RESEARCH PAPER
Prediction of torsional characteristics of clutch driven disc assemblies based on machine learning methods
1
Changchun University of Technology, China
2
Zhejiang Qidie Automotive Parts Co., Ltd, China
3
Jilin Zhonglian Testing Machine Manufacturing Co., Ltd, China
Submission date: 2025-07-26
Final revision date: 2025-09-02
Acceptance date: 2025-12-16
Online publication date: 2025-12-26
Publication date: 2025-12-26
HIGHLIGHTS
- Predicting the torsional characteristics of clutch driven disc.
- Incremental learning opti-mization.
- Artificial Neural Networks.
KEYWORDS
TOPICS
ABSTRACT
A method for predicting the torsional characteristics of clutch driven disc assemblies using machine learning algorithms is proposed. Traditionally, torsional stiffness is measured with a dedicated torsional testing machine. In this study, we aim to predict the torsional stiffness of various driven disc models based on historical testing data. Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) models were employed for prediction. The results show that the ANN achieved the highest prediction accuracy, with R2=0.9590 and RMSE = 5.471 N·m/°. Furthermore, after optimization through incremental learning, the performance of the ANN model was significantly improved, achieving R2=0.9891 and RMSE = 3.045 N·m/°. This study substantially reduces the time and cost associated with measuring the torsional stiffness of clutch driven discs and demonstrates the potential of machine learning approaches in traditional mechanical engineering applications.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the financial support from the Jilin Province Science and Technology Development Plan (No. 20220201027GX).
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