Thermal Barrier Coating Of Blades Based On ECT Technology Thickness Measurement Method Study

Turbine blades are important components of aero-engines.They are prone to thermal corrosion under high temperature,high pressure,and high stress working environments,which endangers flight safety.It is difficult to fully protect the safety of aircraft by only using cooling technology and optimizing the turbine blade processing technology.The thermal barrier coating can significantly improve the high temperature resistance,oxidation resistance and wear resistance of turbine blades.Based on the theory of eddy current thickness measurement,an eddy current thickness simulation model of planar and two curved-shaped thermal barrier coating members is established.The coil sensor and excitation system parameters are optimized.An elastic pressure eddy current thickness probe is developed.The influence of the thickness of the barrier coating on the voltage amplitude is studied.The thermal barrier coating thickness inversion method based on multi-frequency and multi-parameter method and the BP neural network method is studied.The inversion result is compared with the measurement result of scanning electron microscope to realize thermal barrier coating,and the effective measurement of the thermal barrier coating thickness is realized.The effects of coil size and parameters of the excitation system on the sensitivity of the eddy current thickness measuring probe were analyzed by establishing a finite element simulation model of eddy current thickness measurement of plane and two curved surface thermal barrier coating members.The simulation results show that: For flat and curved members,the change in the induced voltage and the relative change in the influence of the coating are about the same.Combining with the results of simulation optimization and avoiding the effect of the eddy current thickness probe on the measurement results,a spring-loaded eddy current thickness probe is developed,and the signal response of the eddy current thickness probe under different coating thickness specifications is analyzed.The linear relationship between the thickness of the thermal barrier coatings is studied,the influence of the thickness of the thermal barrier coatings on the voltage amplitude is analyzed,by using the voltage amplitude resolution and the correlation coefficient of the voltage amplitude and the ceramic layer thickness fitting function,an eddy current measurement method for inverting the thickness of the thermal barrier coatings is researched,that is,Multi-frequency and multi-parameter method,analyzing eddy current measurement signals at different measurement frequencies and thermal barrier coating thicknesses,selecting two optimal excitation frequencies of150 k Hz and 250 k Hz to invert the thermal barrier coating thickness,comparing the inversion results with the scanning electron microscope measurement results.The maximum relative error of the layer inversion results is relatively large,and it is analyzed and researched.The relative error of the ceramic layer inversion results is less than 10%.Within the allowable range of the industrial measurement error,an effective measurement of the ceramic layer is achieved.The BP neural network algorithm program was written in Python language,and the thermal barrier coating thickness measurement software was designed in combination with HTML language.The thickness of the thermal barrier coating was measured by BP neural network inversion method,and the inversion results were compared with the scanning electron microscope.The observation results are compared.The relative errors of the adhesive layer and the ceramic layer are less than10% and 5%,respectively,which are within the tolerance range of industrial measurement errors.It is verified that the BP neural network method can measure the thickness of the thermal barrier coating with high accuracy.