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Ellipse of uncertainty based algorithm for quantitative evaluation of defect localization using Lamb waves.

Honglei Chen1, Kailiang Xu2, Zenghua Liu3

  • 1Academy for Engineering & Technology, Fudan University, Shanghai 200433, China.

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|July 14, 2022
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Summary

Measurement deviations in ultrasonic testing impact defect localization accuracy. This study introduces an ellipse of uncertainty (EOU)-based algorithm to improve quantitative defect evaluation using Lamb waves and sparse arrays.

Keywords:
Defect localizationEllipse of uncertaintyNondestructive testingQuantitative evaluationUltrasonic Lamb waves

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Area of Science:

  • Nondestructive Testing
  • Ultrasonic Lamb Wave Propagation
  • Signal Processing

Background:

  • Measurement deviations in time of flight (ToF) are inherent in sparse array ultrasonic Lamb wave testing.
  • These deviations limit the accuracy of temporal-spatial mapping trajectories (TSMTs) and quantitative defect localization.
  • Existing methods struggle with precise defect characterization due to signal parameter uncertainties.

Purpose of the Study:

  • To develop a novel algorithm for quantitative defect localization in ultrasonic testing.
  • To address the limitations imposed by time of flight measurement deviations.
  • To enhance the accuracy of defect identification using sparse array Lamb wave analysis.

Main Methods:

  • Derivation of the ellipse of uncertainty (EOU) for TSMTs using group velocity, ToFs, measurement deviations, and actuator-receiver distances.
  • Development of an EOU-based algorithm for defect localization by identifying scatterer intersections.
  • Introduction of a fuzzy scaling factor based on ellipse eccentricity and a fuzzy control parameter to tune TSMT influence zones.
  • Fusion of scattering wave ToFs using the acoustic reciprocity theorem and fuzzy control for scatterer-inspection pair correlation.

Main Results:

  • The EOU-based algorithm effectively reduces interference from uncertainty ellipses in detection.
  • Quantitative evaluation of defect localization was achieved by analyzing scatterer distribution and ToF differences.
  • The method establishes a one-to-one relationship between individual scatterers and inspection pairs.
  • Experimental validation demonstrated improved accuracy in defect localization compared to traditional methods.

Conclusions:

  • The EOU-based algorithm provides a robust framework for quantitative defect localization in ultrasonic Lamb wave testing.
  • This approach mitigates the impact of ToF measurement deviations, enhancing reliability.
  • The developed method offers a significant advancement in precise defect characterization for structural health monitoring.