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Genetic algorithm-enabled quantitative characterization of planar defect using local resonance frequency and

Shuang Xu1, Kai Wang2, Honglin Yan1

  • 1School of Aerospace Engineering, Xiamen University, Xiamen 361005, PR China.

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Summary
This summary is machine-generated.

This study introduces a new method using local defect resonance (LDR) and a genetic algorithm to accurately measure circular defect size and depth in structures. This advances nondestructive evaluation (NDE) for complex designs.

Keywords:
Attenuation rateGenetic algorithmLocal defect resonanceQuantitative evaluation

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Ultrasonics

Background:

  • Advanced manufacturing enables complex structures, increasing the need for effective nondestructive evaluation (NDE).
  • Local defect resonance (LDR) shows promise for NDE in complex thin-walled structures.
  • Existing LDR methods struggle with accurate quantitative defect evaluation due to boundary determination challenges.

Purpose of the Study:

  • To develop a novel LDR-based method for precise quantitative evaluation of circular defects.
  • To simultaneously determine defect diameter and thickness using LDR frequency and attenuation.
  • To enhance the accuracy and efficiency of NDE for complex structures.

Main Methods:

  • Utilizing guided ultrasonic waves and normal mode expansion to analyze reflections at defect boundaries.
  • Establishing relationships between defect parameters (diameter, thickness) and LDR attributes (frequency, attenuation rate).
  • Employing a genetic algorithm for inverse determination of defect parameters from LDR attributes.

Main Results:

  • The proposed method accurately quantifies both diameter and thickness of circular defects.
  • Numerical and experimental validations confirm the method's effectiveness on flat-bottomed holes in plate structures.
  • Significant enhancement in accuracy and efficiency for quantitative defect evaluation was achieved.

Conclusions:

  • The developed LDR-based genetic algorithm method offers improved quantitative defect assessment.
  • This research advances LDR applications in NDE for complex structures.
  • The findings provide a foundation for developing LDR-based structural health monitoring techniques.