Classification, input data, and key performance indicators. - In: Energy-related material flow simulation in production and logistics, (2024), S. 3-23
Simulation is a well-known technology for production and logistics, especially for the planning of new systems and the examination of ideas to optimize existing ones. In the past, the main target of such studies has been costs of equipment and personnel, but the continuously stricter view on consumption of energy has shifted this focus towards the analysis of energy consumption and emission of greenhouse gas. In some cases this might be straightforward, e.g., when the resulting production hours can just be multiplied with energy consumption per hour. Many cases, however, are far more complicated and can only be sufficiently analyzed when the detailed dynamics of energy consumption are already considered in the simulation model. Thus, a number of different approaches exist to model energy aspects in simulation models, depending on the goal of the investigation and the kind of production or logistics process. This chapter classifies these approaches in a morphological box and explains the details of the related categories. Furthermore, it discusses the requirements to input data that arise when simulation models are amended with energy components, and discusses the additional results that can be gained from such models.
Energy-related material flow simulation in production and logistics. - Cham : Springer International Publishing, 2024. - 1 Online-Ressource (xiii, 203 p.) ISBN 978-3-031-34218-9
Classification, input data, and key performance indicators -- Manufacturing -- Automotive -- Transportation -- Retail -- Perishables -- Renewables.
How not to visualize your simulation output data. - In: IEEE Xplore digital library, ISSN 2473-2001, (2023), S. 1321-1362
Hybrid modeling and simulation studies combine well-defined methods from other disciplines with a simulation technique. Especially in the area of output data analysis of simulation studies, there is great potential for hybrid approaches that incorporate methods from machine learning and AI. For their successful application, the analytical capabilities of machine learning and AI must be combined with the interpretive capabilities of humans. In most cases, this connection is achieved through visualizations. As methods become more complicated, the demands on visualizations are increasing. In this paper, we conduct a data farming study and delve into the analysis of the output data. In doing so, we uncover typical errors in visualizations making the interpretation and evaluation of the data difficult or misleading. We then apply concepts of visual analytics to these visualizations and derive general guidelines to help simulation users to analyze their simulation studies and present results unambiguously and clearly.
Basic layouts for modular assembly systems - a simulation-based comparison :
Grundlayouts für modulare Montagesysteme - ein simulationsbasierter Vergleich. - In: Simulation in Produktion und Logistik 2023, (2023), S. 197-206
The article discusses the challenges posed by increased individualization of products, shorter product life cycles, and external factors on the flexibility of modern production systems. In particular, flexible workshop-oriented manufacturing principles are being implemented to replace or supplement traditional assembly lines, with various terms such as "modular assembly" and "matrix production" etc. used to describe similar concepts. The article presents these concepts under the umbrella term of modular production or assembly systems, which utilize adaptable workstations and autonomous vehicles to transport production orders between stations. The design of such systems is crucial to their performance, with considerations such as task allocation, material supply, and fleet sizing requiring complex interplay. The article compares traditional matrix layouts with alternative options, such as single-lane pathways and non-matrix layouts like honeycomb or star shapes, using simulationbased analysis to evaluate their potential impact on system performance.
The Hybrid Digital Twin: a practical way to connect simulation with operational production systems :
Der Hybride Digitale Zwilling: eine praxistaugliche Verbindung von Simulation und operationellen Produktionssystemen. - In: Simulation in Produktion und Logistik 2023, (2023), S. 91-101
Digital Twins are currently a topic of much discussion in academia. However, they have yet to be transferred to general industrial practice because there are still significant challenges concerning their implementation. This paper proposes the concept of the Hybrid Digital Twin to address these challenges. At first, it will elucidate the concept and introduce a real-world prototype of an operational production line. Afterwards, it will validate the prototypical implementation and demonstrate a detailed strategy to calibrate it. Then the paper presents potential strategies to use the Hybrid Digital Twin in a production environment. Finally, further developments and remaining issues are discussed.
Simulation in Produktion und Logistik 2023 : ASIM Fachtagung : 20. Fachtagung, 13.-15. September 2023, TU Ilmenau. - Ilmenau : Universitätsverlag Ilmenau, 2023. - 1 Online-Ressource (XII, 485 Seiten). - (ASIM-Mitteilung ; Nr. 187)
From explainable AI to explainable simulation: using machine learning and XAI to understand system robustness. - In: ACM SIGSIM-PADS 2023, (2023), S. 96-106
Evaluating robustness is an important goal in simulation-based analysis. Robustness is achieved when the controllable factors of a system are adjusted in such a way that any possible variance in uncontrollable factors (noise) has minimal impact on the variance of the desired output. The optimization of system robustness using simulation is a dedicated and well-established research direction. However, once a simulation model is available, there is a lot of potential to learn more about the inherent relationships in the system, especially regarding its robustness. Data farming offers the possibility to explore large design spaces using smart experiment design, high performance computing, automated analysis, and interactive visualization. Sophisticated machine learning methods excel at recognizing and modelling the relation between large amounts of simulation input and output data. However, investigating and analyzing this modelled relationship can be very difficult, since most modern machine learning methods like neural networks or random forests are opaque black boxes. Explainable Artificial Intelligence (XAI) can help to peak into this black box, helping us to explore and learn about relations between simulation input and output. In this paper, we introduce a concept for using Data Farming, machine learning and XAI to investigate and understand system robustness of a given simulation model.
Dynamic Time Warping und Synthesedaten zur Validierung von Seq2Seq für die Simulation. - In: ASIM Workshop 2023, (2023), S. 133-142
Seq2Seq is a machine learning method that allows to translate sequences into other sequences. This method has been tried in hybrid simulation of machine tools. The method has been used to generate time series of energy consumption of jobs from the corresponding numerical control code that runs on a machine tool. Seq2Seq suffers from various problems. Firstly, the creation of training data is costly. Secondly, standard Seq2Seq metrics only allow for the evaluation of a prediction of one timestamp at a time, not an entire time series. Thirdly, training metrics are failing when vanilla data is used, as two identical numerical control codes can result in deviating time series. This causes confusion for the model in the training loop, as it is not clear which time series should be considered correct. Here we propose a holistic framework to all three problems, that contains synthetic data, additional metrics for time series and dynamic time warping.
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.
DaWiS - Entwicklung einer integrierten Lösung für das Data Farming und die Wissensentdeckung in Simulationsdaten : Abschlussbericht des im Rahmen der Fördermaßnahme KMU-innovativ: IKT geförderten Verbundprojekts : (Projektlaufzeit: 01.01.2020-31.12.2021). - Ilmenau : Technische Universität Ilmenau - Fachgebiet Informationstechnik in Produktion und Logistik (ITPL). - 1 Online-Ressource (31 Seiten, 2,81 MB)Förderkennzeichen BMBF 01 IS 190 42B