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

Baohan Yuan,Weize Wang,Dongdong Ye,Ting Yang,Huanjie Fang,Yihao Wang,Zhenghao Zhang,Shuncong Zhong

doi:10.3390/coatings12030390

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.

Highlights

The typical Thermal barrier coatings (TBCs) systems prepared by atmospheric-plasma-sprayed (APS) consist of four layers: a ceramic top coat (TC)—the material is usually 6–8 wt% Y2 O3 -stabilized-ZrO2 (YSZ)—acting as thermal insulator; a metallic bond coat (BC)—the material is usually MCrAlY—providing the oxidation protection; and a superalloy substrate and thermal grown oxides (TGO), which are generated owing to the high temperature oxidation of the metal in BC at the interface of BC/TC [1–5]
We propose principal component analysis (PCA)–Genetic algorithm (GA)–extreme learning machine (ELM) model to predict the TC thickness
A hybrid machine learning algorithm based on terahertz time domain spectroscopy was proposed to predict the thickness of TC layer of thermal barrier coatings

Summary

Introduction

The increasing thrust-weight-ratio of an aeroengine increases the demand for the high temperature resistance of the hot-section components of aero-engines. Thermal barrier coatings (TBCs) have become an important means to protect the hot-end components of aero-engines from high temperature because of their good high temperature resistance, low thermal conductivity, and good corrosion resistance. The typical TBCs systems prepared by atmospheric-plasma-sprayed (APS) consist of four layers: a ceramic top coat (TC)—the material is usually 6–8 wt% Y2 O3 -stabilized-ZrO2 (YSZ)—acting as thermal insulator; a metallic bond coat (BC)—the material is usually MCrAlY—providing the oxidation protection; and a superalloy substrate and thermal grown oxides (TGO), which are generated owing to the high temperature oxidation of the metal in BC at the interface of BC/TC [1–5]. The premature failure of the coating directly exposes the superalloy to the high temperature environment, resulting in a detrimental effect on the performances of aero-engine [6–11]. In order to guarantee the structural integrity and service performance of aero-engine blades, it is necessary to effectively monitor the structural integrity of TBCs

Methods

Discussion

Conclusion