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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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Published in: | Journal of physics. Conference series 2024-08, Vol.2825 (1), p.012016 |
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creator | Han, Jinliang Ling, He Sun, Xiao Zou, Lin |
description | 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. |
doi_str_mv | 10.1088/1742-6596/2825/1/012016 |
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subjects | Algorithms Commercial vehicles Coupling Disc brakes Finite element method Machine learning Prediction models Predictions Thermal simulation |
title | Commercial vehicle disc brake temperature prediction model construction based on machine learning |
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