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March 3, 2026MeasurementOpen Access

Towards diagnostics of damage state in self-healing composites using an AI-driven acousto-ultrasonic approach

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Authors

CBClaudia BarileVKVimalathithan Paramsamy Kannan

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Overview

Observational analysis identifies damage states in self-healing composites, suggesting enhanced non-destructive evaluation techniques.

Key Points

  • The convolutional neural network classifies damage states with exceptional accuracy of 98.66%.
  • Mel frequency cepstral coefficients transform stress waves into usable features for analysis.
  • AI-driven diagnostics harnessing a lightweight CNN reveals profound implications for self-healing composite evaluation.
  • The method supports large-scale applications in non-destructive testing of composite materials.

Cite This Study

Barile et al. (2026) studied this question.

synapsesocial.com/papers/69a75f58c6e9836116a2aa8ehttps://doi.org/10.1016/j.measurement.2026.120652
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