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Error analysis and reliability of zero-order Lamb mode inversion for waveguide characterization.

A Sabbadini1, J Massaad1, P L M J van Neer2

  • 1Applied Sciences, Delft University of Technology, Lorentzweg 1, Delft, 2628 CJ, The Netherlands.

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Summary
This summary is machine-generated.

This study introduces a novel method to quantify the precision of reconstructing plate parameters from Lamb wave dispersion curves. The technique analyzes error distributions to determine how accurately parameters can be extracted.

Keywords:
Lamb waveMulti-parameter inversionNon-destructive testingSensitivity

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Area of Science:

  • Materials Science
  • Acoustics
  • Non-Destructive Testing

Background:

  • Lamb wave dispersion curves are used to reconstruct plate parameters.
  • Existing fitting techniques offer high accuracy but lack quantitative precision analysis.
  • Determining the precision of parameter reconstruction is crucial for applications.

Purpose of the Study:

  • To introduce a method for quantitatively analyzing the precision of parameter extraction from Lamb wave dispersion curves.
  • To provide a framework for understanding the reliability of reconstructed parameters.

Main Methods:

  • Comparing target dispersion curves to a database of theoretical curves across a parameter space.
  • Calculating a dissimilarity (error) measure to map the error distribution.
  • Introducing dimensionless quantities to describe error distribution and parameter spread.

Main Results:

  • The method provides quantitative information on parameter reconstruction precision.
  • Error distribution maps reveal the reliability of parameter extraction.
  • Parameter reconstruction precision is dependent on the Lamb wave mode and frequency range.

Conclusions:

  • The developed method offers a robust way to assess the precision of Lamb wave-based parameter reconstruction.
  • Understanding precision is vital for the successful application of these techniques.
  • Future work can leverage this method to optimize experimental design and data analysis.