Publikationen - Gesamtliste ab 2007 (ohne Abschlussarbeiten)

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Wörrlein, Benjamin; Straßburger, Steffen
Sequence to Sequence Modelle zur hochaufgelösten Prädiktion von Stromverbrauch. - In: Proceedings ASIM SST 2020, (2020), S. 149-157

Modelling power consumption for jobs on a ma-chine tool is commonly performed by measuring the real power consumption of comparable jobs and machines. The so gathered data is then processed to represent the time-av-eraged sums of power consumptions of previous jobs. These values of power consumption are then used for upcoming comparable jobs. This approach allows for no high-resolution prediction of power consumption and further presumes static processing times of jobs. Here we propose a new approach to model power consumption that incorporates a Sequence-to-Sequence model, which generates time series according to dynamic data, that describes a numerical control code and environment settings such as state of tools, etc.



https://doi.org/10.11128/arep.59.a59021
Hafner, Anke; Weißenfels, Stefan; Straßburger, Steffen
Concept for the comparison of intralogistics designs with real factory layout using augmented reality, SLAM and marker-based tracking. - In: 53rd CIRP Conference on Manufacturing Systems 2020, (2020), S. 341-346

In the automotive industry, the intralogistics planning faces the problem of matching the planning data with the current conditions in the assembly hall. The large variety of parts leads to a constantly changing production. Based on this, we establish an approach for the comparison using augmented reality (AR) and simultaneous localization and mapping (SLAM). The use of SLAM enables the consistent application of AR in an assembly hall. Based on this, the objective of this article is to visualize 3D objects from the corresponding CAD planning tool in the real factory and thus the comparison of the intralogistics design with the real factory is possible due to AR. Nevertheless, there was a lack of practical implementations in intralogistics and therefore the concept is evaluated by two prototypical solutions. The first one is implemented on an iPhone 7 using SLAM. The second prototype is developed on a HoloLens 2 and is based on a hybrid tracking solution, SLAM and marker tracking.



https://doi.org/10.1016/j.procir.2020.03.039
Bergmann, Sören; Feldkamp, Niclas; Conrad, Florian; Straßburger, Steffen
A method for robustness optimization using generative adversarial networks. - In: SIGSIM-PADS '20, (2020), S. 1-10

This paper presents an approach for optimizing the robustness of production and logistic systems based on deep generative models, a special method of deep learning. Robustness here refers to setting controllable factors of a system in such a way that variance in the uncontrollable factors (noise) has a minimal effect on given output parameters. In a case study, the proposed method is tested and compared to a traditional method for robustness analysis. The basic idea is to use deep neural networks to generate data for experiment plans and rate them by use of a simulation model of the production system. We propose to use two Generative Adversarial Networks (GANs) to generate optimized experiment plans for the decision factors and the noise factors, respectively, in a competitive, turn-based game. In one turn, the controllable factors are optimized and the noise remains constant, and vice versa in the next turn. For the calculations of the robustness, the planned experiments are conducted and rated using a simulation model in each learning step.



https://doi.org/10.1145/3384441.3395981
Bergmann, Sören; Straßburger, Steffen
Automatische Modellgenerierung - Stand, Klassifizierung und ein Anwendungsbeispiel. - In: Ablaufsimulation in der Automobilindustrie, (2020), S. 333-347

Die automatische Modellgenerierung (AMG) ist ein Ansatz, der darauf abzielt, sowohl die Aufwände einer Simulationsstudie zu senken als auch die Qualität der erzeugten Modelle zu verbessern. Unter automatischer Modellgenerierung werden im Kontext der Simulation verschiedene Ansätze subsumiert, die es erlauben, Simulationsmodelle oder zumindest Teile von Simulationsmodellen mittels Algorithmen zu erzeugen. Eine umfassende Klassifizierung der Ansätze nach verschiedenen Merkmalen ist Ausgangspunkt weiterer Betrachtungen des Beitrags, in denen u. a. verschiedene technische Ansätze zur Modellgenerierung diskutiert werden. Weiterhin werden ergänzende Techniken, die die eigentliche Modellgenerierung flankierenden, wie z. B. die automatische Modellinitialisierung, diskutiert. Als ein möglicher Lösungsansatz wird beispielhaft ein Framework zur automatischen Modellgenerierung, -initialisierung und -adaption, welches das standardisierte Core Manufacturing Simulation Data (CMSD) Format als Basis nutzt, beschrieben.



https://doi.org/10.1007/978-3-662-59388-2_23
Feldkamp, Niclas;
Wissensentdeckung im Kontext der Produktionssimulation. - Ilmenau : Universitätsverlag Ilmenau, 2020. - 1 Online-Ressource (XII, 217, XIV-XX Seiten)
Technische Universität Ilmenau, Dissertation 2019

Die diskrete Simulation stellt eine wichtige und etablierte Methode zur Untersuchung des dynamischen Verhaltens von komplexen Produktions- und Logistiksystemen dar. Sie ist daher zur Planung, Steuerung und Kontrolle solcher Systeme unerlässlich, beispielsweise in der Automobilindustrie oder in der Halbleiterfertigung. Klassische Simulationsstudien zielen in diesem Kontext üblicherweise darauf ab, typische, vorab definierte Fragestellungen zu beantworten. Dies geht oftmals einher mit der Simulation und Analyse einiger weniger vorab definierter Szenarien. Wirkzusammenhänge, die über diesen definierten Projektrahmen hinausgehen, bleiben daher eventuell unentdeckt. Auf der anderen Seite erwachsen mit steigender Rechenleistung und der allgemeinen Verfügbarkeit von Big-Data-Infrastrukturen neue Möglichkeiten zur Durchführung von sehr großen Bandbreiten von Simulationsexperimenten, um das Verhalten des Modells möglichst vollständig abzudecken und automatisiert auszuwerten. Dies wird allgemein als Data Farming bezeichnet. Ziel dieser Arbeit war es, die Methode des Data Farming für die Nutzung zur Wissensentdeckung in Produktionssimulationen zu übertragen und weiterzuentwickeln. Dazu wurde ein ganzheitliches Konzept ausgearbeitet, um unbekannte, versteckte und potenziell nützliche Wirkzusammenhänge in großen Mengen von Simulationsdaten entdecken zu können. Das Konzept beinhaltet hierzu die Auswahl geeigneter Experimentdesignmethoden, die Anwendung und Ausgestaltung von geeigneten Data-Mining-Verfahren in einem dafür zweckmäßigen und zielgerichteten Analyseprozess sowie die Definition geeigneter Visualisierungs- und Interaktionsmethoden zur iterativen, anwenderorientierten Analyse großer Mengen von Simulationsdaten. Darüber hinaus wurde das Konzept in einem ganzheitlichen Softwareframework prototypisch implementiert. Die Anwendbarkeit des Konzeptes wurde anhand von vier Fallstudien aufgezeigt und validiert. Die Fallstudien beinhalteten hierbei zwei akademische Laborstudien sowie zwei Industrieanwendungsfälle.



https://www.db-thueringen.de/receive/dbt_mods_00040526
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen; Schulze, Thomas
Visualization and interaction for knowledge discovery in simulation data. - In: Hawaii International Conference on System Sciences 2020, (2020), S. 1340-1349

Discrete-event simulation is an established and popular technology for investigating the dynamic behavior of complex manufacturing and logistics systems. Besides traditional simulation studies that focus on single model aspects, data farming describes an approach for using the simulation model as a data generator for broad scale experimentation with a broader coverage of the system behavior. On top of that we developed a process called knowledge discovery in simulation data that enhances the data farming concept by using data mining methods for the data analysis. In order to uncover patterns and causal relationships in the model, a visually guided analysis then enables an exploratory data analysis. While our previous work mainly focused on the application of suitable data mining methods, we address suitable visualization and interaction methods in this paper. We present those in a conceptual framework followed by an exemplary demonstration in an academic case study.



https://doi.org/10.24251/HICSS.2020.165
Römer, Anna Carina; Straßburger, Steffen
Hybrid system modeling approach for the depiction of the energy consumption in production simulations. - In: 2019 Winter Simulation Conference (WSC), (2019), S. 1366-1377

In many industrial manufacturing companies, energy has become a major cost factor. Energy aspects are included in the decision-making system of production planning and control to reduce manufacturing costs. For this priority, the simulation of production processes requires not only the consideration of logistical and technical production factors but also the integration of time-dependent energy flows which are continuous in nature. A hybrid simulation, using a continuous approach to depict the energy demand of production processes in combination with a discrete approach to map the material flows and logistic processes, shows the complex interactions between material flow and energy usage in production closer to reality. This paper presents a hybrid simulation approach combining System Dynamics, Discrete-Event and Agent-Based Simulation for energy efficiency analysis in production, considering the energy consumption in the context of planning and scheduling operations and applying it to a use-case scenario of mechanical processing of die-cast parts.



https://doi.org/10.1109/WSC40007.2019.9004772
Bell, David; Groen, Derek; Mustafee, Navonil; Ozik, Jonathan; Straßburger, Steffen
Hybrid simulation development - is it just analytics?. - In: 2019 Winter Simulation Conference (WSC), (2019), S. 1352-1365
Richtiger Name des 5. Verfassers: Steffen Straßburger

Hybrid simulations can take many forms, often connecting a diverse range of hardware and software components with heterogeneous data sets. The scale of examples is also diverse with both the high-performance computing community using high-performance dataanalytics (HPDA) to the synthesis of software libraries or packages on a single machine. Hybrid simulation configuration and output analysis is often akin to analytics with a range of dashboards, machine learning, data aggregations and graphical representation. Underpinning the visual elements are hardware, software and data architectures that execute hybrid simulation code. These are wide ranging with few generalized blueprints, methods or patterns of development. This panel will discuss a range of hybrid simulation development approaches and endeavor to uncover possible strategies for supporting the development and coupling of hybrid simulations.



https://doi.org/10.1109/WSC40007.2019.9004923
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen
Modelling and simulation of modular production systems :
Modellierung und Simulation von modularen Produktionssystemen. - In: Simulation in Produktion und Logistik 2019, (2019), S. 391-401

Modular production systems aim to supersede the traditional line production in the automobile industry. The idea here is that highly customized products can move dynamically and autonomously through a system of flexible workstations without fixed production cycles. This approach has challenging demands regarding planning and organization of such systems. The use of modelling and simulation methods is therefore indispensable. This paper presents simulation approaches for modelling modular production systems and discusses a comparison between an agent-based and a process-oriented implementation of an example model.



Wörrlein, Benjamin; Bergmann, Sören; Feldkamp, Niclas; Straßburger, Steffen
Deep learning based prediction of energy consumption for hybrid simulation :
Deep-Learning-basierte Prognose von Stromverbrauch für die hybride Simulation. - In: Simulation in Produktion und Logistik 2019, (2019), S. 121-131

Modern production facilities need to prepare for changing market conditions within the energy market due to ongoing implementation of governmental policies. This results in higher volatility of the availability of energy and therefore energy costs. If a simulation model of a machinery model can estimate its own future consumption, and according time frames for said consumption, this information could be used for optimized scheduling of energy consuming jobs. This would result in lower procurement costs. To make said estimation about the dynamic behaviour of jobs, methods of time series prediction tend to be applied. Here a proposal is made to apply a Hybrid System Model incorporating a recurrent neural network (RNN)-Encoder-Decoder-Architecture, which returns a discrete times series when a behavioural sequence (such as an NC-Code) has been put into a neural net model of the respective machinery. Those discrete time series reflect the machines energy consumption for each job that it has been operated on. This neural net, if weighted and called, emits the length value of a job and an according time series which displays the quasi-continuous time consumption of said job. Such generative models combined with classic simulation paradigm qualify as potent applications of hybrid simulation approaches.