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Two-Signal Set and Adaptive Spectral Decomposition Algorithm for Estimating the Phase Velocity of Dispersive Lamb

Lina Draudvilienė¹, Asta Meškuotienė², Aušra Gadeikytė³

¹Ultrasound Research Institute, Kaunas University of Technology, K. Baršausko St. 59, LT-51423 Kaunas, Lithuania.

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Summary

This study presents an automated tool for evaluating the A0 mode

Keywords:

frequency phase velocity dispersion spectrum decomposition technique ultrasonic Lamb waves zero-crossing technique

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Area of Science:

Ultrasonic Nondestructive Testing
Materials Science
Signal Processing

Background:

Accurate phase velocity measurement of the A0 mode is crucial for ultrasonic nondestructive testing (NDT).
Traditional methods for dispersion curve analysis often require extensive spatial scanning and complex data processing.
The A0 mode's high dispersion presents challenges for precise velocity evaluation.

Purpose of the Study:

To develop an automated computational tool for efficient phase velocity evaluation of the A0 mode.
To reduce the number of required signals for accurate dispersion curve analysis.
To enhance the accuracy and frequency range of measurable dispersion curves.

Main Methods:

An algorithm combining the zero-crossing technique with automated spectral decomposition was developed.
A bank of bandpass filters with adaptive bandwidths was utilized.
The method was validated using theoretical and experimental analysis on an aluminum plate.

Main Results:

The automated tool accurately evaluated the phase velocity of the A0 mode using only two signals.
The measurable frequency range of the dispersion curve was extended by over 65% of the signal's spectral width.
High accuracy was achieved, with an average relative error of 0.8% ± 1.2% and a best-case error of 0.3% ± 0.4%.

Conclusions:

The developed automated tool offers a time-efficient and labor-reducing alternative to traditional spatial scanning methods.
Utilizing a two-signal set and adaptive filter widths significantly improves accuracy and extends the measurable frequency range.
This approach simplifies data collection for ultrasonic dispersion curve analysis.