Analysis of Centrifugal Clutches in Two-Speed Automatic Transmissions with Deep Learning-Based Engagement Prediction

TL;DR

This paper combines numerical analysis and deep learning to study centrifugal clutch systems in two-speed automatic transmissions, aiming to optimize design and improve transmission performance.

Contribution

It introduces a deep neural network model to predict clutch engagement, integrating it with numerical analysis for better transmission system optimization.

Findings

DNN accurately predicts clutch engagement based on design parameters.

Numerical analysis reveals effects of clutch configurations on transmission dynamics.

Deep learning enhances design efficiency and transmission performance insights.

Abstract

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.