Abstract
The existing methods for thermal barrier coating (TBC) life prediction rely mainly on experience and formula derivation and are inefficient and inaccurate. By introducing deep learning into TBC life analyses, a convolutional neural network (CNN) is used to extract the TBC interface morphology and analyze its life information, which can achieve a high-efficiency accurate judgment of the TBC life. In this thesis, an Adap–Alex algorithm is proposed to overcome the problems related to the large training time, over-fitting, and low accuracy in the existing CNN training of TBC images with complex tissue morphologies. The method adjusts the receptive field size, stride length, and other parameter settings and combines training epochs with a sigmoid function to realize adaptive pooling. TBC data are obtained by thermal vibration experiments, a TBC dataset is constructed, and then the Adap–Alex algorithm is used to analyze the generated TBC dataset. The average training time of the Adap–Alex method is significantly smaller than those of VGG-Net and Alex-Net by 125 and 685 s, respectively. For a fixed number of thermal vibrations, the test accuracy of the Adap–Alex algorithm is higher than those of Alex-Net and VGG-Net, which facilitates the TBC identification. When the number of thermal vibrations is 300, the accuracy reaches 93%, and the performance is highest.
Highlights
Aero engine thermal barrier coating (TBC) is a key thermal protection structure coated on engine turbine blades to ensure reliable operation in high-temperature environments
In the algorithm model classification, according to the TGO thickness after the different number of thermal vibration experiments of the sample, the classification results are divided into three categories: safe, critical, and Coatings 2021, 11, x FOR PEER REVdIEaWmaged; and identify the thermal vibration times of the thermal barrier coating acco1r0dionfg14 to the TGO thickness interval
When the number of thermal vibrations is 300 times, al3m.1o.sItmaalgl eTAGnOalsyasmispRlesualrtes greater than 8μm, and the thermal barrier coating is in a failure state [2T8h–e33sa].mTphleesstwateisrteicdailvtiadbeledoinf tToGsOixtghricokunpesssacocfosradminpgletsowthitehftrheequinecnrceyasoefotfhtehremrmalavlivibbrraattioionnfrtiommes0 atond60i,ts12d0i,s1tr8i0b,u2t4io0n, ainndte3rv00a.l IanrethsehoalwgonriinthTmabmleod5.elFcilgaussriefi1c0atsiohno,wascctohrediminaggetos othf ethTeGcOoattihnigckinneSsescaufrtietry,thCeridtiicfafel raenndt Dnuammbaegre ostfattheesrrmesaplevcitbivraetliyo. n experiments of the sample, the classification results are divided into three categories: safe, critical, and Tadbalme 5a.gSetda;tiastnicdalidtaebnletifoyf ttehset tthhiecrkmneaslsvoifbsroamtioensatmimpelessooff tShEeMthweritmh adlifbfearrerniet rticmoeastionfgthaecrcmoardl viibnrgattioonth(uenTitG: μOmt)h. ickness interval
Summary
Aero engine thermal barrier coating (TBC) is a key thermal protection structure coated on engine turbine blades to ensure reliable operation in high-temperature environments. TBC materials improve the high-temperature resistance and corrosion resistance of the blade material and largely reduce the generation of defects such as turbine blade cracks, holes, and surface damage, extend the service life of the blade, and can increase the thermal efficiency of the engine by more than 60% [5]. To further utilize the potential of the TBC, maintain the stable thermal insulation performance of the TBC, improve the high-temperature oxidation resistance of the blade, and improve the performance and safety of the engine, the key to ensuring the safe service of the aero engine blade is to study the failure mechanism of the TBC and effectively predict its service life. The research on TBC life prediction has become increasingly important
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