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Autonomous characterization of grain size distribution using nonlinear Lamb waves based on deep learning.

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This study introduces a novel deep learning method for nondestructive characterization of metallic material microstructures using nonlinear ultrasonics. The technique accurately determines grain size distribution, enabling real-time microstructural evaluation.

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Area of Science:

  • Materials Science
  • Nonlinear Acoustics
  • Deep Learning

Background:

  • Microstructure characterization is vital for metallic materials.
  • Conventional electron microscopy is destructive and time-consuming.
  • Nonlinear ultrasonic techniques offer nondestructive potential but face challenges in multiparameter estimation due to complex mechanisms.

Purpose of the Study:

  • To develop an explainable deep learning model for analyzing nonlinear ultrasonic responses.
  • To establish a robust method for nondestructive characterization of metallic material microstructures.
  • To accurately determine grain size distribution from acoustic nonlinearity.

Main Methods:

  • Proposed an explainable nonlinearity-aware multilevel wavelet decomposition-multichannel one-dimensional convolutional neural network (CNN).
  • Hierarchically extracted time-frequency features from acoustic nonlinearity.
  • Modeled latent nonlinear dynamics directly from nonlinear ultrasonic responses.

Main Results:

  • Successfully mapped acoustic nonlinearity to microstructural features.
  • Determined the lognormal distribution of grain size in metallic materials, not just the average.
  • Demonstrated physical explainability of the deep learning approach through component importance analysis.

Conclusions:

  • The developed deep learning approach enables accurate, nondestructive characterization of metallic material microstructures.
  • The technique allows for real-time in situ evaluation of microstructural evolution.
  • This method overcomes limitations of traditional techniques and addresses the ill-posed inverse problem in nonlinear ultrasonics.