Conference Papers

Results: 83
Created on: Sun, 26 Jun 2022 13:55:51 +0200 in 0.0914 sec


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: IEEE Xplore digital library, ISSN 2473-2001, (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
Rohacz, Anke; Straßburger, Steffen;
The acceptance of augmented reality as a determining factor in intralogistics panning. - In: Procedia CIRP, ISSN 2212-8271, Bd. 104 (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: Procedia CIRP, ISSN 2212-8271, 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
Genath, Jonas; Bergmann, Sören; Spieckermann, Sven; Stauber, Stephan; Feldkamp, Niclas;
Development of an integrated solution for data farming and knowledge discovery in simulation data :
Entwicklung einer integrierten Lösung für das Data Farming und die Wissensentdeckung in Simulationsdaten. - In: Simulation in Produktion und Logistik 2021, (2021), S. 377-386
Literaturangaben

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 is 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 for 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.



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