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Reconstruction of Composite Stiffness Matrix with Array-Guided Wave-Based Genetic Algorithm.

Menglong Liu1, Yaohui Zhang1, Lun Li1

  • 1School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China.

Materials (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a guided wave-based genetic algorithm (GA) to non-destructively measure the stiffness matrix of carbon fiber reinforced polymers (CFRPs). This method accurately reconstructs material parameters, enabling in situ composite performance evaluation.

Keywords:
carbon-fiber-reinforced polymergenetic algorithmguided wavestiffness matrix

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Non-destructive Testing

Background:

  • Accurate material parameter measurement is crucial for composite mechanical performance evaluation.
  • Non-destructive methods are highly desirable for in situ assessment of composites.
  • Carbon Fiber Reinforced Polymers (CFRPs) are advanced materials requiring precise characterization.

Purpose of the Study:

  • To develop and validate a novel method for reconstructing the stiffness matrix of CFRPs using guided waves and a genetic algorithm.
  • To compare the accuracy of the proposed method with traditional tensile testing.
  • To enable in situ and non-destructive measurement of composite material properties.

Main Methods:

  • Utilizing a genetic algorithm (GA) for stiffness matrix reconstruction based on array-guided wave (GW) data.
  • Comparing numerically calculated GW dispersion curves with experimental data obtained via 2D-FFT.
  • Employing a matching coefficient as the objective function for the GA, bypassing mode sorting.

Main Results:

  • The GW-based GA achieved an average deviation of 11.62% for four independent stiffness parameters across different CFRP stacking sequences.
  • An average deviation of 11.03% was observed for parameters in unidirectional CFRPs when compared using different measurement methods.
  • Further corrections for fiber orientation and GW frequency range reduced deviations to 2.72% and 3.9% respectively.

Conclusions:

  • The proposed guided wave-based genetic algorithm provides an effective approach for non-destructive reconstruction of composite stiffness matrices.
  • This method offers a viable pathway for in situ monitoring and evaluation of CFRP material properties.
  • The technique demonstrates high accuracy and potential for practical application in composite manufacturing and maintenance.