Kongress- und Tagungsbeiträge

Anzahl der Treffer: 90
Erstellt: Thu, 02 Feb 2023 23:03:22 +0100 in 0.0858 sec


Scheer, Richard; Straßburger, Steffen; Knapp, Marc
Hybridization of the Digital Twin - overcoming implementation challenges. - In: Proceedings of the 56th Annual Hawaii International Conference on System Sciences, (2023), S. 1438-1447

In the context of Industry 4.0 the concept of the Digital Twin has gained significant momentum in industry as well as academia. Researchers have hypothesized a great number of potential benefits of the concept's usage. However, few real-world implementations have been recorded. This paper addresses the most pressing challenges inhibiting the concept's industrial application. It describes the process of the concept's hybridization to achieve a practical implementation strategy: the Hybrid Digital Twin. Subsequently, a prototype is implemented using a presently operational real-world manufacturing system to substantiate the viability of the methodology. Finally, the benefits, remaining issues and future developments of the concept are discussed.



https://hdl.handle.net/10125/102810
Morlang, Frank; Straßburger, Steffen
On the role of HLA-based simulation in New Space. - In: IEEE Xplore digital library, ISSN 2473-2001, (2022-), S. 430-440

This paper discusses High Level Architecture (HLA) based simulation in the context of the emergence of the private spaceflight industry called New Space. We postulate that distributed simulation plays a fundamental role in facilitating new opportunities of a cost efficient access to space. HLA defines a simulation system's architecture framework with a focus on reusability and interoperability. The article will therefore discuss the impact of its usage on the potential of affordable new aerospace systems developments. Future possibilities with an increased level of loose component coupling are presented.



https://doi.org/10.1109/WSC57314.2022.10015338
Bergmann, Sören;
Optimization of the design of modular production systems. - In: IEEE Xplore digital library, ISSN 2473-2001, (2022), S. 1783-1793

The desire for more flexibility in manufacturing systems, especially when different products or many product variants are manufactured in one production system is leading to a move away from the manufacturing principle of classic line production to more flexible and workshop-oriented production systems, particularly in the automotive industry. One of the challenges in these so-called modular assembly or production systems is the system design, especially the allocation of activities to the individual production cells. One approach to improve this allocation is offered by simulation-based optimization. In this paper, a concept for simulation-based optimization of the design of modular production systems is presented and demonstrated by means of a small academic case study. Classical genetic algorithms and additionally the NSGA-II algorithm, which also allows multi-objective optimization, are used.



https://doi.org/10.1109/WSC57314.2022.10015350
Feldkamp, Niclas; Genath, Jonas; Straßburger, Steffen
Explainable AI for data farming output analysis: a use case for knowledge generation through black-box classifiers. - In: IEEE Xplore digital library, ISSN 2473-2001, (2022), S. 1152-1163

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. Among the most interesting are methods of explainable artificial intelligence (XAI). Those methods enable the use of black-box-classifiers for data farming output analysis, which has been shown in a previous paper. In this paper, we apply the concept for XAI-based data farming analysis on a complex, real world case study to investigate the suitability of such concept in a real world application, and we also elaborate on which black-box classifiers are actually the most suitable for large-scale simulation data that accumulates in a data farming project.



https://doi.org/10.1109/WSC57314.2022.10015304
Wörrlein, Benjamin; Straßburger, Steffen
Hochaufgelöste Energieprofile durch hybride Simulation. - In: ASIM SST 2022 Proceedings Langbeiträge, (2022), S. 243-251

The price of a commodity, as electricity, is determined on a commodity market. A market is efficient when the supply and demand in the market are at an equilibrium. Efficient markets run on information. Information can cause a spontaneous and instantaneous change within the supply and demand in a market. The market communicates this new equilibrium through the change of the price of a commodity. In the electricity market the supplier and consumer communicate through electrical load profiles. A load profile signals when and how much energy should be consumed within a certain time frame without causing a change in the price of electricity. Creating such load profiles is commonly done by the supplier of energy by means of standard load profiles. Here we propose a data-driven simulation-based method that allows for the consumer to create its own specific load profile, which potentially will bring down the cost of energy consumed.



https://dx.doi.org/10.11128/arep.20.a2004
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen
Simulationsbasiertes Deep Reinforcement Learning für Modulare Produktionssysteme. - In: ASIM SST 2022 Proceedings Langbeiträge, (2022), S. 65-72

Modulare Produktionssysteme sollen die traditionelle Fließbandproduktion in der Automobilindustrie ablösen. Die Idee dabei ist, dass sich hochgradig individualisierte Produkte dynamisch und autonom durch ein System flexibler Arbeitsstationen bewegen können. Dieser Ansatz stellt hohe Anforderungen an die Planung und Organisation solcher Systeme. Da jedes Produkt seinen Weg durch das System frei und individuell bestimmen kann, kann die Implementierung von Regeln und Heuristiken, die die Flexibilität des Systems zur Leistungssteigerung ausnutzen, in diesem dynamischen Umfeld schwierig sein. Transportaufgaben werden in der Regel von fahrerlosen Transportsystemen (FTS) ausgeführt. Daher bietet die Integration von KI-basierten Steuerungslogiken eine vielversprechende Alternative zu manuell implementierten Entscheidungsregeln für den Betrieb der FTS. In diesem Beitrag wird ein Ansatz für den Einsatz von Reinforcement Learning (RL) in Kombination mit Simulation vorgestellt, um FTS in modularen Produktionssystemen zu steuern. Darüber hinaus werden Untersuchungen zu dessen Flexibilität und Skalierbarkeit durchgeführt.



https://dx.doi.org/10.11128/arep.20.a2007
Bergmann, Sören;
Optimierung des Designs modularer Montagesysteme. - In: ASIM SST 2022 Proceedings Langbeiträge, (2022), S. 15-22

Der Wunsch nach mehr Flexibilität in Fertigungssystemen, insbesondere, wenn verschiedene Produkte bzw. viele Produktvarianten in einem Produktionssystem gefertigt werden, führt, besonders in der Automobilindustrie, zur Abkehr vom Fertigungsprinzip der klassischen Linienfertigungen hin zu eher flexiblen und werkstattorientierten Produktionssystemen. Eine der Herausforderungen in diesen so genannten modularen Montage- bzw. Produktionssystemen ist das Systemdesign, insbesondere die Zuordnung der Tätigkeiten auf die einzelnen Fertigungsinseln. Ein Ansatz, diese Zuordnung zu verbessern bietet die simulationsbasierte Optimierung. In diesem Beitrag wird ein Konzept zur simulationsbasierten Optimierung des Designs modularer Montagesysteme vorgestellt und anhand einer Fallstudie demonstriert. Zum Einsatz kommen hierbei genetische Algorithmen, speziell der NSGA-II-Algorithmus, welcher auch mehrkriterielle Optimierung ermöglicht.



https://dx.doi.org/10.11128/arep.20.a2006
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
Literaturangaben

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.



https://doi.org/10.1109/WSC52266.2021.9715470