Rautela, Mahindra und Huber, Armin und Senthilnath, J und Gopalakrishnan, S (2021) Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion. Mechanics of Advanced Materials and Structures, Seiten 1-16. Taylor & Francis. doi: 10.1080/15376494.2021.1982090. ISSN 1537-6494.
Dieses Archiv kann nicht den Volltext zur Verfügung stellen.
Kurzfassung
In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i.e., finding layup sequence type and identifying material properties. In the forward problem, polar group velocity representations are obtained for two fundamental Lamb wave modes using the stiffness matrix method. For the inverse problems, a supervised classification-based network is implemented to classify the polar representations into different layup sequence types (inverse problem - 1) and a regression-based network is utilized to identify the material properties (inverse problem -2).
elib-URL des Eintrags: | https://elib.dlr.de/144464/ | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||
Titel: | Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion | ||||||||||||||||||||
Autoren: |
| ||||||||||||||||||||
Datum: | 7 Oktober 2021 | ||||||||||||||||||||
Erschienen in: | Mechanics of Advanced Materials and Structures | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Nein | ||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||
DOI: | 10.1080/15376494.2021.1982090 | ||||||||||||||||||||
Seitenbereich: | Seiten 1-16 | ||||||||||||||||||||
Verlag: | Taylor & Francis | ||||||||||||||||||||
ISSN: | 1537-6494 | ||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | material characterization; property identification; inverse problem; guided waves; deep learning; dualbranch CNN | ||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||
HGF - Programm: | Luftfahrt | ||||||||||||||||||||
HGF - Programmthema: | Umweltschonender Antrieb | ||||||||||||||||||||
DLR - Schwerpunkt: | Luftfahrt | ||||||||||||||||||||
DLR - Forschungsgebiet: | L CP - Umweltschonender Antrieb | ||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | L - Werkstoffe und Herstellverfahren | ||||||||||||||||||||
Standort: | Augsburg | ||||||||||||||||||||
Institute & Einrichtungen: | Institut für Bauweisen und Strukturtechnologie > Automation und Produktionstechnologie | ||||||||||||||||||||
Hinterlegt von: | Huber, Armin | ||||||||||||||||||||
Hinterlegt am: | 12 Okt 2021 14:38 | ||||||||||||||||||||
Letzte Änderung: | 12 Okt 2021 14:38 |
Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags