Quantitative Determination of Porosity in Thermal Barrier Coatings Using Terahertz Reflectance Spectrum: Case Study of Atmospheric-Plasma-Sprayed YSZ Coatings

Ye, Dongdong; Wang, Weize; Zhou, Haiting; Li, Yuanjun; Fang, Huanjie; Huang, Jibo; Gong, Hanhong; Li, Zhen

doi:10.1109/tthz.2020.2995821

Cited by 20 publications

(11 citation statements)

References 47 publications

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“…Compared to the two semicircles of carbon fibers, the added semicircles can be found in the curves of all NiFs, due to the introduction of an interfacial polarization loss mechanism after the Ni surface has been applied to the carbon fibers, indicating that the NiFs have a similar relaxation process. 42 Figure 1h shows that the C0 peaks of NiF04 and NiF12 are consistent, which indicates the similar magnetic loss mechanism 43 and the consistency of loss mechanisms. Compared with CF, it can be found that the magnetic surface is beneficial to the broad effective absorption bandwidth (EAB) and low-frequency absorption (Figure 1i), and it is reasonable to believe that the introduction of the Ni surface contributes to the performance of MA.…”

Section: Designable Factor−objective Function Associationmentioning

confidence: 69%

“…And each Cole–Cole semicircle corresponds to a kind of Debye dipolar relaxation process, ,, as shown in Figure f. Compared to the two semicircles of carbon fibers, the added semicircles can be found in the curves of all NiFs, due to the introduction of an interfacial polarization loss mechanism after the Ni surface has been applied to the carbon fibers, indicating that the NiFs have a similar relaxation process Figure h shows that the C0 peaks of NiF04 and NiF12 are consistent, which indicates the similar magnetic loss mechanism and the consistency of loss mechanisms.…”

Section: Resultsmentioning

confidence: 99%

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Performance Optimization Engineering of Multicomponent Absorbing Materials Based on Machine Learning

Wang

Huang

Yan

et al. 2023

ACS Appl. Mater. Interfaces

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Multicomponent materials are microwave-absorbing (MA) materials composed of a variety of absorbents that are capable of reaching the property inaccessible for a single component. Discovering mostly valuable properties, however, often relies on semi-experience, as conventional multicomponent MA materials’ design rules alone often fail in high-dimensional design spaces. Therefore, we propose performance optimization engineering to accelerate the design of multicomponent MA materials with desired performance in a practically infinite design space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with the expanded Maxwell–Garnett model, electromagnetic calculations, and experimental feedback; aiming at different desired performances, Ni surface@carbon fiber (NiF) materials and NiF-based multicomponent (NMC) materials with target MA performance were screened and identified out of nearly infinite possible designs. The designed NiF and NMC fulfilled the requirements for the X- and Ku-bands at thicknesses of only 2.0 and 1.78 mm, respectively. In addition, the targets regarding S, C, and all bands (2.0–18.0 GHz) were also achieved as expected. This performance optimization engineering opens up a unique and effective way to design microwave-absorbing materials for practical application.

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Section: Designable Factor−objective Function Associationmentioning

confidence: 69%

Section: Resultsmentioning

confidence: 99%

Performance Optimization Engineering of Multicomponent Absorbing Materials Based on Machine Learning

Wang

Huang

Yan

et al. 2023

ACS Appl. Mater. Interfaces

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“…This is essentially a simple effective medium approach. Rigorous effective medium models have been previously applied to YSZ coatings in [ 27 ]. Since the proposed method only uses the real part of the refractive index, the modeled material parameters are real-valued.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, the modeled material parameters are not frequency-dependent. This is a reasonable simplification, since the real part of the refractive index of YSZ is fairly constant in the investigated frequency range [ 27 ]. The outer boundaries are set as high absorption scattering with two exceptions: the sender, which has a scattering boundary condition without absorption, and the TBC–BC interface, which is set to perfect electric conductor.…”

Section: Resultsmentioning

confidence: 99%

THz-TDS Reflection Measurement of Coating Thicknesses at Non-Perpendicular Incidence: Experiment and Simulation

Burger

Frisch

Hübner

et al. 2021

Sensors

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Time-domain spectroscopy (TDS) in the terahertz (THz) frequency range is gaining in importance in nondestructive testing of dielectric materials. One application is the layer thickness measurement of a coating layer. To determine the thickness from the measurement data, the refractive index of the coating layer must be known in the surveyed frequency range. For perpendicular incidence of the radiation, methods exist to extract the refractive index from the measurement data themselves without prior knowledge. This paper extends these methods for non-perpendicular incidence, where the polarization of the radiation becomes important. Furthermore, modifications considering effects of surface roughness of the coating are introduced. The new methods are verified using measurement data of a sample of Inconel steel coated with yttria-stabilized zirconia (YSZ) and with COMSOL simulations of the measurement setup. To validate the thickness measurements, scanning electron microscopy (SEM) images of the layer structure are used. The results show good agreement with an average error of 1% for the simulation data and under 4% for the experimental data compared to reference measurements.

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“…Numerous nondestructive methods have been proposed for quantitative determination of porosity in TBCs, such as thermography method [6][7][8][9][10], terahertz technique [11], X-ray [12], and effective medium theory [13]. 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].…”

Section: Introductionmentioning

confidence: 99%

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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Quantitative Determination of Porosity in Thermal Barrier Coatings Using Terahertz Reflectance Spectrum: Case Study of Atmospheric-Plasma-Sprayed YSZ Coatings

Cited by 20 publications

References 47 publications

Performance Optimization Engineering of Multicomponent Absorbing Materials Based on Machine Learning

Performance Optimization Engineering of Multicomponent Absorbing Materials Based on Machine Learning

THz-TDS Reflection Measurement of Coating Thicknesses at Non-Perpendicular Incidence: Experiment and Simulation

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

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