Universität Stuttgart
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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 Simulation model for digital twins of pneumatic vacuum ejectors(2022) Stegmaier, Valentin; Schaaf, Walter; Jazdi, Nasser; Weyrich, MichaelIncreasing productivity, as well as flexibility, is required for the industrial production sector. To meet these challenges, concepts in the field of “Industry 4.0” are arising, such as the concept of Digital Twins. Vacuum handling systems are a widespread technology for material handling in industry and face the same challenges and opportunities. In this field, a key issue is the lack of Digital Twins containing behavior models for vacuum handling systems and their components in different applications and use cases. A novel concept for modeling and simulating the fluidic behavior of pneumatic vacuum ejectors as key components of vacuum handling systems is proposed. In order to increase the simulation accuracy, the concept can access instance‐specific data of the used asset instead of object‐specific data. The model and the data are part of the Digital Twins of pneumatic vacuum ejectors, which shall be able to be combined with other components to represent a Digital Twin of entire vacuum handling systems. The proposed model is validated in an experimental test setup and in an industrial application delivering sufficiently accurate results.Item Open Access Modell zum maschinellen Lernen von Wirkzusammenhängen bei der Holzverarbeitung auf Basis von online-erfassten Werkzeugmaschinendaten(Stuttgart : Fraunhofer Verlag, 2018) Lenz, Jürgen Herbert; Westkämper, Engelbert (Univ.-Prof. a. D. Dr.-Ing. Prof. E.h. Dr.-Ing. E.h. Dr. h.c. mult.)Aufgrund des immer härter werdenden globalen Wettbewerbs müssen produzierende Unternehmen, die auch in der Zukunft profitabel produzieren wollen, ihre Leistungsreserven nutzten. Die Möbelfertigung, die größte holzverarbeitende Industrie, besteht im Hauptprozess aus dem Fräsen von Holzwerkstoffen. Hierbei gibt es Leistungsreserven in der Einsatzplanung der Fräswerkzeuge. Gute Einsatzplanung ist die Voraussetzung für eine hohe Verfügbarkeit des Produktionssystems. Die Einsatzplanung wird durch Entwicklungen wie individuelle Möbelstücke, kleinere Losgrößen und neue Schneidstoffe erschwert. Die Herausforderung der Planungsunsicherheit beim Werkzeugeinsatz in der Holzbearbeitung wächst zusätzlich durch die größere Anzahl an industriell hergestellten Holzwerkstoffen mit jeweils unterschiedlicher Abrasivität. Dadurch wird die Bestimmung der Reststandzeit eines Werkzeuges erschwert. Zielsetzung dieser Arbeit ist die Planungssicherheit des Werkzeugeinsatzes durch eine exakte Planung des Werkzeugwechselfensters sowie durch Prognose der Reststandzeit zu erhöhen. Mithilfe dieser Prognose kann das gesamte Standvermögen des Werkzeuges verwendet werden. Das führt dazu, dass die Verfügbarkeit des Produktionssystems erhöht wird, da durch das Überschreiten der Werkzeugeinsatzgrenze bedingte Stillstände vermieden werden. Hierfür wurde ein Modell erstellt, das online erfasste Daten aus der Werkzeugmaschinensteuerung mit kontextbezogenen Informationen aus Datenbanken wie dem ERP-System und der Werkzeugverwaltung kombiniert. Aus diesen Informationen wird eine werkzeugspezifische Einsatzhistorie gebildet und mit gemessenen physikalischen Werten über den Werkzeugverschleiß und Kantenqualität des Werkstückes in Verbindung gebracht. Diese Verbindung von Bearbeitungshistorie und echten physikalischen Messgrößen bilden die Datenbasis für das maschinelle Lernen von Wirkzusammenhängen. Durch das Erlernen dieser Zusammenhänge kann die Reststandzeit eines Werkzeuges prognostiziert werden und somit die Planungsgenauigkeit des Werkzeugeinsatzes durch exakte Festlegung von Werkzeugwechselfenstern gesteigert werden. Zur Erprobung wurde das entwickelte Modell implementiert und seine Funktionsfähigkeit anhand einer Werkstoff-/Schneidstoffpaarung validiert. Diese Erprobung zeigte dass die Wirkzusammenhänge erlernt werden können.Item Open Access Scheduling & routing time-triggered traffic in time-sensitive networks(2018) Nayak, Naresh Ganesh; Rothermel, Kurt (Prof. Dr. rer. nat. Dr. h. c.)The application of recent advances in computing, cognitive and networking technologies in manufacturing has triggered the so-called fourth industrial revolution, also referred to as Industry 4.0. Smart and flexible manufacturing systems are being conceived as a part of the Industry 4.0 initiative to meet the challenging requirements of the modern day manufacturers, e.g., production batch sizes of one. The information and communication technologies (ICT) infrastructure in such smart factories is expected to host heterogeneous applications ranging from the time-sensitive cyber-physical systems regulating physical processes in the manufacturing shopfloor to the soft real-time analytics applications predicting anomalies in the assembly line. Given the diverse demands of the applications, a single converged network providing different levels of communication guarantees to the applications based on their requirements is desired. Ethernet, on account of its ubiquity and its steadily growing performance along with shrinking costs, has emerged as a popular choice as a converged network. However, Ethernet networks, primarily designed for best-effort communication services, cannot provide strict guarantees like bounded end-to-end latency and jitter for real-time traffic without additional enhancements. Two major standardization bodies, viz., the IEEE Time-sensitive Networking (TSN) Task Group (TG) and the IETF Deterministic Networking (DetNets) Working Group are striving towards equipping Ethernet networks with mechanisms that would enable it to support different classes of real-time traffic. In this thesis, we focus on handling the time-triggered traffic (primarily periodic in nature) stemming from the hard real-time cyber-physical systems embedded in the manufacturing shopfloor over Ethernet networks. The basic approach for this is to schedule the transmissions of the time-triggered data streams appropriately through the network and ensure that the allocated schedules are adhered with. This approach leverages the possibility to precisely synchronize the clocks of the network participants, i.e., end systems and switches, using time synchronization protocols like the IEEE 1588 Precision Time Protocol (PTP). Based on the capabilities of the network participants, the responsibility of enforcing these schedules can be distributed. An important point to note is that the network utilization with respect to the time-triggered data streams depends on the computed schedules. Furthermore, the routing of the time-triggered data streams also influences the computed transmission schedules, and thus, affects the network utilization. The question however remains as to how to compute transmission schedules for time-triggered data streams along with their routes so that an optimal network utilization can be achieved. We explore, in this thesis, the scheduling and routing problems with respect to the time-triggered data streams in Ethernet networks. The recently published IEEE 802.1Qbv standard from the TSN-TG provides programmable gating mechanisms for the switches enabling them to schedule transmissions. Meanwhile, the extensions specified in the IEEE 802.1Qca standard or the primitives provided by OpenFlow, the popular southbound software-defined networking (SDN) protocol, can be used for gaining an explicit control over the routing of the data streams. Using these mechanisms, the responsibility of enforcing transmission schedules can be taken over by the end systems as well as the switches in the network. Alternatively, the scheduling can be enforced only by the end systems or only by the switches. Furthermore, routing alone can also be used to isolate time-triggered data streams, and thus, bound the latency and jitter experienced by the data streams in absence of synchronized clocks in the network. For each of the aforementioned cases, we formulate the scheduling and routing problem using Integer Linear Programming (ILP) for static as well as dynamic scenarios. The static scenario deals with the computation of schedules and routes for time-triggered data streams with a priori knowledge of their specifications. Here, we focus on computing schedules and routes that are optimal with respect to the network utilization. Given that the scheduling problems in the static setting have a high time-complexity, we also present efficient heuristics to approximate the optimal solution. With the dynamic scheduling problem, we address the modifications to the computed transmission schedules for adding further or removing already scheduled time-triggered data streams. Here, the focus lies on reducing the runtime of the scheduling and routing algorithms, and thus, have lower set-up times for adding new data streams into the network.Item Open Access IDEA - towards an interactive tool that supports creativity sessions in automotive product development(2023) Kaschub, Verena Lisa; Wechner, Reto; Krautmacher, Lara; Diers, Daniel; Bues, Matthias; Lossack, Ralf; Kloos, Uwe; Riedel, OliverItem Open Access Smart Engineering Apps für eine mobile und situationssensitive Bereitstellung von Engineeringdaten(2019) Hoos, Eva; Mitschang, Bernhard (Prof. Dr.-Ing.)Globale Megatrends wie steigende Produktindividualisierung, kürzere Produktlebenszyklen und steigende Produktkomplexität führen zu erheblichen Herausforderungen im Engineering, also in der Produkt- und Produktionsprozessentwicklung. Die Informationsbereitstellung in der Engineeringdomäne ist zeitaufwendig und komplex, deshalb werden Engineeringprozesse nur unzureichend unterstützt. Gleichzeitig wird das Potenzial neuer Technologien wie mobile Apps oder situationssensitive Anwendungen bisher nicht genutzt. Um dies zu adressieren, stehen im Zentrum dieser Arbeit Smart Engineering Apps (SEA), die eine mobile und situationsabhängige Bereitstellung von Engineeringdaten ermöglichen. Die Beiträge der Doktorarbeit umfassen sowohl die Konzeption und prototypische Implementierung von SEAs als auch die Entwicklung von Methoden und Konzepten für deren strategischen Einsatz und Entwurf. Die Beiträge lassen sich in drei Teilbereiche untergliedern: (1) Es wird eine systematische, prozessorientierte Analysemethode zur Identifikation von App-Potenzialen bereitgestellt. Die Methode wird auf mehrere Engineeringprozesse angewendet, um eine Übersicht über App-Potenziale und den möglichen Geschäftsnutzen zu erhalten. (2) Für die systematische Konzeption der Situationssensitivität wird ein Entwurfsframework zur Verfügung gestellt. Es stellt sowohl ein Metamodell für Situationen als auch Entwicklungsmethoden und -bausteine für die Modellierung und Erfassung von Situationen bereit. (3) Die situationsabhängige Bereitstellung von Engineeringdaten stellt eine Kernfunktionalität von SEAs dar. Das erarbeitete Konzept ermöglicht die Realisierung von SEAs, die Anwendern nur die Engineeringdaten zur Verfügung stellen, die sie für ihre aktuelle Aufgabe benötigen. Auf Basis der entwickelten Konzepte und Methoden werden zwei SEAs realisiert. Die Evaluation der Forschungsbeiträge erfolgt durch die Anwendung der Konzepte und Methoden bei deren Entwurf. Damit wird gezeigt, dass die erarbeiteten Forschungsbeiträge in realen Anwendungsfällen einsetzbar sind und dass sie den Entwurf von SEAs unterstützen. Eine Expertenevaluation zeigt, dass durch SEAs Prozessverbesserungen erreicht werden können. Zusammenfassend kann gesagt werden, dass SEAs die Herausforderungen der Informationsbereitstellung im Engineering adressieren. Durch die entwickelten Lösungskonzepte werden der Entwurf und die Konzeption von SEAs unterstützt. Deren Einsatz liefert einen Beitrag für Prozessverbesserungen im Engineering.Item Open Access Augmented reality to visualize a finite element analysis for assessing clamping concepts(2024) Maier, Walther; Möhring, Hans-Christian; Feng, Qi; Wunderle, RichardThis paper presents the development of an innovative augmented reality application for evaluating clamping concepts through visualizing the finite element analysis. The focus is on transforming the traditional simulation results into immersive, holographic displays, enabling users to experience and assess finite element analysis in three dimensions. The application development process involves data processing by MATLAB, visualization in the software Unity, and displaying holograms through Microsoft’s Hololens2. The most significant advancement introduces a new algorithm for rendering different finite elements in Unity. The application targets not only university engineering students but also vocational students with limited background in finite element analysis and machining, aiming to make the learning process more interactive and engaging. It was tested in a real machining environment, demonstrating its technical feasibility and potential in engineering education.Item Open Access Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification(2023) Hirsch, Vitali; Reimann, Peter; Treder-Tschechlov, Dennis; Schwarz, Holger; Mitschang, BernhardReal-world data of multi-class classification tasks often show complex data characteristics that lead to a reduced classification performance. Major analytical challenges are a high degree of multi-class imbalance within data and a heterogeneous feature space, which increases the number and complexity of class patterns. Existing solutions to classification or data pre-processing only address one of these two challenges in isolation. We propose a novel classification approach that explicitly addresses both challenges of multi-class imbalance and heterogeneous feature space together. As main contribution, this approach exploits domain knowledge in terms of a taxonomy to systematically prepare the training data. Based on an experimental evaluation on both real-world data and several synthetically generated data sets, we show that our approach outperforms any other classification technique in terms of accuracy. Furthermore, it entails considerable practical benefits in real-world use cases, e.g., it reduces rework required in the area of product quality control.