Stürmer, Marius und Graumann, Marius und Koch, Tobias (2023) Demonstrating Automated Generation of Simulation Models from Engineering Diagrams. In: 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. ICMLA 2023, 2023-12-15 - 2023-12-17, Jacksonville, Florida, USA. doi: 10.1109/ICMLA58977.2023.00173. ISBN 979-835034534-6. ISSN 1946-0759.
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Offizielle URL: https://ieeexplore.ieee.org/document/10459771
Kurzfassung
Digital twins are powerful tools for analysis and representation of physical systems. However, the creation of digital twins including simulation models remains a challenging and time-consuming task, requiring expertise in the domain. Previous work showed algorithms for digitizing engineering diagrams, which is a necessary step for automated simulation model generation. In this paper, we present a comprehensive pipeline for automatically generating simulation models through the digitization of engineering diagrams, exemplified by a simple hydraulic system. For the digitization, we employ several computer vision techniques and deep learning models trained on synthetic data. The demonstrator showcases necessary steps and modules to create a simulation model, and which data has to be available at each working step. Potentially, our approach accelerates the adoption and utilization of digital twin technologies, reducing the time and manual work needed to create simulation models.
elib-URL des Eintrags: | https://elib.dlr.de/203974/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Poster) | ||||||||||||||||
Zusätzliche Informationen: | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||||||
Titel: | Demonstrating Automated Generation of Simulation Models from Engineering Diagrams | ||||||||||||||||
Autoren: |
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Datum: | 2023 | ||||||||||||||||
Erschienen in: | 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Nein | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||
DOI: | 10.1109/ICMLA58977.2023.00173 | ||||||||||||||||
ISSN: | 1946-0759 | ||||||||||||||||
ISBN: | 979-835034534-6 | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | digital twin, automatic model generation, plan digitization, deep learning | ||||||||||||||||
Veranstaltungstitel: | ICMLA 2023 | ||||||||||||||||
Veranstaltungsort: | Jacksonville, Florida, USA | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsbeginn: | 15 Dezember 2023 | ||||||||||||||||
Veranstaltungsende: | 17 Dezember 2023 | ||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Synergieprojekt Automated Model Generation | ||||||||||||||||
Standort: | Rhein-Sieg-Kreis | ||||||||||||||||
Institute & Einrichtungen: | Institut für den Schutz terrestrischer Infrastrukturen > Digitale Zwillinge von Infrastrukturen Institut für den Schutz terrestrischer Infrastrukturen | ||||||||||||||||
Hinterlegt von: | Stürmer, Marius | ||||||||||||||||
Hinterlegt am: | 14 Jun 2024 09:10 | ||||||||||||||||
Letzte Änderung: | 14 Jun 2024 09:10 |
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