Nondestructive Evaluation of Thermal Barrier Coatings Thickness Using Terahertz Technique Combined with PCA–GA–ELM Algorithm
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Thermography and Photoacoustic Techniques · High-Temperature Coating Behaviors · Thermal properties of materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Pressure System and Safety, Ministry of Education, School of Mechanical and Power Engineering, East China University of Science and Technology, No.130 Meilong Road, Shanghai 200237, China
School of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China
Laboratory of Optics, Terahertz and Non-Destructive Testing, School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Funding
National Natural Science Foundation of China · Science Center for Gas Turbine Project · Shanghai Joint Innovation Program in the Field of Commercial Aviation Engines
References (52)
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