See project's publications See project's page
	AdaptiveSBO An adaptive simulation-based optimisation approach for the scheduling and control of dynamic manufacturing systems Motivation 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. Approach 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. Contact persons: E. Broda (Project manager) Funded by: DFG Duration: 01.04.2016 - 31.03.2021 See project's publications See project's page (https://www.smartconnectedmanufacturing.de/#projects)

Research

Project