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ORIGINAL RESEARCH article

Front. Phys.
Sec. Interdisciplinary Physics
Volume 12 - 2024 | doi: 10.3389/fphy.2024.1456236

Damage detection of composite laminates based on deep learnings

Provisionally accepted
JianHua Jiang JianHua Jiang 1*Zhengshui Wang Zhengshui Wang 2
  • 1 Nanchang Normal College of Applied Technology, Nanchang, China
  • 2 Nanchang Hangkong University, Nanchang, Jiangxi Province, China

The final, formatted version of the article will be published soon.

    Composite structure is widely used in various technological fields because of its superior material properties. Composite structure detection technology has been exploring efficient and fast damage detection technology. In this paper, image-based NDT technology is proposed to detect composite damage using deep learning. A data set was established through literature, which contained images of damaged and non-damaged composite material structures. Then, five convolutional neural network models Alexnet, VGG16, ResNet-34, ResNet-50 and GoogleNet were used to automatically classify the damage. Finally, the performance of five pre-trained network architectures is evaluated, and the results show that RESNET-50 technology can successfully detect damage in a reasonable computation time with the highest accuracy and low complexity using relatively small image datasets.

    Keywords: Composite materials, damage detection, deep learning, Neural Network, Visual inspection

    Received: 28 Jun 2024; Accepted: 09 Sep 2024.

    Copyright: © 2024 Jiang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: JianHua Jiang, Nanchang Normal College of Applied Technology, Nanchang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.