Characterization of thermal barrier coatings microstructural features using terahertz spectroscopy

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

doi:10.1016/j.surfcoat.2020.125836

Cited by 31 publications

(22 citation statements)

References 38 publications

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“…A novel hybrid machine-learning method was proposed by Ye et al [27] to predict the microstructural features of TBCs using thermal spray processing parameters based on a support vector machine method optimised by the cuckoo search algorithm (CS-SVM). In this work, atmospheric-plasma-sprayed (APS) TBCs samples with multifarious microstructural features were acquired by modifying the spray powder size, spray distance, and spray power during thermal spray processing.…”

Section: Support Vector Machine Methods Optimised By the Cuckoo Search Algorithm (Cs-svm)mentioning

confidence: 99%

Application of Machine Learning to Solid Particle Erosion of APS-TBC and EB-PVD TBC at Elevated Temperatures

Liu¹,

Ravichandran²,

Chen

et al. 2021

Coatings

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Machine learning (ML) and deep learning (DL) for big data (BD) management are currently viable approaches that can significantly help in high-temperature materials design and development. ML-DL can accumulate knowledge by learning from existing data generated through multi-physics modelling (MPM) and experimental tests (ETs). DL mainly involves analyzing nonlinear correlations and high-dimensional datasets implemented through specifically designed numerical algorithms. DL also makes it possible to learn from new data and modify predictive models over time, identifying anomalies, signatures, and trends in machine performance, develop an understanding of patterns of behaviour, and estimate efficiencies in a machine. Machine learning was implemented to investigate the solid particle erosion of both APS (air plasma spray) and EB-PVD (electron beam physical vapour deposition) TBCs of hot section components. Several ML models and algorithms were used such as neural networks (NNs), gradient boosting regression (GBR), decision tree regression (DTR), and random forest regression (RFR). It was found that the test data are strongly associated with five key factors as identifiers. Following test data collection, the dataset is subjected to sorting, filtering, extracting, and exploratory analysis. The training and testing, and prediction results are analysed. The results suggest that neural networks using the BR model and GBR have better prediction capability.

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Section: Support Vector Machine Methods Optimised By the Cuckoo Search Algorithm (Cs-svm)mentioning

confidence: 99%

Application of Machine Learning to Solid Particle Erosion of APS-TBC and EB-PVD TBC at Elevated Temperatures

Liu¹,

Ravichandran²,

Chen

et al. 2021

Coatings

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“…Principal component analysis (PCA) is an important statistical analysis approach to evaluate the correlation between multiple variables. Through linear transformation, the original data is transformed into a set of linearly independent representations of each dimension, and the main features of the data can be extracted as much as possible while retaining the original variable information as much as possible, and the weights can be calculated [17,28].…”

Section: Data Processing and Feature Extractionmentioning

confidence: 99%

Nondestructive Evaluation of Thermal Barrier Coatings Interface Delamination Using Terahertz Technique Combined with SWT-PCA-GA-BP Algorithm

Wang

Yin

et al. 2020

Coatings

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Thermal barrier coatings (TBCs) are usually subjected to the combined action of compressive stress, tensile stress, and bending shear stress, resulting in the interfacial delamination of TBCs, and finally causing the ceramic top coat to peel off. Hence, it is vital to detect the early-stage subcritical delamination cracks. In this study, a novel hybrid artificial neural network combined with the terahertz nondestructive technology was presented to predict the thickness of interface delamination in the early stage. The finite difference time domain (FDTD) algorithm was used to obtain the raw terahertz time-domain signals of 32 TBCs samples with various thicknesses of interface delamination, not only that, the influence of roughness and the thickness of the ceramic top layer were considered comprehensively when modeling. The stationary wavelet transform (SWT) and principal component analysis (PCA) methods were employed to extract the signal features and reduce the data dimensions before modeling, to make the cumulative contribution rate reach 100%, the first 31 components of the SWT detail data was used as the input data during modeling. Finally, a back propagation (BP) neural network method optimized by the genetic algorithm (GA-BP) was proposed to set up the interface delamination thickness prediction model. As a result, the root correlation coefficient R2 reached over 0.95, the various errors—including the mean square error, mean squared percentage error, and mean absolute percentage error—were less than or equal to 0.53. All these indicators proved that the trained hybrid SWT-PCA-GA-BP model had excellent prediction performance and high accuracy. Finally, this work proposed a novel and convenient interface delamination evaluation method that could also be potentially utilized to evaluate the structural integrity of TBCs.

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“…Moreover, a deep learning algorithm was used for classification of THz images 24 , 25 . In addition, a simple neural network for resolving of coating thickness 26 and a back-propagation (BP) neural network and SVM for conducting regression analysis for the characterization of thermal barrier coatings with THz-TDS were realized 27 . Besides ML algorithms 28 , various segmentation methods 29 , 30 were applied for automated detection of concealed objects from THz images.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for pattern and waveform recognitions in terahertz image data

Bulgarevich

Talara

Tani

et al. 2021

Sci Rep

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Several machine learning (ML) techniques were tested for the feasibility of performing automated pattern and waveform recognitions of terahertz time-domain spectroscopy datasets. Out of all the ML techniques under test, it was observed that random forest statistical algorithm works well with the THz datasets in both the frequency and time domains. With such ML algorithm, a classifier can be created with less than 1% out-of-bag error for segmentation of rusted and non-rusted sample regions of the image datasets in frequency domain. The degree of linear correlation between the rusted area percentage and the image spatial resolution with terahertz frequency can be used as an additional cross-validation criteria for the evaluation of classifier quality. However, for different rust staging measured datasets, a standardized procedure of image pre-processing is necessary to create/apply a single classifier and its usage is only limited to 1 ± 0.2 THz. Moreover, random forest is practically the best choice among the several popular ML techniques under test for waveform recognition of time-domain data in terms of classification accuracy and timing. Our results demonstrate the usefulness of random forest and several other machine learning algorithms for terahertz hyperspectral pattern recognition.

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Characterization of thermal barrier coatings microstructural features using terahertz spectroscopy

Cited by 31 publications

References 38 publications

Application of Machine Learning to Solid Particle Erosion of APS-TBC and EB-PVD TBC at Elevated Temperatures

Application of Machine Learning to Solid Particle Erosion of APS-TBC and EB-PVD TBC at Elevated Temperatures

Nondestructive Evaluation of Thermal Barrier Coatings Interface Delamination Using Terahertz Technique Combined with SWT-PCA-GA-BP Algorithm

Machine learning for pattern and waveform recognitions in terahertz image data

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