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Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion

Rautela, Mahindra and Huber, Armin and Senthilnath, J and 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, pp. 1-16. Taylor & Francis. doi: 10.1080/15376494.2021.1982090. ISSN 1537-6494.

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Abstract

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).

Item URL in elib:https://elib.dlr.de/144464/
Document Type:Article
Title:Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Rautela, Mahindramrautela (at) iisc.ac.inhttps://orcid.org/0000-0002-2678-9682
Huber, ArminArmin.Huber (at) dlr.dehttps://orcid.org/0000-0002-5694-8293
Senthilnath, JUNSPECIFIEDhttps://orcid.org/0000-0002-1737-7985
Gopalakrishnan, SUNSPECIFIEDhttps://orcid.org/0000-0001-6165-6132
Date:7 October 2021
Journal or Publication Title:Mechanics of Advanced Materials and Structures
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1080/15376494.2021.1982090
Page Range:pp. 1-16
Publisher:Taylor & Francis
ISSN:1537-6494
Status:Published
Keywords:material characterization; property identification; inverse problem; guided waves; deep learning; dualbranch CNN
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Clean Propulsion
DLR - Research area:Aeronautics
DLR - Program:L CP - Clean Propulsion
DLR - Research theme (Project):L - Advanced Materials and New Manufacturing Technologies
Location: Augsburg
Institutes and Institutions:Institute of Structures and Design > Automation and Production Technology
Deposited By: Huber, Armin
Deposited On:12 Oct 2021 14:38
Last Modified:12 Oct 2021 14:38

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