Thermal barrier coating debonding defects detection based on infrared thermal wave testing technology under linear frequency modulation heat exitation

Liu, Yuanlin; Zhao, Guojin; Du, Yongzhi; Zhang, Yang

doi:10.2298/tsci180901230l

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Therefore, vital modules such as probe positioning system and processing unit used for other techniques, such as ultrasonic testing, can also be utilized for this technique with minimal modifications. Figure (12) shows the experimental setup, where an automatic mechanical positioner was used to move the probe along the sample under test with pre-defined steps, while the VNA recorded the frequency domain reflection coefficient at each step. The sample in Figure ( 10) was tested first.…”

Section: Resultsmentioning

confidence: 99%

IFFT-Based Microwave Non-Destructive Testing for Delamination Detection and Thickness Estimation

Jawad

Akbar

2021

IEEE Access

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Microwave Non-Destructive Testing (NDT) techniques for dielectric coatings are vital processes in many industrial applications due to their superior defect detection capabilities over traditional NDT methods. However, the limited bandwidth and post-processing complexities of these techniques cause them to fall short of accurately estimating the delamination thickness in such structures. In this paper, a novel low-complexity signal processing method is proposed to estimate the delamination thickness in metalbacked dielectric coatings using bandwidth-limited Open-Ended Rectangular Waveguide (OERW) probes. Here, Inverse Fast Fourier Transform (IFFT) process is used to convert the measured complex reflection coefficients at the surface of the coating to the time domain. Next, the amplitude of a specific time step is used to accurately estimate the delamination thickness without extra time-consuming processing. Using a 3 mm-thick macor samples with machined defects, the proposed technique is validated by correctly estimating delamination thickness down to 0.4 mm using an OERW probe with 13.5 GHz bandwidth. Hence, this technique overcomes the challenge of the OERW's limited bandwidth, which highly affects the possibility of tracking shifting in the peak reflection in the time domain. Moreover, the proposed method does not add to the complexity of the NDT process, which makes it suitable for in-situ real time applications. Hence, following this approach would be of great importance to numerous industries, where accurate thickness estimation of minute delamination in coatings is essential to avoid system failure.

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Section: Resultsmentioning

confidence: 99%

IFFT-Based Microwave Non-Destructive Testing for Delamination Detection and Thickness Estimation

Jawad

Akbar

2021

IEEE Access

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“…Signal-to-noise ratio (SNR) is used as a quantitative measure of the quality of the thermographic processed image 13 . This value can be used to compare heating sources and post processing algorithms used to detect delaminations 14,15 . SNR is defined as (5).…”

Section: Signal-to-noise Ratiomentioning

confidence: 99%

Detection of delaminated areas in thermal barrier coatings by active infrared thermography using different heat sources

Revuelta¹,

Román²,

Arboleda³

et al. 2023

Optical Measurement Systems for Industrial Inspection XIII

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Active infrared thermography (IRT) has been used to detect delamination on yttria-stabilized zirconia thermal barrier coatings (YSZ-TBC) using various heat sources. Thermal shock tests were performed to induce delamination on 1x1 inch flat samples. Each thermal cycle consisted of heating the samples in a muffle furnace, holding at 1020°C for 5 minutes, and quenching on water at approximately 20°C. Two samples were used, the first was brought to failure (184 cycles) and the second was almost intact to the naked eye (100 cycles). In addition to these samples, a discarded gas turbine blade was cut, and a delaminated fragment was used for comparison.IRT was performed using two 600 W flash lamps, one 500 W halogen lamp, and a long-wave infrared camera with a frame rate of 30 Hz. The acquired signal was first processed by the Thermal Signal Reconstruction (TSR) method using a 4th order polynomial interpolation and its first and second time derivatives. The thermographic data were processed using the Principal Component Analysis (PCA) algorithm. PCA was applied to both raw and pre-processed data.The results were compared using signal-to-noise ratio (SNR) to measure the quality of the algorithms used to detect delaminated areas. The comparisons showed that PCA results are improved when applied to the TSR sequences. However, the selection of the principal component with the highest SNR depends on the heating condition (flash or long pulse) and on the samples analyzed.

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“…These methods require sample preparation through grinding and polishing, which not only damages the samples but also introduces measurement errors. Hence, the application of non-destructive testing (NDT) techniques, such as microwave [13,14], ultrasonic [15,16], X-ray [17,18], and thermal infrared technology [19,20] is undoubtedly a better choice for evaluating TBCs [21]. However, these methods have certain limitations that prevent precise testing in a stable environment.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Method for Porosity Measurement of Thermal Barrier Coatings Using Terahertz Time-Domain Spectroscopy

et al. 2023

Coatings

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Accurate measurement of porosity is crucial for comprehensive performance evaluation of thermal barrier coatings (TBCs) on aero-engine blades. In this study, a novel data-driven predictive method based on terahertz time-domain spectroscopy (THz-TDS) was proposed. By processing and extracting features from terahertz signals, multivariate parameters were composed to characterize the porosity. Principal component analysis, which enabled effective representation of the complex signal information, was introduced to downscale the dimensionality of the time-domain data. Additionally, the average power spectral density of the frequency spectrum and the extreme points of the first-order derivative of the phase spectrum were extracted. These extracted parameters collectively form a comprehensive set of multivariate parameters that accurately characterize porosity. Subsequently, the multivariate parameters were used as inputs to construct an extreme learning machine (ELM) model optimized by the sparrow search algorithm (SSA) for predicting porosity. Based on the experimental results, it was evident that the predictive accuracy of SSA-ELM was significantly higher than the basic ELM. Furthermore, the robustness of the model was evaluated through K-fold cross-validation and the final model regression coefficient was 0.92, which indicates excellent predictive performance of the data-driven model. By introducing the use of THz-TDS and employing advanced signal processing techniques, the data-driven model provided a novel and effective solution for the rapid and accurate detection of porosity in TBCs. The findings of this study offer valuable references for researchers and practitioners in the field of TBCs inspection, opening up new avenues for improving the overall assessment and performance evaluation of these coatings.

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Thermal barrier coating debonding defects detection based on infrared thermal wave testing technology under linear frequency modulation heat exitation

Cited by 4 publications

References 7 publications

IFFT-Based Microwave Non-Destructive Testing for Delamination Detection and Thickness Estimation

IFFT-Based Microwave Non-Destructive Testing for Delamination Detection and Thickness Estimation

Detection of delaminated areas in thermal barrier coatings by active infrared thermography using different heat sources

Data-Driven Method for Porosity Measurement of Thermal Barrier Coatings Using Terahertz Time-Domain Spectroscopy

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