Publikationen - Gesamtliste ab 2007 (ohne Abschlussarbeiten)

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Genath, Jonas; Bergmann, Sören; Straßburger, Steffen; Spieckermann, Sven; Stauber, Stephan
Data farming and knowledge discovery in simulation data : development of an integrated solution
Data Farming und Wissensentdeckung in Simulationsdaten : Entwicklung einer integrierten Lösung. - In: Zeitschrift für wirtschaftlichen Fabrikbetrieb, ISSN 2511-0896, Bd. 117 (2022), 3, S. 144-150

Simulation als Methode der Digitalen Fabrik hat sich seit langem zur Unterstützung der Planung von Produktions- und Logistiksystemen etabliert. In Ergänzung zu bisher vorherrschenden Simulationsstudien wird bei der hier vorgestellten Methode der Wissensentdeckung in Simulationsdaten ein Simulationsmodell als Datengenerator verwendet. Dadurch können mittels Data-Mining- und Visual-Analytics-Methoden versteckte und potenziell nützliche Ursache-Wirkungs-Beziehungen in den generierten Daten aufgedeckt werden. Bislang fehlte es jedoch an integrierten Softwarelösungen für die Praxis.
Feldkamp, Niclas; Bergmann, Sören; Conrad, Florian; Straßburger, Steffen
A method using generative adversarial networks for robustness optimization. - In: ACM transactions on modeling and computer simulation, ISSN 1558-1195, Bd. 32 (2022), 2, S. 12:1-12:22

The evaluation of robustness is an important goal within simulation-based analysis, especially in production and logistics systems. Robustness refers to setting controllable factors of a system in such a way that variance in the uncontrollable factors (noise) has minimal effect on a given output. In this paper, we present an approach for optimizing robustness based on deep generative models, a special method of deep learning. We propose a method consisting of two Generative Adversarial Networks (GANs) to generate optimized experiment plans for the decision factors and the noise factors in a competitive, turn-based game. In a case study, the proposed method is tested and compared to traditional methods for robustness analysis including Taguchi method and Response Surface Method.
Harman, Durmus; Buschmann, Daniel; Scheer, Richard; Hellwig, M.; Knapp, Marc; Schmitt, Robert; Eigenbrod, Hella
Data Analytics Production Line Optimization Model (DAPLOM) - a systematic framework for process optimizations. - In: Production at the leading edge of technology, (2022), S. 412-420

In this paper, we present a new framework for process optimizations, the Data Analytics Production Line Optimization Model (DAPLOM). Due to increasing efforts in the digitalization of production systems, an extensive amount of production data is available for analytics. This data can be used for the optimization of production lines and the prediction of their performance (e.g. drift of parameters or component quality) in order to achieve economic and technical improvements. The demand for systematical usage of data-driven methods involving technologies like Data Analytics and Machine Learning and the combination of engineering approaches is growing continuously.DAPLOM guides the implementation process of IT supported problem-solving solutions in production environments. It combines classical process- with data-driven approaches. Specific focus lies on achieving a holistic perspective with a macro- as well as a microscopic view on the given conditions. Here the macroscopic view covers the general material flow, whereas microscopic view considers process details. Additionally, DAPLOM provides useful methods in a step-by-step procedure structured in seven phases. The framework is validated in an industrial use case of an automated wire bending process. Thus, the effectiveness of the framework is demonstrated and further development potentials are identified.

Genath, Jonas; Bergmann, Sören; Feldkamp, Niclas; Straßburger, Steffen
Automation within the process of knowledge discovery in simulation data : characterization of the result data
Automatisierung im Prozess der Wissensentdeckung in Simulationsdaten : Charakterisierung der Ergebnisdaten. - In: Simulation in Produktion und Logistik 2021, (2021), S. 367-376

The traditional application of simulation in production and logistics is usually aimed at changing certain parameters in order to answer clearly defined objectives or questions. In contrast to this approach, the method of knowledge discovery in simulation data (KDS) uses a simulation model as a data generator (data farming). Subsequently using data mining methods, hidden, previously unknown and potentially useful cause-effect relationships can be uncovered. So far, however, there is a lack of guidelines and automatization-tools for non-experts or novices in KDS, which leads to a more difficult use in industrial applications and prevents a broader utilization. This paper presents a concept for automating the first step of the KDS, which is the process of characterization of the result data, using meta learning and validates it on small case study.

Feldkamp, Niclas;
Data farming output analysis using explainable AI. - In: 2021 Winter Simulation Conference (WSC), (2021), insges. 12 S.

Data Farming combines large-scale simulation experiments with high performance computing and sophisticated big data analysis methods. The portfolio of analysis methods for those large amounts of simulation data still yields potential to further development, and new methods emerge frequently. Especially the application of machine learning and artificial intelligence is difficult, since a lot of those methods are very good at approximating data for prediction, but less at actually revealing their underlying model of rules. To overcome the lack of comprehensibility of such black-box algorithms, a discipline called explainable artificial intelligence (XAI) has gained a lot of traction and has become very popular recently. This paper shows how to extend the portfolio of Data Farming output analysis methods using XAI.
Hafner, Anke; Straßburger, Steffen
The acceptance of augmented reality as a determining factor in intralogistics planning. - In: 54th CIRP CMS 2021, (2021), S. 1209-1214

In the automotive industry, an innovative tool to support the intralogistics planning is essential. One possibility is the use of augmented reality (AR). AR can be a suitable tool for improving the planning of intralogistics processes. An improvement of the intralogistics planning processes can be realized by applying this technology. The application thereby dependents on the acceptance of AR. In this paper, the Technology Acceptance Model and a survey are used to verify the acceptance of AR in intralogistics planning. In addition, relevant factors are identified having an impact on the acceptance using AR in intralogistics.
Kuehner, Kim Jessica; Scheer, Richard; Straßburger, Steffen
Digital Twin: finding common ground - a meta-review. - In: 54th CIRP CMS 2021, Bd. 104 (2021), S. 1227-1232

The concept of the Digital Twin in the context of Industry 4.0 is omnipresent in research concerning manufacturing. However, the understanding of the term varies between applications. Although scholars have previously reviewed research on the topic, a consensus was never reached. Therefore, this paper attempts to compare existing reviews relating to the Digital Twin with the purpose of detecting prevalent as well as contrasting views on key issues. It elucidates commonalities in terminology, conceivable benefits as well as remaining research issues. Hence, it provides a conceptual outline of the Digital Twin that further research can build upon.
Genath, Jonas; Bergmann, Sören; Spieckermann, Sven; Stauber, Stephan; Feldkamp, Niclas
Development of an integrated solution for data farming and knowledge discovery in simulation data :
Entwicklung einer integrierten Lösung für das Data Farming und die Wissensentdeckung in Simulationsdaten. - In: Simulation in Produktion und Logistik 2021, (2021), S. 377-386

Simulation is an established methodology for planning and evaluating manufacturing and logistics systems. In contrast to classical simulation studies, the method of knowledge discovery in simulation data uses a simulation model as a data generator (data farming). Subsequently, hidden, previously unknown and potentially useful cause-effect relationships can be uncovered on the generated data using data mining and visual analytics methods. So far, however, there is a lack of integrated, easy-to-use software solutions for the application of the data farming in operational practice. This paper presents such an integrated solution, which allows for generating experiment designs, implements a method to distribute the necessary experiment runs, and provides the user with tools to analyze and visualize the result data.

Scheer, Richard; Straßburger, Steffen; Knapp, Marc
Concepts for digital-physical connection : comparison, benefits and critical issues
Digital-physische Verbundkonzepte: Gegenüberstellung, Nutzeffekte und kritische Hürden. - In: Simulation in Produktion und Logistik 2021, (2021), S. 11-20

Several concepts for digital-physical connection exist in literature and practice. This paper provides an overview over prevalent concepts. It characterises their specific attributes and places them in contrast with each other. Furthermore, it describes the major benefits as well as the most critical issues in the implementation of these concepts. These potential benefits and issues might then also serve as indicators for further research. From a practical perspective, this paper introduces a straightforward procedure to indicate the appropriate and most efficient concept for any specific implementation of a digital-physical connection system. It bases this indication on the specific requirements of the application.

Römer, Anna Carina;
Simulation-based optimization of energy efficiency in production. - Wiesbaden : Springer Gabler, 2021. - xxviii, 221 Seiten. - (Forschung zur Digitalisierung der Wirtschaft | Advanced Studies in Business Digitization)
Technische Universität Ilmenau, Dissertation 2020

ISBN 978-3-658-32970-9

Die vorliegende Arbeit beschäftigt sich mit der Integration von Energieaspekten in die Produktionssimulation. Die Energieverbräuche von Produktionsanlagen werden in einem Simulationsmodell abgebildet, um diese für die simulationsbasierte Optimierung der Energieeffizienz nutzen zu können und damit eine umfassende Prozessqualität im Hinblick auf den optimalen Energieeinsatz im Produktionsprozess sicherzustellen. Dazu wird ein hybrider Simulationsansatz entwickelt, der verschiedene Simulationsparadigmen in einem Modell kombiniert. Die Hybridisierung von Simulationsmodellen bietet dem Modellersteller eine große Flexibilität bei der Erfassung von Problemen, die sich gleichzeitig auf diskrete und kontinuierliche Strukturen beziehen. Das Simulationsmodell wird dann für Optimierungsexperimente genutzt. Die Grundidee hinter diesem Ansatz ist es, durch mehrere Iterationen, eine optimale Lösung für die zu variierenden Optimierungsparameter zu finden. Die Simulation wird durch die Optimierung gestartet, liefert die Ergebnisdaten und bildet die Grundlage für eine Beurteilung des dynamischen Verhaltens des abgebildeten Produktionssystems. Um die energetischen Aspekte in der Produktion für Optimierungsszenarien zu nutzen, werden lexikographisch geordnete Zielfunktionen abgeleitet, die im Rahmen von simulationsbasierten Optimierungsexperimenten ideale Parameterkonfigurationen für den energieeffizienten Betrieb der Produktion ermitteln. Der Fokus liegt dabei auf der Reduzierung des Energieverbrauchs durch die Vermeidung nicht-wertschöpfender Maschinenzeiten. Die Verbrauchsoptimierung zeigt auf, dass Unternehmen dieser Ressourcenverschwendung durch ein effizientes Schalten der Anlagen entgegenwirken können, ohne finanzielle Investitionen in neue Technologien zu tätigen. Neben der Optimierung des Gesamtenergiebedarfs werden im Rahmen einer Lastspitzen-Optimierung die Maschinenstarts innerhalb eines definierten Zeitraumes so angepasst, dass auftretende Spitzenlasten reduziert werden. Die praktische Anwendung der Methodik zeigt, dass es möglich ist, ein hybrides Simulationsmodel zur Darstellung des Energieverbrauchsverhaltens in der Produktion auf Basis historischer Verbrauchsdaten aufzubauen und in Kombination mit Prognosezahlen auch die zukünftigen Energieverbräuche mit den anstehenden Spitzenlasten und nicht wertschöpfenden Produktionsphasen sehr genau abzubilden.