Analysis of Centrifugal Clutches in Two-Speed Automatic Transmissions with Deep Learning-Based Engagement Prediction
This work addresses optimization of clutch designs for automotive torque transfer, but it is incremental as it applies existing deep learning methods to a specific domain problem.
The paper tackled the analysis of centrifugal clutch systems in two-speed automatic transmissions by examining various configurations' effects on dynamics and using a Deep Neural Network to predict clutch engagement, offering an efficient alternative to complex simulations.
This paper presents a comprehensive numerical analysis of centrifugal clutch systems integrated with a two-speed automatic transmission, a key component in automotive torque transfer. Centrifugal clutches enable torque transmission based on rotational speed without external controls. The study systematically examines various clutch configurations effects on transmission dynamics, focusing on torque transfer, upshifting, and downshifting behaviors under different conditions. A Deep Neural Network (DNN) model predicts clutch engagement using parameters such as spring preload and shoe mass, offering an efficient alternative to complex simulations. The integration of deep learning and numerical modeling provides critical insights for optimizing clutch designs, enhancing transmission performance and efficiency.