Content area
Effective control of the micro- and nanostructure of thermal barrier coatings is essential to enhance the thermal radiation performance of the coating, which helps to determine the remaining service life of the coating. This paper proposed a method to measure the radiation properties of thermal barrier coatings by terahertz nondestructive testing technique, using APS-prepared thermal barrier coatings as the object of study. Radiative properties were a comprehensive set of properties characterized by the diffuse reflectance, transmittance, and absorptance of the thermal barrier coating. The coating data in actual service were obtained by scanning electron microscopy and metallographic experiments, and the data were used as the simulation model critical value. The terahertz time-domain simulation data of coatings with different microstructural features were obtained using the finite-different time-domain (FDTD) method. In simulating the real test signals, white noise with a signal-to-noise ratio of 20 dB was added, and fast Fourier transform (FFT), short-time Fourier transform (STFT), and wavelet transform (WT) were used to reduce the noise and compare their noise reduction effects. Different machine learning methods were used to build the model, including support vector machine algorithm (SVM) and k-nearest neighbor algorithm (KNN). The principal component algorithm (PCA) was used to reduce the dimensionality of terahertz time-domain data, and the SVM algorithm and KNN algorithm were optimized using the particle swarm optimization algorithm (PSO) and the ant colony optimization algorithm (ACO), respectively, to improve the robustness of the system. The K-fold cross-validation method was used to construct the model to improve the adaptability of the model. It could be clearly seen that the novel hybrid PCA-ACO-SVM model had superior prediction performance. Finally, this work proposed a novel, convenient, nondestructive, online, safe and highly accurate method for measuring the radiation performance of thermal barrier coatings, which could be used for the judgment of the service life of thermal barrier coatings.
Details
Particle swarm optimization;
Accuracy;
Nondestructive testing;
Coatings;
Simulation models;
Fast Fourier transformations;
Artificial neural networks;
Aviation;
White noise;
Time domain analysis;
Data processing;
Noise levels;
Service life;
Absorptance;
Machine learning;
Ant colony optimization;
Wavelet transforms;
Radiation;
Computer simulation;
Efficiency;
Big Data;
Simulation;
Research methodology;
Artificial intelligence;
Principal components analysis;
Support vector machines;
Thermal radiation;
Thermal barriers;
Noise reduction;
Algorithms;
Absorptivity;
Experimental methods;
Signal to noise ratio;
K-nearest neighbors algorithm
; Xu, Shuheng 4 ; Xu, Jianfei 5 ; Ye, Dongdong 6 1 School of Electrical and Automation, Wuhu Institute of Technology, Wuhu 241006, China;
2 Institute of Intelligent Manufacturing, Wuhu Institute of Technology, Wuhu 241006, China;
3 Department of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, China;
4 School of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China;
5 Department of Automotive Engineering and Intelligent Manufacturing, Wanjiang College of Anhui Normal University, Wuhu 241008, China;
6 School of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China;