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

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.