The Cross-Scale Interactive Simulation and Artificial Neural Network Failure Prediction of Temperature Field on Wet Friction Interface

Aiming at the temperature failure for wet friction interface within mechanical transmissions, this article constructs a single-pair asperities contact model, which considers the influence of rough surface micro-contact states on the wet friction interface temperature field, and it interacts with the macro thermal-mechanical coupled simulation model in real-time. Then, the interface temperature field is analyzed by cross-scale interactive simulation model, and two types of temperature field failure characterization parameters are proposed. The accuracy of the cross-scale interactive simulation model is validated through the wet clutch accelerated life test. Furthermore, this article constructs a failure prediction model focusing on critical circumferential and radial temperature differences. Subsequently, it investigates the influence patterns of pressure and speed on the failure characteristic parameters based on the controlled variable method. The results indicate that the cross-scale interactive simulation model exhibits higher reliability and accuracy compared to the macro thermal-mechanical coupled simulation model. The average error of the failure prediction model is 3.71%. Additionally, the failure characteristic parameters are more sensitive to pressure variations.

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Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3550954
Citations
4
Primary Topic
Advanced Decision-Making Techniques
Type
article
Field-Weighted Citation Impact
12.98

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article

The Cross-Scale Interactive Simulation and Artificial Neural Network Failure Prediction of Temperature Field on Wet Friction Interface

Jianpeng Wu, H. Zhang, Liyong Wang, Chengbing Yang
4 citations
IEEE Transactions on Instrumentation and Measurement
Advanced Decision-Making Techniques
12.98
article

The Cross-Scale Interactive Simulation and Artificial Neural Network Failure Prediction of Temperature Field on Wet Friction Interface

Jianpeng Wu, H. Zhang, Liyong Wang, Chengbing Yang
article en
4 citations

Abstract

Aiming at the temperature failure for wet friction interface within mechanical transmissions, this article constructs a single-pair asperities contact model, which considers the influence of rough surface micro-contact states on the wet friction interface temperature field, and it interacts with the macro thermal-mechanical coupled simulation model in real-time. Then, the interface temperature field is analyzed by cross-scale interactive simulation model, and two types of temperature field failure characterization parameters are proposed. The accuracy of the cross-scale interactive simulation model is validated through the wet clutch accelerated life test. Furthermore, this article constructs a failure prediction model focusing on critical circumferential and radial temperature differences. Subsequently, it investigates the influence patterns of pressure and speed on the failure characteristic parameters based on the controlled variable method. The results indicate that the cross-scale interactive simulation model exhibits higher reliability and accuracy compared to the macro thermal-mechanical coupled simulation model. The average error of the failure prediction model is 3.71%. Additionally, the failure characteristic parameters are more sensitive to pressure variations.

IEEE Transactions on Instrumentation and MeasurementVol. 74
Rice University (US), Beijing Information Science & Technology University (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 2%
Advanced Decision-Making Techniques
12.98
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The Cross-Scale Interactive Simulation and Artificial Neural Network Failure Prediction of Temperature Field on Wet Friction Interface — Jianpeng Wu, H. Zhang, et al. · IEEE Transactions on Instrumentation and Measurement (2025) | TGRS Research Map | TGRS