A comparison of methods for generating synthetic training data for domain adaption of deep learning models in ultrasonic non-destructive evaluation-Reference-Cited by-同舟云学术

A comparison of methods for generating synthetic training data for domain adaption of deep learning models in ultrasonic non-destructive evaluation

Published:2024-01 Issue: Volume:141 Page:102978
ISSN:0963-8695
Container-title:NDT & E International
language:en
Short-container-title:NDT & E International

Author:

McKnight Shaun^ORCID,Pierce S. Gareth,Mohseni Ehsan,MacKinnon Christopher,MacLeod Charles,O'Hare Tom,Loukas Charalampos

Funder

Royal Academy of Engineering

Publisher

Elsevier BV

Subject

Mechanical Engineering,Condensed Matter Physics,General Materials Science

Reference39 articles.

1. Nondestructive evaluation of carbon fibre reinforced composites with infrared thermography and ultrasonics;Meola;Compos Struct,2015

2. A. M.-E. Dorado, ‘Composite material characterization using acoustic wave speed measurements’, p. 5..

3. Ley, O. and V. Godinez, ‘Non-destructive evaluation (NDE) of aerospace composites: application of infrared (IR) thermography’, doi: 10.1533/9780857093554.3.309..

4. Ultrasonic detection of manufacturing defects in multilayer composite structures;Kokurov;IOP Conf Ser Mater Sci Eng,2021

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