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

Ye, Dongdong; Wang, Weize; Yin, Changdong; Xu, Zhou; Fang, Huanjie; Huang, Jibo; Li, Yuanjun

doi:10.3390/coatings10090859

Cited by 8 publications

(4 citation statements)

References 37 publications

(22 reference statements)

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“…(1) Based on the characteristics of the controlled system parameters, determine the basic structure of the BP neural network, including the number of input nodes [8], hidden layer nodes, and output nodes. Initialize the weighting coefficients w 1 ij(0) and w 2 ij(0) with initial values.…”

Section: Bp Neural Network Pid Controlmentioning

confidence: 99%

Research and application of intelligent car based on BP neural network PID control

Sun

2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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This paper aims to study and apply the BP neural network-based PID control for intelligent cars. Although the traditional PID controller has been widely used in control systems, its difficulties in parameter tuning and poor adaptability limit its performance in complex environments. To address this, the BP neural network is introduced as an auxiliary component to enhance the control system's robustness and adaptability. The paper first introduces the principles of PID controllers and BP neural networks and then designs a control strategy based on BP neural network-based PID control, taking into account the characteristics of the intelligent car system. Subsequently, the effectiveness of the control strategy on intelligent cars is verified through experiments and simulations, analyzing its performance indicators. Finally, the potential applications and future directions of intelligent cars in different scenarios are discussed.

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Section: Bp Neural Network Pid Controlmentioning

confidence: 99%

Research and application of intelligent car based on BP neural network PID control

Sun

2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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“…Existing information indicated that machine learning was highly advantageous in large-scale data modeling [26][27][28]. Therefore, machine learning was introduced to process terahertz time-domain spectral data in this study.…”

Section: Algorithms and Modelsmentioning

confidence: 99%

“…Existing information has shown that machine learning has been better applied in evaluating and predicting performance [26][27][28]. In this study, a simulation model was constructed based on the microstructural characterization data obtained from the inspection of actual service blades.…”

Section: Introductionmentioning

confidence: 99%

Terahertz Nondestructive Measurement of Heat Radiation Performance of Thermal Barrier Coatings Based on Hybrid Artificial Neural Network

Xu,

Yin,

et al. 2024

Coatings

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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.

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“…The data must be uniformly planned to ensure the validity of the data. Normalization methods are usually used for data preprocessing [26,27]. The formulation of the BP model can be expressed as follows:…”

Section: Bp Neural Network Model Optimized By Genetic Algorithmmentioning

confidence: 99%

Prediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network

Pan

et al. 2021

Coatings

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Grit blasting as a pretreatment process for the substrate surface before thermal spraying is of great importance for assuring the service performance of thermal spraying coatings. In this work, a novel hybrid artificial neural network (ANN) was presented to optimize the grit blasting process to improve the structural properties and corrosion resistance performance of thermal spraying coatings. Different grit blasting process parameters were combined to pretreat the substrate surface, and the corresponding surface roughness, interface adhesion strength and corrosion resistance performance were obtained. Hence, a backpropagation (BP) neural network model optimized by the genetic algorithm (GA) was presented to address the poor regression roughness and accuracy of the traditional fitting models; the grit blasting processing parameters were utilized as the inputs for the GA–BP model; the structural properties and the corrosion resistance performance were used as the outputs. The correlation coefficient R reached and exceeded 0.90, and three error values were less than 1.75 on the prediction of the service performance of random samples. All these indicators demonstrated convincingly that the obtained hybrid artificial neural network models possessed good prediction performance, and this innovative and time-saving grit blasting process optimization approach could be potentially employed to improve the comprehensive service performance of thermal spraying coatings.

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

Cited by 8 publications

References 37 publications

Research and application of intelligent car based on BP neural network PID control

Research and application of intelligent car based on BP neural network PID control

Terahertz Nondestructive Measurement of Heat Radiation Performance of Thermal Barrier Coatings Based on Hybrid Artificial Neural Network

Prediction and Analysis of the Grit Blasting Process on the Corrosion Resistance of Thermal Spray Coatings Using a Hybrid Artificial Neural Network

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