Summary of Analysis Of Centrifugal Clutches in Two-speed Automatic Transmissions with Deep Learning-based Engagement Prediction, by Bo-yi Lin and Kai Chun Lin
Analysis of Centrifugal Clutches in Two-Speed Automatic Transmissions with Deep Learning-Based Engagement Prediction
by Bo-Yi Lin, Kai Chun Lin
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Numerical Analysis (math.NA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a thorough numerical analysis of centrifugal clutch systems integrated with two-speed automatic transmissions, crucial in automotive torque transfer. Centrifugal clutches enable torque transmission based on rotational speed without external controls. The study 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 like spring preload and shoe mass, offering an efficient alternative to complex simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about how to make car transmissions better! It looks at special clutches that help torque move between gears without needing any extra control. The study finds out what happens when you change different parts of the clutch system, like how it affects the transmission’s behavior. A special kind of computer model called a Deep Neural Network helps predict when these clutches will engage, which can make designing them easier and more efficient. |
Keywords
» Artificial intelligence » Neural network
