Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO

Zheng, Bo; Gao, Feng; Ma, Xin; Zhang, Xiaoqiang

doi:10.3390/app112210592

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Zheng et al used SVR coupled with guided PSO to predict the wear rate of aeroengine. The results indicated better prediction accuracy when compared with single SVR 52 . In a related development, Kahhal et al optimized and predicted the process parameters of friction stir welding of AH12 1050 using response surface algorithm coupled with PSO model.…”

Section: Resultsmentioning

confidence: 94%

Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models

Ibrahim

Çamur²,

Savas³

et al. 2022

Sci Rep

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This study presents optimization and prediction of tribological behaviour of filled polytetrafluoroethylene (PTFE) composites using hybrid Taguchi and support vector regression (SVR) models. To achieve the optimization, Taguchi Deng was employed considering multiple responses and process parameters relevant to the tribological behaviour. Coefficient of friction (µ) and specific wear rate (Ks) were measured using pin-on-disc tribometer. In this study, load, grit size, distance and speed were the process parameters. An L27 orthogonal array was applied for the Taguchi experimental design. A set of optimal parameters were obtained using the Deng approach for multiple responses of µ and KS. Analysis of variance was performed to study the effect of individual parameters on the multiple responses. To predict µ and Ks, SVR was coupled with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) forming SVR-HHO and SVR-PSO models respectively, were employed. Four model evaluation metrics were used to appraise the prediction accuracy of the models. Validation results revealed enhancement under optimal test conditions. Hybrid SVR models indicated superior prediction accuracy to single SVR model. Furthermore, SVR-HHO outperformed SVR-PSO model. It was found that Taguchi Deng, SVR-PSO and SVR-HHO models led to optimization and prediction with low cost and superior accuracy.

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Section: Resultsmentioning

confidence: 94%

Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models

Ibrahim

Çamur²,

Savas³

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

Research on cutting tool edge geometry design based on SVR-PSO

Jiang,

Huang,

Tian

et al. 2024

Int J Adv Manuf Technol

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In order to optimize the design of tool edge, an intelligent method was used for modeling and optimization. The tool edge design method based on Support Vector Regression (SVR) and Particle Swarm Optimization (PSO) was proposed. By combining tool edge parameters and processing condition parameters, and learning from empirical data, a functional model was established between tool life, edge parameters, and processing condition parameters. Taking the tool life as the objective function, the optimal edge pro le design parameters were solved under different processing condition parameters. The T-shape tool validates was taken as a case for veri cation. The SVR-PSO function model was established and solved based on the processing condition parameters, and the optimized edge design parameters and predicted tool life were obtained. The results showed that the deviation between the calculated and actual tool life was less than 6.4%. This method was feasible and practical, and has been applied in the design department of tool manufacturing companies.

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Effect of multiple environmental conditions on the formation process and adhesion strength of ice on asphalt binder surface

Yao,

Zhou,

et al. 2024

Construction and Building Materials

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Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO

Cited by 4 publications

References 19 publications

Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models

Optimization and prediction of tribological behaviour of filled polytetrafluoroethylene composites using Taguchi Deng and hybrid support vector regression models

Research on cutting tool edge geometry design based on SVR-PSO

Effect of multiple environmental conditions on the formation process and adhesion strength of ice on asphalt binder surface

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