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Damage Diagnosis Framework for Composite Structures Based on Multi-Dimensional Signal Feature Space and Neural

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Lamb waves effectively detect damage in composite structures like aerospace vehicles. A neural network model accurately assesses damage levels using multi-feature analysis of wave signals.

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Area of Science:

  • Structural Health Monitoring
  • Composite Materials Science
  • Non-Destructive Testing

Background:

  • Composite materials are crucial for aerospace and wind turbines, but susceptible to damage accumulation.
  • Sudden structural failures can occur due to undetected damage.
  • Lamb waves offer a promising method for early damage detection due to their sensitivity and propagation characteristics.

Purpose of the Study:

  • To develop an efficient damage assessment model for composite structures using Lamb wave analysis.
  • To extract sensitive multi-feature parameters from Lamb wave signals for damage evaluation.
  • To validate the model's accuracy in assessing various levels of structural damage.

Main Methods:

  • Utilized multi-scale wavelet transform to extract time-frequency domain features from Lamb wave signals.
  • Employed a neural network with nonlinear mapping capabilities to build a damage assessment model.
  • Conducted experiments on an epoxy-glass-fiber-reinforced plate to test the methodology.

Main Results:

  • Extracted multi-feature parameters from Lamb waves demonstrated sensitivity to accumulated damage.
  • The developed neural network model accurately evaluated the degree of damage in the composite plate.
  • The approach showed satisfactory accuracy in assessing typical structural damage at different levels.

Conclusions:

  • Lamb wave analysis combined with neural networks provides an effective method for structural health monitoring of composite materials.
  • The time-frequency domain features are valuable indicators of damage in composite structures.
  • This methodology holds potential for ensuring the safety and integrity of critical engineering applications.