Commercial vehicle disc brake temperature prediction model construction based on machine learning

The process of conducting thermal-mechanical coupling finite element analysis for commercial vehicle disc brakes is both complex and time-consuming. Moreover, real vehicle testing conditions are diverse, and there are limitations in data collection, impeding a comprehensive understanding of the enti...

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Bibliographic Details
Published in:Journal of physics. Conference series 2024-08, Vol.2825 (1), p.012016
Main Authors: Han, Jinliang, Ling, He, Sun, Xiao, Zou, Lin
Format: Article
Language:English
Subjects:
Algorithms
Commercial vehicles
Coupling
Disc brakes
Finite element method
Machine learning
Prediction models
Predictions
Thermal simulation
Online Access:Get full text
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Summary:The process of conducting thermal-mechanical coupling finite element analysis for commercial vehicle disc brakes is both complex and time-consuming. Moreover, real vehicle testing conditions are diverse, and there are limitations in data collection, impeding a comprehensive understanding of the entire braking process. A pioneering approach is proposed, blending thermal-mechanical coupling finite element simulation with machine learning algorithms, to construct an innovative model for predicting brake temperatures. Employing advanced machine learning algorithms: Random Forest, XGBoost, and CatBoost, the study successfully developed a highly accurate model for predicting the temporal temperature variations at special nodes. The model was evaluated against four quantitative benchmarks, revealing that the Random Forest-based model stands out in terms of both accuracy and stability.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2825/1/012016