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.
https://doi.org/10.1016/j.procir.2021.11.203
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.
https://doi.org/10.1016/j.procir.2021.11.206
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
Literaturangaben
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.
Feldkamp, Niclas;
Bergmann, Sören; Straßburger, Steffen Simulation-based deep reinforcement learning for modular production systems. - In: 2020 Winter Simulation Conference (WSC), (2020), S. 1596-1607
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. Since each product can define its way through the system freely and individually, implementing rules and heuristics that leverage the flexibility in the system in order to increase performance can be difficult in this dynamic environment. Transport tasks are usually carried out by automated guided vehicles (AGVs). Therefore, integration of AI-based control logics offer a promising alternative to manually implemented decision rules for operating the AGVs. This paper presents an approach for using reinforcement learning (RL) in combination with simulation in order to control AGVs in modular production systems. We present a case study and compare our approach to heuristic rules.
https://doi.org/10.1109/WSC48552.2020.9384089
Wörrlein, Benjamin;
Straßburger, Steffen A method for predicting high-resolution time series using sequence-to-sequence models. - In: 2020 Winter Simulation Conference (WSC), (2020), S. 1075-1086
With the increasing availability of data, the desire to interpret that data and use it for behavioral predictions arises. Traditionally, simulation has used data about the real system for input data analysis or within data-driven model generation. Automatically extracting behavioral descriptions from the data and representing it in a simulation model is a challenge of these approaches. Machine learning on the other hand has proven successful to extract knowledge from large data sets and transform it into more useful representations. Combining simulation approaches with methods from machine learning seems therefore promising to combine the strengths of both approaches. Representing some aspects of a real system by a traditional simulation model and others by a model incorporating machine learning, a hybrid system model (HSM) is generated. This paper suggests a specific HSM incorporating a deep learning method for predicting the anticipated power usage of machining jobs.
https://doi.org/10.1109/WSC48552.2020.9383969
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
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
