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Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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In-situ sparse-array imaging for through-thickness cracks in plates with A0 Lamb waves and an efficient waveform

Shengyuan Zhang1, Jinglei Yang2, Wenjing Ye2

  • 1Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore.

Ultrasonics
|January 20, 2024
PubMed
Summary

This study introduces an efficient model-based crack imaging method for structural health monitoring (SHM). The technique uses A0 mode Lamb waves for accurate, low-cost online crack detection and characterization.

Keywords:
Crack characterizationFull-waveform inversionGenetic algorithmHuygens’ principleLamb wavesRay tracingStructural health monitoring

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Non-destructive Testing

Background:

  • Structural failures often result from fatigue cracks, necessitating efficient online crack detection and characterization for structural health monitoring (SHM).
  • Current methods for crack characterization using sensed wave signals can be complex, requiring extensive sensor networks or detailed wave packet analysis.
  • Model-based inversion offers a promising alternative by utilizing full waveform data for more streamlined and accurate characterization.

Purpose of the Study:

  • To develop an efficient, model-based crack imaging method for online structural health monitoring.
  • To address the limitations of existing methods that require onerous steps or numerous sensors.
  • To enable accurate crack characterization with fewer sensors and simpler implementation.

Main Methods:

  • Proposed an efficient model leveraging Huygens' principle and the no-mode-conversion property of A0 mode Lamb waves.
  • Verified the model-based crack imaging method through both numerical simulations and experimental tests.
  • Applied the method to analyze both smooth and rough crack types.

Main Results:

  • The proposed model-based inversion method effectively detects and characterizes cracks.
  • The approach demonstrated accuracy with fewer sensors and simpler implementation compared to traditional methods.
  • Successful verification was achieved on both simulated and experimentally generated smooth and rough cracks.

Conclusions:

  • The developed model-based crack imaging method is efficient, easy to implement, and cost-effective for SHM.
  • This technique offers a desirable solution for online monitoring of structural integrity.
  • The method's reliance on full waveform analysis and specific Lamb wave properties enhances its applicability.