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Updated: Aug 12, 2025

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Simultaneous inversion method of thermal barrier coating parameters based on electromagnetic/capacitive dual-module

Wei Wang1, Yuan Ren1, Jiafei Hu2

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.

The Review of Scientific Instruments
|February 1, 2023
PubMed
Summary

This study introduces a neural network method to monitor thermal barrier coatings (TBCs) in aeroengines. The technique accurately determines key TBC parameters, enhancing aircraft safety.

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Area of Science:

  • Materials Science
  • Aerospace Engineering
  • Non-destructive Testing

Background:

  • Turbine blades require thermal barrier coatings (TBCs) to withstand high temperatures and prevent aeroengine damage.
  • TBCs have a complex multilayer structure with dielectric ceramic and conductive bonding layers, where failure of either layer compromises aircraft safety.
  • Changes in TBC parameters are critical indicators of potential failure.

Purpose of the Study:

  • To develop a novel neural-network-based method for the simultaneous inverse analysis of three key TBC parameters.
  • To utilize an electromagnetic/capacitive dual-module sensor for TBC monitoring.
  • To enable real-time status monitoring of aeroengines through TBC analysis.

Main Methods:

  • A neural network model was employed to perform inverse analysis of TBC parameters.
  • An electromagnetic/capacitive dual-module sensor was utilized for data acquisition.
  • The method focused on simultaneously determining the thickness and permittivity of the ceramic layer and the conductivity of the bonding layer.

Main Results:

  • The proposed method achieved an inversion error of less than 2% for the thickness and permittivity of the ceramic layer.
  • The conductivity of the bonding layer was also determined with an error of less than 2%.
  • Experimental validation confirmed the high accuracy and reliability of the developed technique.

Conclusions:

  • The neural-network-based method effectively and accurately determines critical TBC parameters.
  • The developed technique satisfies the stringent application requirements for aeroengine monitoring.
  • This approach offers a promising solution for enhancing the safety and reliability of aeroengines through advanced TBC diagnostics.