Parameter-based RNN micro-interface inversion model for wet friction components morphology
The interface morphology significantly impact the service life of wet clutches friction components in heavy tracked vehicle transmission systems. This paper designs a sliding test and utilizes a recurrent neural network (RNN) model to construct the three-dimensional morphology of the wet clutch fric...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824010159 |
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| _version_ | 1846113799545815040 |
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| author | Jianpeng Wu Yuxin Wang Chengbing Yang Xiaozan Huang Liyong Wang |
| author_facet | Jianpeng Wu Yuxin Wang Chengbing Yang Xiaozan Huang Liyong Wang |
| author_sort | Jianpeng Wu |
| collection | DOAJ |
| description | The interface morphology significantly impact the service life of wet clutches friction components in heavy tracked vehicle transmission systems. This paper designs a sliding test and utilizes a recurrent neural network (RNN) model to construct the three-dimensional morphology of the wet clutch friction interface under specific operating conditions. It also explores the relationship between these factors and clutch performance. The interface morphology characteristics are analyzed by the RNN inversion model to assess the effects of three working condition parameters including rotational speed, pressure, and sliding time. This research provides important primary data for engineering studies and applications aimed at optimizing the design of wet clutches and improving transmission system reliability. |
| format | Article |
| id | doaj-art-8d33a001094249e3962a4f08a750176b |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-8d33a001094249e3962a4f08a750176b2024-12-21T04:27:51ZengElsevierAlexandria Engineering Journal1110-01682024-12-01109229238Parameter-based RNN micro-interface inversion model for wet friction components morphologyJianpeng Wu0Yuxin Wang1Chengbing Yang2Xiaozan Huang3Liyong Wang4Corresponding author.; Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100192, ChinaKey Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100192, ChinaKey Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100192, ChinaKey Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100192, ChinaKey Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100192, ChinaThe interface morphology significantly impact the service life of wet clutches friction components in heavy tracked vehicle transmission systems. This paper designs a sliding test and utilizes a recurrent neural network (RNN) model to construct the three-dimensional morphology of the wet clutch friction interface under specific operating conditions. It also explores the relationship between these factors and clutch performance. The interface morphology characteristics are analyzed by the RNN inversion model to assess the effects of three working condition parameters including rotational speed, pressure, and sliding time. This research provides important primary data for engineering studies and applications aimed at optimizing the design of wet clutches and improving transmission system reliability.http://www.sciencedirect.com/science/article/pii/S1110016824010159Micro-interface morphologyThe RNN inversion modelWorking condition parametersWet friction components |
| spellingShingle | Jianpeng Wu Yuxin Wang Chengbing Yang Xiaozan Huang Liyong Wang Parameter-based RNN micro-interface inversion model for wet friction components morphology Alexandria Engineering Journal Micro-interface morphology The RNN inversion model Working condition parameters Wet friction components |
| title | Parameter-based RNN micro-interface inversion model for wet friction components morphology |
| title_full | Parameter-based RNN micro-interface inversion model for wet friction components morphology |
| title_fullStr | Parameter-based RNN micro-interface inversion model for wet friction components morphology |
| title_full_unstemmed | Parameter-based RNN micro-interface inversion model for wet friction components morphology |
| title_short | Parameter-based RNN micro-interface inversion model for wet friction components morphology |
| title_sort | parameter based rnn micro interface inversion model for wet friction components morphology |
| topic | Micro-interface morphology The RNN inversion model Working condition parameters Wet friction components |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824010159 |
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