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.
On the role of HLA-based simulation in New Space. - In: 2022 Winter Simulation Conference (WSC), (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.
Optimization of the design of modular production systems. - In: 2022 Winter Simulation Conference (WSC), (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.
Explainable AI for data farming output analysis: a use case for knowledge generation through black-box classifiers. - In: 2022 Winter Simulation Conference (WSC), (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.
Development of an integrated solution for data farming and knowledge discovery in simulation data. - In: Simulation Notes Europe, ISSN 2164-5353, Bd. 32 (2022), 3, S. 121-126
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 was 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 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.