Visible fingerprint of X-ray images of epoxy resins using singular value decomposition of deep learning features

Edgar Avalos, Kazuto Akagi, Yasumasa Nishiura

研究成果: Article査読

抄録

Although the process variables of epoxy resins alter their mechanical properties, recently it was found that the total variation of the X-ray images of these resins is one of the key features that affect the toughness of these materials. However it is still not clear how to visualize such a difference in a clear way. To facilitate the visualization, we use a robust approximation of the gradient of the intensity field of the X-ray images of different kinds of epoxy resins and then we use deep learning to discover the most representative features of the transformed images. In this solution of the inverse problem to find characteristic features to discriminate samples of heterogeneous materials, we use the eigenvectors obtained from the singular value decomposition of all the channels of the response maps of the early layers in a convolutional neural network. While the strongest activated channel gives a visual representation of the characteristic features, often these are not robust enough in some practical settings. On the other hand, the left singular vectors of the matrix decomposition of the response maps barely change when variables such as the capacity of the network or the network architecture change. High classification accuracy and robustness of characteristic features are presented in this work.

本文言語English
論文番号109996
ジャーナルComputational Materials Science
186
DOI
出版ステータスPublished - 2021 1

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 化学 (全般)
  • 材料科学(全般)
  • 材料力学
  • 物理学および天文学(全般)
  • 計算数学

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