In the engineering phase of modern manufacturing systems, simulation-based methods and tools have been established to face the increasing demands on time-efficiency and profitability. In the application of these simulation solutions, model-based digital twins are created, as multi-domain simulation models to describe the behavior of the manufacturing system. During the production process, a data-driven digital twin arises in the context of industry 4.0 based on an increasing networking and new cloud technologies. Recent developments in machine learning of fer new possibilities in conjunction with the digital twin. These range from data-based learning of models to learning control logic of complex systems. This paper proposes a combined model-based and data-driven concept of a digital twin. It shows how to use machine learning in connection with these models, in order to archive faster development times of manufacturing systems.
%0 Conference Paper
%1 8600844
%A Jaensch, Florian
%A Csiszar, Akos
%A Scheifele, Christian
%A Verl, Alexander
%B 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)
%D 2018
%K DigitalTwin DigitaleFabrik MachineLearning grk2198 isw myown
%P 1-6
%R 10.1109/M2VIP.2018.8600844
%T Digital Twins of Manufacturing Systems as a Base for Machine Learning
%U https://ieeexplore.ieee.org/document/8600844/
%X In the engineering phase of modern manufacturing systems, simulation-based methods and tools have been established to face the increasing demands on time-efficiency and profitability. In the application of these simulation solutions, model-based digital twins are created, as multi-domain simulation models to describe the behavior of the manufacturing system. During the production process, a data-driven digital twin arises in the context of industry 4.0 based on an increasing networking and new cloud technologies. Recent developments in machine learning of fer new possibilities in conjunction with the digital twin. These range from data-based learning of models to learning control logic of complex systems. This paper proposes a combined model-based and data-driven concept of a digital twin. It shows how to use machine learning in connection with these models, in order to archive faster development times of manufacturing systems.
@inproceedings{8600844,
abstract = {In the engineering phase of modern manufacturing systems, simulation-based methods and tools have been established to face the increasing demands on time-efficiency and profitability. In the application of these simulation solutions, model-based digital twins are created, as multi-domain simulation models to describe the behavior of the manufacturing system. During the production process, a data-driven digital twin arises in the context of industry 4.0 based on an increasing networking and new cloud technologies. Recent developments in machine learning of fer new possibilities in conjunction with the digital twin. These range from data-based learning of models to learning control logic of complex systems. This paper proposes a combined model-based and data-driven concept of a digital twin. It shows how to use machine learning in connection with these models, in order to archive faster development times of manufacturing systems.},
added-at = {2019-01-17T22:17:56.000+0100},
author = {Jaensch, Florian and Csiszar, Akos and Scheifele, Christian and Verl, Alexander},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/208e7136aaad4731582ec69d690483ae6/isw-bibliothek},
booktitle = {2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)},
doi = {10.1109/M2VIP.2018.8600844},
interhash = {5b083907fa1783d06c168d70a4adf590},
intrahash = {08e7136aaad4731582ec69d690483ae6},
keywords = {DigitalTwin DigitaleFabrik MachineLearning grk2198 isw myown},
month = nov,
pages = {1-6},
timestamp = {2020-01-20T14:27:38.000+0100},
title = {Digital Twins of Manufacturing Systems as a Base for Machine Learning},
url = {https://ieeexplore.ieee.org/document/8600844/},
year = 2018
}
