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This study uses the InceptionTime deep learning model with acoustic emission (AE) data for effective damage classification in composite materials. The approach achieved 99% accuracy in identifying fiber breakage, matrix cracking, and delamination.

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Artificial Intelligence

Background:

  • Non-destructive testing (NDT) is crucial for assessing damage in carbon fiber-reinforced composites.
  • Acoustic emission (AE) is a sensitive NDT technique for monitoring material integrity.
  • Classifying diverse damage types like fiber breakage, matrix cracking, and delamination requires advanced analytical methods.

Purpose of the Study:

  • To apply a deep learning approach for classifying damage in composite materials using acoustic emission data.
  • To evaluate the performance of the InceptionTime model for time series classification of AE signals.
  • To investigate the effectiveness of raw AE time series versus frequency-domain data as input for the model.

Main Methods:

  • Utilized acoustic emission (AE) technique to collect data from tensile damage tests on composites.
  • Implemented the InceptionTime deep learning model for time series classification of AE data.
  • Compared classification performance using raw AE time series data and frequency-domain sequence data.

Main Results:

  • Achieved high classification accuracy, approximately 99%, for composite damage detection.
  • Demonstrated superior training, validation, and test accuracy with raw AE time series data compared to frequency-domain data.
  • The InceptionTime model showed effectiveness in handling imbalanced datasets.

Conclusions:

  • The InceptionTime deep learning model is highly effective for acoustic emission-based damage classification in composite materials.
  • Raw AE time series data provides better performance for the InceptionTime network than frequency-domain data.
  • The InceptionTime model shows promise for real-world applications with imbalanced AE data.