Prediction model for oil seal performance parameters based on PSO-MLP-KAN
Oil seals are critical sealing components in mechanical systems, and their sealing performance directly impacts the operational reliability of the entire system. To accurately, efficiently, and stably predict the sealing performance of oil seals, this study proposes a prediction method based on a co...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2025-05-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0255178 |
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| author | Weixing Yan Mingshuo Shi Pengbo Xiao Kui Zhang Xin Wu |
| author_facet | Weixing Yan Mingshuo Shi Pengbo Xiao Kui Zhang Xin Wu |
| author_sort | Weixing Yan |
| collection | DOAJ |
| description | Oil seals are critical sealing components in mechanical systems, and their sealing performance directly impacts the operational reliability of the entire system. To accurately, efficiently, and stably predict the sealing performance of oil seals, this study proposes a prediction method based on a combination of the Kolmogorov–Arnold network (KAN) and multi-layer perceptron (MLP) optimized by particle swarm optimization (PSO) algorithm. First, an oil seal performance prediction model is developed, taking into account macroscopic contact forces and contact curve parameters. To address the challenges posed by incomplete experimental data and limited data quantity, a hybrid approach integrating simulation data and experimental data is employed, complemented by the use of the Latin hypercube sampling method to construct an oil seal performance dataset. To further enhance model prediction accuracy, a PSO-MPL-KAN multi-output prediction model is established. Comparative analysis with existing prediction models demonstrates that the proposed PSO-MPL-KAN model achieves significantly higher accuracy in predicting oil seal sealing performance. |
| format | Article |
| id | doaj-art-8a0ece6479a144e98fb6589d4e415a3a |
| institution | OA Journals |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-8a0ece6479a144e98fb6589d4e415a3a2025-08-20T02:10:07ZengAIP Publishing LLCAIP Advances2158-32262025-05-01155055102055102-1210.1063/5.0255178Prediction model for oil seal performance parameters based on PSO-MLP-KANWeixing Yan0Mingshuo Shi1Pengbo Xiao2Kui Zhang3Xin Wu4School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaZhejiang Gunai Rubber and Plastic Science and Technology Co., Ltd., Huzhou 313000, ChinaSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaOil seals are critical sealing components in mechanical systems, and their sealing performance directly impacts the operational reliability of the entire system. To accurately, efficiently, and stably predict the sealing performance of oil seals, this study proposes a prediction method based on a combination of the Kolmogorov–Arnold network (KAN) and multi-layer perceptron (MLP) optimized by particle swarm optimization (PSO) algorithm. First, an oil seal performance prediction model is developed, taking into account macroscopic contact forces and contact curve parameters. To address the challenges posed by incomplete experimental data and limited data quantity, a hybrid approach integrating simulation data and experimental data is employed, complemented by the use of the Latin hypercube sampling method to construct an oil seal performance dataset. To further enhance model prediction accuracy, a PSO-MPL-KAN multi-output prediction model is established. Comparative analysis with existing prediction models demonstrates that the proposed PSO-MPL-KAN model achieves significantly higher accuracy in predicting oil seal sealing performance.http://dx.doi.org/10.1063/5.0255178 |
| spellingShingle | Weixing Yan Mingshuo Shi Pengbo Xiao Kui Zhang Xin Wu Prediction model for oil seal performance parameters based on PSO-MLP-KAN AIP Advances |
| title | Prediction model for oil seal performance parameters based on PSO-MLP-KAN |
| title_full | Prediction model for oil seal performance parameters based on PSO-MLP-KAN |
| title_fullStr | Prediction model for oil seal performance parameters based on PSO-MLP-KAN |
| title_full_unstemmed | Prediction model for oil seal performance parameters based on PSO-MLP-KAN |
| title_short | Prediction model for oil seal performance parameters based on PSO-MLP-KAN |
| title_sort | prediction model for oil seal performance parameters based on pso mlp kan |
| url | http://dx.doi.org/10.1063/5.0255178 |
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