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Cortical Bone Assessment Using Ultrasonic Guided Waves: A Reproducibility Study in a Healthy Population
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Guided waves-based damage identification in plates through an inverse Bayesian process.

W Wu1, M K Malik2, S Cantero-Chinchilla3

  • 1Institute for Aerospace Technology & The Composites Group, The University of Nottingham, NG7 2RD, United Kingdom.

Ultrasonics
|June 10, 2022
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Summary
This summary is machine-generated.

This study introduces a novel Bayesian framework for identifying plate damage using guided waves. The method accurately detects and characterizes structural defects, supporting advanced structural health monitoring.

Keywords:
Bayesian inferenceDamage identificationDigital twinsGuided wavesStructural health monitoring

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Area of Science:

  • Structural Health Monitoring
  • Non-Destructive Testing
  • Wave Mechanics

Background:

  • Guided waves are effective for inspecting large structures and detecting minor defects.
  • Analyzing scattered wave fields can characterize plate damage geometry.
  • Existing methods lack a comprehensive framework for guided wave-based damage identification.

Purpose of the Study:

  • To present a novel Bayesian framework for guided wave-based damage identification in infinite plates.
  • To develop an efficient inversion procedure for characterizing circular damage geometries.
  • To validate the proposed method using experimental and finite element analysis.

Main Methods:

  • A semi-analytical approach based on plate theories to model wave scattering.
  • Bayesian inference framework for damage characterization.
  • Ultrasound experiments on an aluminum plate with a circular indentation.
  • Finite element modeling for validation.

Main Results:

  • The proposed Bayesian framework accurately identifies plate damage.
  • Scattering features derived from a semi-analytical model efficiently characterize circular defects.
  • Experimental results validated the effectiveness and efficiency of the damage identification scheme.
  • Reconstructed scattering coefficients closely matched experimental data.

Conclusions:

  • The developed Bayesian framework provides a robust and efficient method for guided wave-based damage identification.
  • The approach is suitable for real-time structural health monitoring and digital twin applications.
  • This study advances the capability of detecting and characterizing structural defects using guided waves.