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Rapid Identification of Material Defects Based on Pulsed Multifrequency Eddy Current Testing and the k-Nearest

Jacek M Grochowalski1, Tomasz Chady1

  • 1Faculty of Electrical Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, Poland.

Materials (Basel, Switzerland)
|October 28, 2023
PubMed
Summary

This study uses Pulsed Multifrequency Excitation and Spectrogram Eddy Current Testing (PMFES-ECT) with k-Nearest Neighbors (k-NN) to accurately estimate defect parameters in conductive materials.

Keywords:
eddy currentsfinite element analysisk-Nearest Neighbors (k-NN) algorithmmultifrequency excitation and spectrogram eddy current testingnondestructive testingnumerical simulations

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Area of Science:

  • Materials Science
  • Non-Destructive Testing
  • Machine Learning

Background:

  • Eddy Current Testing (ECT) is a common non-destructive method.
  • Accurate defect parameter estimation is crucial for material integrity.
  • Supervised learning offers potential for enhancing ECT analysis.

Purpose of the Study:

  • To develop and evaluate a method for estimating defect parameters in conductive materials.
  • To integrate Pulsed Multifrequency Excitation and Spectrogram Eddy Current Testing (PMFES-ECT) with machine learning.
  • To assess the classification accuracy of the proposed approach.

Main Methods:

  • A three-dimensional finite element method (FEM) model was created for sensor-specimen simulation.
  • Simulation outcomes were used as training data for the k-Nearest Neighbors (k-NN) algorithm.
  • The k-NN algorithm was applied to measurement data for defect parameter estimation.

Main Results:

  • The study successfully employed PMFES-ECT and k-NN for defect parameter estimation.
  • FEM simulations provided effective training data for the machine learning model.
  • Classification accuracy was evaluated for various predictor combinations.

Conclusions:

  • The combined PMFES-ECT and k-NN approach is effective for defect characterization.
  • This method offers a promising avenue for automated defect analysis in conductive materials.
  • Further research can explore different machine learning algorithms and feature sets.