&lt;title&gt;Application of NDE in aerospace coatings&lt;/title&gt;

Fahr, A.; Giguère, Sylvain; Rogé, B.; McRae, K. I.

doi:10.1117/12.469867

Cited by 1 publication

(2 citation statements)

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“…ermal barrier coatings (TBCs) are widely used in the aerospace industry to protect critical components from wear, erosion, corrosion, or thermal oxidation [1]. e thermal insulation performance of TBCs as well as their strain compliance required to accommodate the thermal expansion coefficient mismatch between metal and ceramic upper layers strongly depends on its porosity.…”

Section: Introductionmentioning

confidence: 99%

“…Among all existing methods, the ultrasonic technique is a relatively inexpensive, fast, and reliable nondestructive method, which has been widely used to detect porosity in thermally sprayed coatings [3,[14][15][16][17]. A representative method is to use ultrasonic parameters in thermally sprayed coatings, usually longitudinal wave velocity and ultrasonic attenuation, to evaluate its properties, including porosity [15,[17][18][19][20]. For example, a trend line or corresponding expression between the velocity/attenuation and porosity for the coatings investigated is used to estimate the porosity content of other parts with the same coatings by measuring their thickness and velocity [17,19].…”

Section: Introductionmentioning

confidence: 99%

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Porosity Characterization of Thermal Barrier Coatings by Ultrasound with Genetic Algorithm Backpropagation Neural Network

Zhang

Guo

et al. 2021

Complexity

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Porosity is considered as one of the most important indicators for the characterization of the comprehensive performance of thermal barrier coatings (TBCs). In this study, the ultrasonic technique and the artificial neural network optimized with the genetic algorithm (GA_BPNN) are combined to develop an intelligent method for automatic detection and accurate prediction of TBCs’s porosity. A series of physical models of plasma-sprayed ZrO2 coating are established with a thickness of 288 μm and porosity varying from 5.71% to 26.59%, and the ultrasonic reflection coefficient amplitude spectrum (URCAS) is constructed based on the time-domain numerical simulation signal. The characteristic features f 1 , f 2 , A max , Δ A of the URCAS, which are highly dependent on porosity, are extracted as input data to train the GA_BPNN model for predicting the unknown porosity. The average error of the prediction results is 1.45%, which suggests that the proposed method can achieve accurate detection and quantitative characterization of the porosity of TBCs with complex pore morphology.

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Section: Introductionmentioning

confidence: 99%