Nondestructive Evaluation of Thermal Barrier Coatings Thickness Using Terahertz Technique Combined with PCA–GA–ELM Algorithm

Abstract

Thermal barrier coatings (TBCs) are usually used in high temperature and harsh environment, resulting in thinning or even spalling off. Hence, it is vital to detect the thickness of the TBCs. In this study, a hybrid machine learning model combined with terahertz time-domain spectroscopy technology was designed to predict the thickness of TBCs. The terahertz signals were obtained from the samples prepared in laboratory and actual turbine blade. The principal component analysis (PCA) method was used to decrease the data dimensions. Finally, an extreme learning machine (ELM) was proposed to establish the thickness of TBCs prediction model. Genetic algorithm (GA) was selected to optimize the model to make it more accurate. The results showed that the root correlation coefficient (R2) exceeded 0.97 and the errors (root mean square error and mean absolute percentage error) were less than 2.57. This study proposes that terahertz time-domain technology combined with PCA–GA–ELM model is accurate and feasible for evaluating the thickness of the TBCs.

Nondestructive Evaluation of Thermal Barrier Coatings Thickness Using Terahertz Time-Domain Spectroscopy Combined with Hybrid Machine Learning Approaches

“…The relevant parameters of the whale optimization algorithm and Elman neural network model are shown in Tables 3 and 4. Meanwhile, in order to verify the superiority of the combined PCA-WOA-Elman prediction model, its results were compared and analyzed with those of the back propagation (BP) neural network, single extreme learning machine (ELM) model [45], and Elman model. All four methods were simulated with MATLAB 2021a software, and a comparison of the prediction results is shown in Figure 9; single-point error was compared as shown in Figure 10.…”

“…It was evident that the PCA-WOA-Elman model had the slightest error est error variation, and the ELM model had the most significant error variatio in Table 5, the MAEs of the four models were 10.78, 6.04, 3.34, and 0.16, resp RMSEs were 12.92, 7.77, 4.43, and 0.28, and the MAPEs were 6.11%, 3.20% atings 2022, 12, x FOR PEER REVIEW Meanwhile, in order to verify the superiority of the combined PCA-WOA diction model, its results were compared and analyzed with those of the back (BP) neural network, single extreme learning machine (ELM) model [45] model. All four methods were simulated with MATLAB 2021a software, an son of the prediction results is shown in Figure 9; single-point error was shown in Figure 10.…”

Nondestructive Evaluation of Thermal Barrier Coatings’ Porosity Based on Terahertz Multi-Feature Fusion and a Machine Learning Approach

“…The propagation and interaction of terahertz waves within the TBCs were influenced by factors such as the coating's porosity, distribution of material composition, and microstructure. Hence, THz-TDS offered a highly sensitive approach to detecting the TBCs' porosity and its reflective properties at different frequencies [41]. In this study, THz-TDS was employed to investigate the TBCs' porosity on aero-engine blades, with a focus on obtaining time-domain data.…”

The Influence of Microstructure Characteristics on Thickness Measurement of TBCs Using Terahertz Time-Domain Spectroscopy

“…The difference compared with scanning electron microscopy (SEM) observation is about 30 µm when the thickness of TC is 330 µm. Yuan B. et al [19] discovered that when the thickness decreased, the signals of multiple reflection peaks would overlap due to the reduced time delay between reflection peaks.…”