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Related Experiment Video

Updated: Feb 6, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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Simultaneous ultrasonic parameter estimation of a multi-layered material by the PSO-based least squares algorithm

Xiaoyu Yang1, Chengcheng Zhang2, Chuanyong Wang1

  • 1The State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, People's Republic of China.

Ultrasonics
|August 22, 2018
PubMed
Summary

This study introduces a particle swarm optimization (PSO)-based least squares method for non-destructively characterizing multi-layered materials using ultrasonic reflection spectra. The enhanced algorithm improves speed and accuracy for simultaneous property estimation in advanced materials.

Keywords:
Least squares inversionMulti-layered materialParticle swarm optimizationReflection spectrumSimultaneous measurementUltrasonic parameters

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Area of Science:

  • Materials Science
  • Non-destructive Testing
  • Acoustics

Background:

  • Advanced multi-layered materials require precise characterization for optimal performance.
  • Current non-destructive methods for multi-layer properties often face challenges with speed, simplicity, and local optima.
  • The least squares inversion method, while effective for single layers, struggles with the complexity of multi-layered structures.

Purpose of the Study:

  • To develop an improved non-destructive method for simultaneous characterization of multi-layered material properties.
  • To enhance the least squares inversion technique for better accuracy and efficiency in complex material structures.
  • To address the limitations of traditional methods in estimating parameters for multi-layered systems.

Main Methods:

  • Utilized particle swarm optimization (PSO), a global search algorithm, to enhance the least squares inversion method.
  • Employed ultrasonic reflection spectrum analysis for non-destructive property estimation.
  • Applied the proposed PSO-based least squares estimation to an aluminum-TC4 bi-layered material.

Main Results:

  • The PSO-based least squares method demonstrated capability in simultaneous measurement of bi-layered material properties.
  • Both simulation and experimental results validated the effectiveness of the new algorithm.
  • Comparison between real measured and estimated parameters provided insights into the accuracy and error analysis.

Conclusions:

  • The proposed PSO-based least squares estimation offers a robust and efficient approach for non-destructive characterization of multi-layered materials.
  • This method overcomes the limitations of traditional least squares inversion, particularly for complex layered structures.
  • The study successfully validated the algorithm's performance on a practical bi-layered material, paving the way for advanced material analysis.