11 Interfakultäre Einrichtungen
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Item Open Access Kontextszenarien der deutschen Energiewende : eine Datenerhebung zur Analyse gesellschaftlich-politischer Rahmenbedingungen einer sozio-technischen Transformation(2015) Weimer-Jehle, Wolfgang; Prehofer, Sigrid; Hauser, WolfgangDieser Bericht beschreibt eine Expertenerhebung zum sozio-technischen Kontext der deutschen Energiewende und stellt ausgewählte Ergebnisse vor. Wesentliche sozio-technische Treiber des Energiesystems und dessen Entwicklung wurden identifiziert sowie alternative Zukünfte für jeden Treiber auf Basis von Literaturanalyse und Expertenbefragungen abgeleitet. Die wechselseitigen Beziehungen zwischen den möglichen Zukünften der Treiber wurden in einer Reihe von Experteninterviews unter Verwendung der Cross-Impact Bilanz Analyse abgeschätzt. Eine vorläufige Evaluation der Rohdaten ergab insgesamt 565 konsistente Kontextszenarien. Bevor jedoch endgültige Ergebnisse abgeleitet werden können, ist eine weitere Konsolidierung der Daten notwendig.Item Open Access Long-term stability of capped and buffered palladium-nickel thin films and nanostructures for plasmonic hydrogen sensing applications(2013) Strohfeldt, Nikolai; Tittl, Andreas; Giessen, HaraldOne of the main challenges in optical hydrogen sensing is the stability of the sensor material. We found and studied an optimized material combination for fast and reliable optical palladium-based hydrogen sensing devices. It consists of a palladium-nickel alloy that is buffered by calcium fluoride and capped with a very thin layer of platinum. Our system shows response times below 10 s and almost no short-term aging effects. Furthermore, we successfully incorporated this optimized material system into plasmonic nanostructures, laying the foundation for a stable and sensitive hydrogen detector.Item Open Access Spatio-temporal and immersive visual analytics for advanced manufacturing(2019) Herr, Dominik; Ertl, Thomas (Prof. Dr.)The increasing amount of digitally available information in the manufacturing domain is accompanied by a demand to use these data to increase the efficiency of a product’s overall design, production, and maintenance steps. This idea, often understood as a part of Industry 4.0, requires the integration of information technologies into traditional manufacturing craftsmanship. Despite an increasing amount of automation in the production domain, human creativity is still essential when designing new products. Further, the cognitive ability of skilled workers to comprehend complex situations and solve issues by adapting solutions of similar problems makes them indispensable. Nowadays, customers demand highly customizable products. Therefore, modern factories need to be highly flexible regarding the lot size and adaptable regarding the produced goods, resulting in increasingly complex processes. One of the major challenges in the manufacturing domain is to optimize the interplay of human expert knowledge and experience with data analysis algorithms. Human experts can quickly comprehend previously unknown patterns and transfer their knowledge and gained experience to solve new issues. Contrarily, data analysis algorithms can process tasks very efficiently at the cost of limited adaptability to handle new situations. Further, they usually lack a sense of semantics, which leads to a need to combine them with human world knowledge to assess the meaningfulness of such algorithms’ results. The concept of Visual Analytics combines the advantages of the human’s cognitive abilities and the processing power of computers. The data are visualized, allowing the users to understand and manipulate them interactively, while algorithms process the data according to the users’ interaction. In the manufacturing domain, a common way to describe the different states of a product from the idea throughout the realization until the product is disposed is the product lifecycle. This thesis presents approaches along the first three phases of the lifecycle: design, planning, and production. A challenge that all of the phases face is that it is necessary to be able to find, understand, and assess relations, for example between concepts, production line layouts, or events reported in a production line. As all phases of the product lifecycle cover broad topics, this thesis focuses on supporting experts in understanding and comparing relations between important aspects of the respective phases, such as concept relationships in the patent domain, as well as production line layouts, or relations of events reported in a production line. During the design phase, it is important to understand the relations of concepts, such as key concepts in patents. Hence, this thesis presents approaches that help domain experts to explore the relationship of such concepts visually. It first focuses on the support of analyzing patent relationships and then extends the presented approach to convey relations about arbitrary concepts, such as authors in scientific literature or keywords on websites. During the planning phase, it is important to discover and compare different possibilities to arrange production line components and additional stashes. In this field, the digitally available data is often insufficient to propose optimal layouts. Therefore, this thesis proposes approaches that help planning experts to design new layouts and optimize positions of machine tools and other components in existing production lines. In the production phase, supporting domain experts in understanding recurring issues and their relation is important to improve the overall efficiency of a production line. This thesis presents visual analytics approaches to help domain experts to understand the relation between events reported by machine tools and comprehend recurring error patterns that may indicate systematic issues during production. Then, this thesis combines the insights and lessons learned from the previous approaches to propose a system that combines augmented reality with visual analysis to allow the monitoring and a situated analysis of machine events directly at the production line. The presented approach primarily focuses on the support of operators on the shop floor. At last, this thesis discusses a possible combination of the product lifecycle with knowledge generating models to communicate insights between the phases, e.g., to prevent issues that are caused from problematic design decisions in earlier phases. In summary, this thesis makes several fundamental contributions to advancing visual analytics techniques in the manufacturing domain by devising new interactive analysis techniques for concept and event relations and by combining them with augmented reality approaches enabling an immersive analysis to improve event handling during production.Item Open Access Concepts and methods for the design, configuration and selection of machine learning solutions in manufacturing(2021) Villanueva Zacarias, Alejandro Gabriel; Mitschang, Bernhard (Prof. Dr.-Ing. habil.)The application of Machine Learning (ML) techniques and methods is common practice in manufacturing companies. They assign teams to the development of ML solutions to support individual use cases. This dissertation refers as ML solution to the set of software components and learning algorithms to deliver a predictive capability based on available use case data, their (hyper) paremeters and technical settings. Currently, development teams face four challenges that complicate the development of ML solutions. First, they lack a formal approach to specify ML solutions that can trace the impact of individual solution components on domain-specific requirements. Second, they lack an approach to document the configurations chosen to build an ML solution, therefore ensuring the reproducibility of the performance obtained. Third, they lack an approach to recommend and select ML solutions that is intuitive for non ML experts. Fourth, they lack a comprehensive sequence of steps that ensures both best practices and the consideration of technical and domain-specific aspects during the development process. Overall, the inability to address these challenges leads to longer development times and higher development costs, as well as less suitable ML solutions that are more difficult to understand and to reuse. This dissertation presents concepts to address these challenges. They are Axiomatic Design for Machine Learning (AD4ML), the ML solution profiling framework and AssistML. AD4ML is a concept for the structured and agile specification of ML solutions. AD4ML establishes clear relationships between domain-specific requirements and concrete software components. AD4ML specifications can thus be validated regarding domain expert requirements before implementation. The ML solution profiling framework employs metadata to document important characteristics of data, technical configurations, and parameter values of software components as well as multiple performance metrics. These metadata constitute the foundations for the reproducibility of ML solutions. AssistML recommends ML solutions for new use cases. AssistML searches among documented ML solutions those that better fulfill the performance preferences of the new use case. The selected solutions are then presented to decision-makers in an intuitive way. Each of these concepts was evaluated and implemented. Combined, these concepts offer development teams a technology-agnostic approach to build ML solutions. The use of these concepts brings multiple benefits, i. e., shorter development times, more efficient development projects, and betterinformed decisions about the development and selection of ML solutions.Item Open Access Data processing, analysis, and evaluation methods for co-design of coreless filament-wound building systems(2023) Gil Pérez, Marta; Mindermann, Pascal; Zechmeister, Christoph; Forster, David; Guo, Yanan; Hügle, Sebastian; Kannenberg, Fabian; Balangé, Laura; Schwieger, Volker; Middendorf, Peter; Bischoff, Manfred; Menges, Achim; Gresser, Götz T.; Knippers, JanItem Open Access SyKonaS - Projektbericht. Nr. 1, Konflikte in der Energiewende: Definitionen und Typologien(Stuttgart : Verbundvorhaben SyKonaS, Zentrum für interdisziplinäre Risiko- und Innovationsforschung der Universität Stuttgart (ZIRIUS), 2022) Minn, Fabienne; Wassermann, Sandra; León, Christian D.; Püttner, Andreas (Mitwirkender); Liebhart, Laura (Mitwirkende); Wolf, Patrick (Mitwirkender)Das Forschungsprojekt "SyKonaS: Systemische Konfliktanalyse mittels Szenariotechnik" hat zum Ziel, gesellschaftliche Konflikte und deren Wechselwirkungen in der Energiewende zu verstehen, zu antizipieren und Lösungsvorschläge zu entwickeln. Im Rahmen dieser Zielsetzung wurden im Arbeitspaket 1 des Projektes die Konflikte der Energiewende empirisch aufgearbeitet und eine systematische Typologie von Energiewendekonflikten entwickelt (Task 1). Im vorliegenden Bericht werden das Vorgehen und die erzielten Ergebnisse beschrieben.Item Open Access Autonome Entscheidungsfindung in der Produktionssteuerung komplexer Werkstattfertigungen(Stuttgart : Fraunhofer Verlag, 2020) Waschneck, Bernd; Bauernhansl, Thomas (Prof. Dr.-Ing.)Die Variabilität in der kundenindividuellen Massenproduktion stellt eine enorme Herausforderung für die industrielle Fertigung dar. Die komplexe Werkstattfertigung als Produktionsprinzip eignet sich aufgrund der inhärenten Flexibilität besonders für die kundenindividuelle Massenproduktion. Allerdings sind die bestehenden Methodiken für die Produktionssteuerung einer Werkstattfertigung für die Einmal- oder Wiederholproduktion ausgelegt, was zu Defiziten in der Massenproduktion führt. Entweder ist die globale Qualität der Ergebnisse suboptimal oder die notwendige Echtzeitfähigkeit in der Entscheidungsfindung kann nicht bereitgestellt werden. Zudem entsteht durch Veränderungen und Anpassungen der Produktionssteuerung einer komplexen Werkstattfertigung ein hoher manueller Aufwand. In der vorliegenden Arbeit wird eine Methodik für eine dezentrale, selbstorganisierte und autonome Produktionssteuerung für eine Werkstattfertigung entwickelt, die dazu beiträgt, mit der zunehmenden Komplexität und dem Produktionsvolumen umzugehen. Dabei wird die Produktion als Reinforcement-Learning-Modell formalisiert, das die Grundlage für das autonome Lernen einer Strategie zur Optimierung der Abarbeitungsreihenfolge bildet. Mehrere kooperative Deep-Q-Network-Agenten werden in diesem Modell darauf trainiert, eine Strategie zu finden, die eine gegebene Bewertungsfunktion - meist ein Key Performance Indicator aus der Produktion - maximiert. Die Neuronalen Netze, in denen die erlernte Entscheidungslogik der Deep-Q-Network-Agenten abgebildet ist, werden nach der Trainingsphase in die Produktion übertragen. Der Multi-Agenten-Ansatz trägt dazu bei, dass der Lernvorgang beschleunigt wird und im produktiven Einsatz durch die Dezentralität Entscheidungen schneller bestimmt werden können. Die Erprobung der Methodik in zwei praxisnahen Fallbeispielen aus der Halbleiterindustrie zeigt ihre Leistungsfähigkeit. In beiden Fallbeispielen konnten Strategien zur Optimierung der Abarbeitungsreihenfolge auf oder über Expertenniveau autonom erlernt werden. Konkret konnte dadurch im zweiten Fallbeispiel der Anteil verspäteter Aufträge in einer Technologieklasse von 17, 0 % auf 1, 3 % reduziert werden. Abgerundet wird die Arbeit durch eine Einordnung in das soziotechnische System „Fabrik“, in der die Umsetzung der Reihenfolgeentscheidungen durch die Werker betrachtet wird. Dabei wird offensichtlich, dass die Optimierung der Produktionssteuerung ganzheitlich unter Einbeziehung der Werker in einem kontinuierlichen Verbesserungsprozess erfolgen muss.Item Open Access Temporally dense exploration of moving and deforming shapes(2020) Frey, SteffenWe present our approach for the dense visualization and temporal exploration of moving and deforming shapes from scientific experiments and simulations. Our image space representation is created by convolving a noise texture along shape contours (akin to LIC). Beyond indicating spatial structure via luminosity, we additionally use colour to depict time or classes of shapes via automatically customized maps. This representation summarizes temporal evolution, and provides the basis for interactive user navigation in the spatial and temporal domain in combination with traditional renderings. Our efficient implementation supports the quick and progressive generation of our representation in parallel as well as adaptive temporal splits to reduce overlap. We discuss and demonstrate the utility of our approach using 2D and 3D scalar fields from experiments and simulations.Item Open Access Stochastic model for energy propagation in disordered granular chains(2021) Taghizadeh, Kianoosh; Shrivastava, Rohit; Luding, StefanEnergy transfer is one of the essentials of mechanical wave propagation (along with momentum transport). Here, it is studied in disordered one-dimensional model systems mimicking force-chains in real systems. The pre-stressed random masses (other types of disorder lead to qualitatively similar behavior) interact through (linearized) Hertzian repulsive forces, which allows solving the deterministic problem analytically. The main goal, a simpler, faster stochastic model for energy propagation, is presented in the second part, after the basic equations are re-visited and the phenomenology of pulse propagation in disordered granular chains is reviewed. First, the propagation of energy in space is studied. With increasing disorder (quantified by the standard deviation of the random mass distribution), the attenuation of pulsed signals increases, transiting from ballistic propagation (in ordered systems) towards diffusive-like characteristics, due to energy localization at the source. Second, the evolution of energy in time by transfer across wavenumbers is examined, using the standing wave initial conditions of all wavenumbers. Again, the decay of energy (both the rate and amount) increases with disorder, as well as with the wavenumber. The dispersive ballistic transport in ordered systems transits to low-pass filtering, due to disorder, where localization of energy occurs at the lowest masses in the chain. Instead of dealing with the too many degrees of freedom or only with the lowest of all the many eigenmodes of the system, we propose a stochastic master equation approach with reduced complexity, where all frequencies/energies are grouped into bands. The mean field stochastic model, the matrix of energy-transfer probabilities between bands, is calibrated from the deterministic analytical solutions by ensemble averaging various band-to-band transfer situations for short times, as well as considering the basis energy levels (decaying with the wavenumber increasing) that are not transferred. Finally, the propagation of energy in the wavenumber space at transient times validates the stochastic model, suggesting applications in wave analysis for non-destructive testing, underground resource exploration, etc.Item Open Access Context scenarios of the German Energy Transition : a data collection for the analysis of the socio-political framework of a socio-technical transformation(2020) Weimer-Jehle, Wolfgang; Prehofer, Sigrid; Hauser, Wolfgang; Bräutigam, Klaus-Rainer (Translator); Buchgeister, Jens (Translator); Kopfmüller, Jürgen (Translator)An expert survey about the socio-technical context of the German Energy Transformation is described and selected results are reported. Major socio-technical drivers of the energy system and its evolution were identified, alternative futures for each driver were derived based on literature review and expert questioning. Using the framework of Cross-Impact Balance Analysis, the interrelations between the possible futures of the drivers were estimated by a series of expert interviews.