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Fast and quantitative noncontact laser ultrasound tapping detection of debonding in aerospace honeycomb sandwich panel based on autoencoder-softmax

  • Qiang Wu
  • , Weichen Xie
  • , Yi Xiong
  • , Shiyuan Zhou
  • , Menglong Liu*
  • , Zhongqing Su
  • *Corresponding author for this work
  • China Aerospace Science and Technology Corporation
  • Harbin Institute of Technology
  • Southern University of Science and Technology
  • Beijing Institute of Technology
  • Hong Kong Polytechnic University

Research output: Contribution to journalArticlepeer-review

Abstract

Aerospace grade honeycomb sandwich panels (HSPs) feature ultra-thin skins and honeycomb walls and thus are prone to debonding defects during manufacturing and service. A fast, non-destructive, and noncontact laser ultrasound tapping method combining the local fine C-scan imaging and the global sparse C-scan is proposed to detect the debonding in the ultrathin aerospace HSP. Firstly, by measuring the thermoelastic laser-induced vibration signals with fine C-scan at a small-scale region including both known intact and debonding defects, an automatic labelling algorithm is proposed to construct the dataset for training the Autoencoder (AE)-Softmax model. Then, based on the trained AE-Softmax model, the sparse C-scan only at the centroid of each honeycomb cell can quickly identify suspicious defects with low credibility in the HSP. Further, the suspicious cells in HSP are fine scanned to differentiate the intact or debonding status according to the area proportion of the connected component in the C-scan image. Finally, experiments are carried out in a second HSP to validate the proposed method, that all the four diversified defects, including multiple-debonding cells, one debonding wall, and adhesive removals, are successfully detected without false alarm, and the detection efficiency has been improved over 100 times compared with the conventional dense C-scan imaging.

Original languageEnglish
Pages (from-to)1318-1340
Number of pages23
JournalNondestructive Testing and Evaluation
Volume41
Issue number3
DOIs
Publication statusPublished - 2026
Externally publishedYes

Keywords

  • Laser ultrasound
  • debonding
  • defect detection
  • honeycomb sandwich panel
  • machine learning

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