Back Propagation Neural Network Model Research Articles

Corrosion pit-induced stress concentration in 7005 aluminium alloy: Mechanical degradation and pit parameter analysis

Mechanical testing and surface analysis were used to investigate the corrosion behaviour of 7005 aluminium alloys in simulated atmospheric environments. Stress concentrations of pits lead to degradation of mechanical properties. The effect of pit sizes and location parameters on stress concentration was analysed using finite element (FE) method. The location of the secondary pits within the primary pits affects the stress concentration factor (SCF). Secondary pits located in the unaffected region have almost no effect on the stress concentration. A Back Propagation neural network (BPNN) model was developed to predict the stress concentrations of secondary pits with different location parameters.

Post-cyclic behavior of Haikou marine clay and their predictions using back-propagation neural network model

The evaluation of post-cyclic behavior of soft clay is critical for the lifetime safety and performance assessment of various infrastructures built on/in it. In this study, by employing the marine clays collected from Haikou, China, a series of both conventional shear tests and post-cyclic shear tests under undrained condition were performed using triaxial setup to investigate the post-cyclic behavior of the clay, with the investigation foci placed on the post-cyclic shear stress and secant modulus. Some important factors, namely confining pressure, loading frequency, cyclic shear strain amplitude and post-cyclic shear strain, were taken into consideration. From both the results from the tests and the parameter sensitivity analysis using random forest method, the confining pressure and cyclic shear strain amplitude were found to be evidently influential on the post-cyclic behavior of the clay, while the influence of loading frequency was relatively insignificant. In addition, an intelligent model was established based on the back-propagation neural network method to predict the post-cyclic behavior of Haikou marine clay, which was proven to be more favorable as compared to other two prediction methods developed based on multi-variate regression analysis and random forest algorithm.

Estimation of road friction coefficient based on WOA-BP neural network

The road friction coefficient is often estimated based on vehicle dynamics models, but the ideal mathematical model is difficult to describe the actual vehicle operation, which in turn leads to deviations between the estimated friction coefficient and the actual situation. In order to take full account of the actual vehicle driving conditions, back propagation (BP) neural network model is often applied to assess the friction coefficient. To further improve the generalization ability of the network model, this paper proposes a method to predict the friction coefficient using the whale optimized algorithm-BP (WOA-BP) neural network model. The whale algorithm improves the weights and thresholds of the BP neural network model to improve generalization ability and convergence speed. First, the vehicle dynamics analysis is utilized to determine the vehicle dynamics parameters as a function of the friction coefficient. Then, the WOA-BP is compared against the BP under various realistic operating scenarios. The results demonstrate that the WOA-BP estimator is accurate and efficient in estimating the friction coefficient, and the proposed method reduces the mean absolute error by 51.9% and the root mean squared error by 49.1% compared to the BP.

Performance Prediction and Optimization of Single-Piston Free Piston Expander-Linear Generator Based on Machine Learning and Genetic Algorithm

This paper proposed a single-piston free piston expander-linear generator (SFPE-LG) prototype applied to organic Rankine cycle systems. Two valve timing control strategies, namely, time control strategy (TCS) and position control strategy (PCS), were developed. Based on the experimental data, a back propagation neural network (BPNN) prediction model was established. The effects of structural parameters such as neural network layers, transfer function, training function, hidden layer nodes, and learning rate on the prediction accuracy of this BPNN model were discussed. The training and prediction accuracy of the BPNN model was verified using 5-fold cross-validation and Wilcoxon signed-rank test. Moreover, the BPNN model was integrated with a genetic algorithm to predict and optimize the maximum output power of the SFPE-LG. The results showed that the BPNN model used to predict the motion characteristics and output performance of the SFPE-LG exhibits strong learning ability and high prediction accuracy. Notably, the prediction accuracy of the BPNN model is significantly higher under the PCS compared to TCS. The effect of hidden layer nodes on mean square error (MSE) and correlation coefficient (R) is greater than that of the learning rate. When the number of hidden layer nodes exceeds 30, the BPNN model consistently achieves low MSE and high R. The optimization results showed that the SFPE-LG can obtain a maximum output power of 141.69 W under the TCS, when the working parameters are inlet pressure of 0.7 MPa, intake duration of 35 ms, load resistance of 67 Ω, and expansion duration of 104 ms, respectively.

Maximum Thinning Rate Prediction of Friction Heat Single-Point Incremental Forming for AZ31B Magnesium Alloy Based on BP Neural Network

The purpose of this project is to build an accurate prediction model for the maximum thinning rate of the truncated cone made of magnesium alloy AZ31B by single-point incremental forming. A truncated cone’s thickness distribution is analyzed using ABAQUS. Orthogonal experiments and supplementary data were performed as training samples within the maximum refinement rate obtained from the finite element (FE) results. Back-propagation (BP) neural network is established by taking into account tool diameter, initial sheet thickness, and forming angle as input responses, and maximum thinning rate as output responses, and nine hidden layers are involved. The results showed that the incidence of process parameters on the maximum thinning rate of the truncated cone is in the order of forming angle, initial thickness of sheet, and diameter of tool from large to small; The correlation coefficient of BP neural network model training [Formula: see text]. The test results and FE results contrast, and the average error rate of the maximum thinning rate is only 0.97%. The results indicate that the model is reliable and accurate for the prediction of incremental forming, and it is a useful reference value.

Cadmium toxicity to and accumulation in a soil collembolan (Folsomia candida): major factors and prediction using a back-propagation neural network model.

Accurate prediction of cadmium (Cd) ecotoxicity to and accumulation in soil biota is important in soil health. However, very limited information on Cd ecotoxicity on naturally contaminated soils. Herein, we investigated soil Cd ecotoxicity using Folsomia candida, a standard single-species test animal, in 28 naturally Cd-contaminated soils, and the back-propagation neural network (BPNN) model was used to predict Cd ecotoxicity to and accumulation in F. candida. Soil total Cd and pH were the primary soil properties affecting Cd toxicity. However, soil pH was the main factor when the total Cd concentration was < 3mgkg-1. Interestingly, correlation analysis and the K-spiked test confirmed nutrient potassium (K) was essential for Cd accumulation, highlighting the significance of studying K in Cd accumulation. The BPNN model showed greater prediction accuracy of collembolan survival rate (R2 = 0.797), reproduction inhibitory rate (R2 = 0.827), body Cd concentration (R2 = 0.961), and Cd bioaccumulation factor (R2 = 0.964) than multiple linear regression models. Then the developed BPNN model was used to predict Cd ecological risks in 57 soils in southern China. Compared to multiple linear regression models, the BPNN models can better identify high-risk regions. This study highlights the potential of BPNN as a novel and rapid tool for the evaluation and monitoring of Cd ecotoxicity in naturally contaminated soils.

IMPROVING THE EVALUATION AND PREDICTION OF PREVENTION AND TREATMENT EFFICIENCY DURING PUBLIC HEALTH EMERGENCIES BY USING THE SBM-BPNN ALGORITHM

Crisis response is the key to reducing the impact and damage of public health emergencies. Previous studies only focused on specific measures for emergency response, but the effectiveness of these measures has seldom been discussed. How about the effective of the crisis response on public health emergencies? What will happen to the effectiveness of crisis response after the transformation of public health emergency measures? To address these questions, a novel research framework was constructed by combining slacks-based measure (SBM) and back propagation neural network (BPNN) algorithms. At Stage I, an efficiency evaluation was conducted on SBM with undesirable outputs to evaluate the prevention and treatment efficiency during public health emergencies, and stochastic frontier analysis was performed to mitigate the influence of environmental factors and statistical noise. At Stage II, the adjusted efficiency was predicted with the BPNN model. The decision-making units were effectively improved by incorporating slack variables, thus enabling the prediction and optimization of optimal cases given the epidemic resources. An empirical analysis of the response of 43 G20 member countries to the COVID-19 pandemic showed that the novel framework could evaluate prevention and treatment efficiency across regions and predict the efficiency and optimal case outputs following shifts in epidemic preventive measures. After evaluation, the mean squared error of the BPNN efficiency prediction model was only 0.0014, whereas that of the BPNN optimal output prediction model was 0.126. Therefore, this novel framework is suitable for evaluating and predicting the effectiveness of crisis response on public health emergencies, which provides necessary assistance for crisis decision-making by the government and emergency management organizations., Keywords: Public health emergencies, COVID-19 pandemic, Crisis response efficiency, Evaluation, Prediction.

Risk factors and prediction models for cardiovascular complications of hypertension in older adults with machine learning: A cross-sectional study

BackgroundHypertension has emerged as a chronic disease prevalent worldwide that may cause severe cardiovascular complications, particularly in older patients. However, there is a paucity of studies that use risk factors and prediction models for cardiovascular complications associated with hypertension in older adults. ObjectivesTo identify the risk factors and develop prediction models for cardiovascular complications among older patients with hypertension. MethodsA convenience sample of 476 older patients with hypertension was recruited from a university-affiliated hospital in China. Demographic data, clinical physiological indicators, regulatory emotional self-efficacy, medication adherence, and lifestyle information were collected from participants. Binary logistic regression analysis was performed to screen for preliminary risk factors associated with cardiovascular complications. Two machine learning methods, Back-Propagation neural network, and random forest were applied to develop prediction models for cardiovascular complications among the study cohort. The sensitivity, specificity, accuracy, receiver operating characteristic curve, and area under the curve (AUC) values were used to assess the performance of the prediction models. ResultsBinary logistic regression identified nine risk factors for cardiovascular complications among older patients with hypertension. The machine learning models displayed excellent performance in predicting cardiovascular complications, with the random forest model (AUC 0.954) outperforming the Back-Propagation neural network model (AUC 0.811), as confirmed by model comparison analysis. The sensitivity, specificity and accuracy of the Back-Propagation neural network model compared to the random forest model were 74.2% vs. 86.5%, 75.2% vs. 94.3%, and 74.7% vs. 90.4%, respectively. ConclusionThe machine learning methods employed in this study demonstrated feasibility in predicting cardiovascular complications among older patients with hypertension, with the random forest model based on nine risk factors exhibiting excellent prediction performance. These models could be used to identify high-risk populations and suggest early interventions aimed at preventing cardiovascular complications in such cohorts.

Flame state recognition method of a scramjet based on PLIF image fusion features and an artificial neural network

In this study, a pattern recognition model is proposed to differentiate the flame state of a scramjet using an artificial neural network. The flame images are obtained from a scramjet ground test utilizing planar laser-induced fluorescence (PLIF). By extracting basic features, Hu moments and Zernike moments, the preprocessed flame images are compressed to mine valuable information. In order to reduce redundant input features and improve the efficiency of model operation, the partial least squares (PLS) method is introduced for feature screening and fusion. Then, a back propagation neural network (BPNN) model for multi-flame classification is established and analyzed. Finally, the flame states are determined by comparing the probabilities of different states. In order to optimize the recognition performance, the fusion features are studied and discussed. Experimental results show that when the filtered 11-dimensional features are used as input, the average recognition rate for the four states can reach 97.4%. These results demonstrate the significant potential of integrating PLIF and advanced data analysis methods, thereby broadening their application to intricate combustion fields.

Groundwater chemical characteristic analysis and water source identification model study in Gubei coal mine, Northern Anhui Province, China

This study aims to accurately identify mine water sources and reduce the hazards caused by water inrush accidents in coal mines. Taking the Gubei coal mine as an example, the water quality results of the water samples from the Cenozoic unconsolidated aquifer, Permian sandstone fracture aquifer, and Carboniferous Taiyuan Formation limestone karst fracture aquifer in the mine area were tested, and K++Na+, Ca2+, Mg2+, Cl−, SO42−, HCO3−, TDS (Total Dissolved Solids), and pH were selected as the main indicators to study the water chemistry characteristics of the aquifer through water chemistry component analysis, major ion content analysis, Piper trilinear analysis, and correlation analysis. Thirty-five groups of water samples were randomly selected and imported into SPSS software for factor analysis (FA) and downsized to three main factors as the input variables of the artificial neural network model. The particle swarm optimization (PSO) code was written based on the MATLAB platform to improve the self-adjustment weights and acceleration factors for optimizing the initial weights and thresholds of the Back-Propagation (BP) neural network. The training and prediction samples were learned in the ratio of 8:2, and the recognition results were compared with the traditional BP neural network model. Results showed that the groundwater of the Gubei coal mine demonstrated a water quality vertical zoning pattern, and the chemical composition was dominated by cation K++Na+ and anion Cl−. The FA-PSO-BP neural network model has a higher accuracy of water source discrimination compared with the cluster analysis and the FA-BP neural network model. The FA-PSO-BP neural network model is worthy of further application in the problem of water source identification in mine water inrush.

Feed Error Prediction and Compensation of CNC Machine Tools Based on Whale Particle Swarm Backpropagation Neural Network

Current modeling methods of machine tool feed error are challenging to meet the demand of high-precision machining when facing complex machining conditions. To enhance the model’s predictive accuracy and the effectiveness of actual compensation, the Whale Particle Swarm Optimization (WPSO) algorithm is proposed to optimize the Backpropagation Neural Network (BPNN). Subsequently, the optimized network incorporates screw elongation and feed position as inputs to establish a feed-error prediction model. Ultimately, the established model was compared with other models and applied to real-time compensation experiments. The research results show that the proposed prediction model outperforms the BPNN model, the particle swarm-optimized BPNN model, and the whale-optimized BPNN model in various indicators. The accuracy of the prediction model was 93.12%, and the errors ranged from −3.80 μm to 4.57 μm with an average error of −0.30 μm. Under different operating conditions, the maximum backward and forward errors are reduced by 33.21% and 87.21%, and the average backward and forward errors are reduced by 57.15% and 84.37%, respectively. The error range is reduced by 67.41%. Beyond elevating prediction accuracy and compensation efficacy, the proposed model offers robust theoretical guidance for practical production.

A spatio-temporal temperature prediction model for coal spontaneous combustion based on back propagation neural network

Coal spontaneous combustion (CSC) occurs with the change of temperature and gas concentration which is useful for early warning. To explore the intrinsic correlation between temperature evolution and gas distribution, a Back Propagation Neural Network (BPNN) model of coal temperature - gas composition - spatial distance was constructed, which is based on the gas characteristics and spatial distribution of coal temperature in large-scale coal spontaneous combustion experiments. The results showed that the O2 concentration reached the lowest value (8.058%) at 45 cm on Day 52 with the time. CO and CO2 also formed concentration peaks at 45 cm, and the gas distribution continuously moved towards the inlet position. The hydrocarbon gas components showed periodic increases, with values below 100 ppm. When the supply air time increased, the temperature rise rate in the initial stage of oxidation was slow but relatively uniform. Affected by ventilation conditions and coal density, two high-temperature zones were observed at 45 cm and 85 cm in the experimental furnace. With the inclusion of spatial distance parameters in coal temperature prediction, the BPNN model exhibits accurate predictive capability, with a total error of 0.94997. This can provide a method for predicting temperature changes in coal mine.

Experimental Analyses and Predictive Modelling of Ultrasonic Welding Parameters for Enhancing Smart Textile Fabrication.

This study aims to illustrate the design, fabrication, and optimisation of an ultrasonic welding (UW) machine to join copper wires with non-woven PVC textiles as smart textiles. The study explicitly evaluates UW parameters' impact on heat generation, joint strength, and electrical properties, with a comprehensive understanding of the process dynamics and developing a predictive model applicable to smart textiles. The methodological approach involved designing and manufacturing an ultrasonic piezoelectric transducer using ABAQUS finite element analyses (FEA) software and constructing a UW machine for the current purpose. The full factorial design (FFD) approach was employed in experiments to systematically assess the influence of welding time, welding pressure, and copper wire diameter on the produced joints. Experimental data were meticulously collected, and a backpropagation neural network (BPNN) model was constructed based on the analysis of these results. The results of the experimental investigation provided valuable insights into the UW process, elucidating the intricate relationship between welding parameters and heat generation, joint strength, and post-welding electrical properties of the copper wires. This dataset served as the basis for developing a neural network model, showcasing a high level of accuracy in predicting welding outcomes compared to the FFD model. The neural network model provides a valuable tool for controlling and optimising the UW process in the realm of smart textile production.

Application of SCMs and seawater to cement-bonded calcareous sand: Macro performance, micro mechanism, and strength prediction

Cement-bonded calcareous sand (C-BCS) demonstrates promising potential for marine island construction due to its exceptional mechanical properties. However, challenges such as freshwater scarcity and the erosive effects of seawater hinder the extensive utilization of C-BCS. To address this, this study comprehensively examines the feasibility of incorporating supplementary cementitious materials (SCMs) and seawater in C-BCS, focusing on macroscopic properties, microscopic mechanisms, and strength prediction. Macroscopic tests reveal that blast furnace slag and silica fume effectively enhance the hydration process and mechanical properties of C-BCS. Moreover, the cemented specimens developed in this study partially meet the strength standards for road pavement sub-base and airport runway upper-base applications. Microscopic analyses indicate that calcareous sand serves as a plentiful source of calcium, facilitating the formation of calcium aluminate silicate hydrate (C-A-S-H) and tobermorite when combined with Al2O3 and reactive SiO2 from slag and silica fume. This enhances the mechanical properties of the samples and their resistance against seawater erosion. Additionally, a back propagation (BP) neural network model accurately predicts the unconfined compressive strength of the cemented specimens. The findings of this study support the broader implementation of SCMs and seawater in practical island engineering projects involving C-BCS.

Prediction modeling of cigarette ventilation rate based on genetic algorithm backpropagation (GABP) neural network

AbstractThe ventilation rate of cigarettes is an important indicator that affects the internal quality of cigarettes. When producing cigarettes, the unit may experience unstable ventilation rates, which can lead to a decrease in cigarette quality and pose certain risks to smokers. By establishing the ventilation rate prediction model, guide the design of unit parameters in advance, to achieve the goal of stabilizing unit ventilation rate, improve the stability of cigarette ventilation rate, and enhance the quality of cigarettes. This paper used multiple linear regression networks (MLR), backpropagation neural networks (BPNN), and genetic algorithm-optimized backpropagation (GABP) to construct a model for the prediction of cigarette ventilation rate. The model results indicated that the total ventilation rate was significantly positively correlated with weight (P &lt; 0.01), circumference, hardness, filter air permeability, and open resistance. The results showed that the MLR models' (RMSE = 0.652, R2 = 0.841) and the BPNN models’ (RMSE = 0.640, R2 = 0.847) prediction ability were limited. Optimization by genetic algorithm, GABP models were generated and exhibited a little better prediction performance (RMSE = 0.606, R2 = 0.873). The results indicated that the GABP model has the highest accuracy in the prediction of predicting ventilation rate and can accurately predict cigarette ventilation rate. This method can provide theoretical guidance and technical support for the stability study of the ventilation rate of the unit, improve the design and manufacturing capabilities and product quality of short cigarette products, and help to improve the quality of cigarettes.

Data-driven approaches for predicting performance degradation of solid oxide fuel cells system considering prolonged operation and shutdown accumulation effect

Solid oxide fuel cell system is widely acknowledged as the leading alternative energy generation system in the field. Due to their high efficiency, low emissions, low noise, and various other advantages, solid oxide fuel cell systems are being considered for use in automobiles as a replacement for traditional internal combustion engines. However, prolonged operation and abnormal shutdowns can lead to performance degradation, which affects the efficiency, stability, and lifespan of stack. In the context of prolonged operation, considering the fluctuations in stack performance parameters and balance of plant, several regression models based on voltage parameters are established to accurately predict changes in stack performance. The results reveal that the genetic algorithm optimized backpropagation neural network model is highly sensitive in predicting the system performance degradation. Further analysis reveals that abnormal shutdowns can cause system performance fluctuations. As a result, the number and duration of shutdowns are incorporated into genetic algorithm optimized backpropagation neural network model for analysis. This model exhibits the best performance degradation evolution prediction compared to the long short-term memory and particle swarm optimization backpropagation neural network methods. This finding is important for monitoring and controlling the operational status of stack.

Land surface temperature prediction with dual satellites and BPNN: a comparative transfer function analysis

ABSTRACT Driven by global sustainability goals, understanding land surface temperature (LST) in urban thermal environments (UTE) is crucial. This study analyses remotely sensed LST data from Aqua and Terra satellites in a rapidly growing non-tier Indian city. It uniquely integrates remote sensing and soft computational analysis to predict daytime LST using artificial neural networks (ANN). The study is categorized based on transfer functions and hyper-parameters to enhance the comprehension of LST prediction and simulation scenarios by employing Back Propagation Neural Network (BPNN) model. Results indicate that the tansig transfer function yields the best performance overall in Aqua and Terra LST prediction, while purelin performs the least effectively. This study provides evidence that soft computing techniques effectively estimate LST data, offering a valuable solution for data scarcity issues. However, the findings are specific to the study area and may vary elsewhere. Exploring alternative methods and incorporating data from other areas could potentially improve accuracy and overall performance. Future research could focus on investigating the mechanisms behind different methods and their interactions with various factors. Additionally, integrating other variables like vegetation index, soil moisture, and others could contribute to a more holistic understanding of LST dynamics and enhance predictive capabilities.

A Comparative Study of Vehicle Velocity Prediction for Hybrid Electric Vehicles Based on a Neural Network

Vehicle velocity prediction (VVP) plays a pivotal role in determining the power demand of hybrid electric vehicles, which is crucial for establishing effective energy management strategies and, subsequently, improving the fuel economy. Neural networks (NNs) have emerged as a powerful tool for VVP, due to their robustness and non-linear mapping capabilities. This paper describes a comprehensive exploration of NN-based VVP methods employing both qualitative theory analysis and quantitative numerical simulations. The used methodology involved the extraction of key feature parameters for model inputs through the utilization of Pearson correlation coefficients and the random forest (RF) method. Subsequently, three distinct NN-based VVP models were constructed comprising the following: a backpropagation neural network (BPNN) model, a long short-term memory (LSTM) model, and a generative pre-training (GPT) model. Simulation experiments were conducted to investigate various factors, such as the feature parameters, sliding window length, and prediction horizon, and the prediction accuracy and computation time were identified as key performance metrics for VVP. Finally, the relationship between the model inputs and velocity prediction performance was revealed through various comparative analyses. This study not only facilitated the identification of an optimal NN model configuration to balance prediction accuracy and computation time, but also serves as a foundational step toward enhancing the energy efficiency of hybrid electric vehicles.

Prediction Model of Water Abundance of Weakly Cemented Sandstone Aquifer Based on Principal Component Analysis–Back Propagation Neural Network of Grey Correlation Analysis Decision Making

At present, in the vast majority of coal mine production processes in China, the degree of hydrogeological exploration often lags behind geological exploration. The main difficulty in evaluating the water richness of coal seam top and bottom water-bearing beds is that the existing evaluation methods often rely on less hydrogeological investigation data. How to utilize the abundant geological exploration data in the mining area to appraise the water-rich distribution of sandstone aquifers is a feasible and challenging methodology. At present, some experts and scholars have tried to use multivariate factor analysis to solve the problem of water-richness evaluation, and they have achieved certain results, but there are some shortcomings: (1) The prediction results are mostly qualitative estimations of the water-richness grade, and there is a lack of quantitative analysis of the units-inflow; and (2) at present, the more advanced prediction methods, such as the back propagation (BP) neural network model, have the disadvantages of low accuracy, requiring many iterations, and slow convergence speed. Therefore, with geological exploration data of the 1503E working face of the Yili No.1 coal mine as the basis., this paper uses grey correlation analysis to screen out the factor indexes suitable for the evaluation of the water richness of a weakly cemented sandstone aquifer, and it combines principal component analysis (PCA) with a BP neural network. Based on the selected factor indexes, a prediction model of the water richness of a weakly cemented sandstone aquifer is established. The results show that compared with the existing methods, the prediction accuracy is higher and has a certain application value.

Interface Reliability Modeling of Coaxial Through Silicon Via Based on WOA-BP Neural Network

Abstract Due to the complex structure and thermal mismatch of coaxial through silicon via (TSV), cracks easily occur under thermal load, leading to interface delamination or spalling failure. The reliability issue of coaxial TSV is important for its application in three-dimensional packaging, so it is of great significance to predict the crack trend and evaluate the reliability of coaxial TSV. In this paper, an algorithm model with the combination of whale optimization algorithm (WOA) and back propagation (BP) neural network for the reliability prediction of coaxial TSV is proposed. Based on finite element method (FEM), the training and validation datasets of the energy release rates (ERR) of the crack at the critical interface are calculated to construct the deep learning neural network. Six key structure parameters affecting the reliability of coaxial TSV are selected as the input values of the BP neural network. The maximum relative error of whale optimization algorithm optimized back propagation (WOA-BP) neural network model is 0.88%, which is better than the prediction results of the traditional BP and genetic algorithm (GA) optimized BP models. The WOA-BP neural network model was also compared with BP and GA-BP neural network models with four error metric models. It is verified that WOA-BP neural network model has the best prediction performance. The proposed model can be used to achieve improved prediction accuracy for the interface reliability of coaxial TSV under complex structural conditions since it has higher accuracy and stronger robustness.