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

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Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion
Title:
Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion
Other Titles:
Mechanics of Advanced Materials and Structures
Publication Date:
07 October 2021
Citation:
Rautela, M., Huber, A., Senthilnath, J., & 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, 1–17. doi:10.1080/15376494.2021.1982090
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).
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Accelerated Materials Development for Manufacturing Program - AME Programmatic Fund
Grant Reference no. : A1898b0043
Description:
This is an Accepted Manuscript of an article published by Taylor & Francis in Mechanics of Advanced Materials and Structures on 7 Oct 2021, available online: http://www.tandfonline.com/10.1080/15376494.2021.1982090
ISSN:
1537-6494
1537-6532
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