Modeling of Dry Clutch Wear for a Wide Range of Operating Parameters

The paper presents an experimentally validated regression model for dry clutch friction lining wear, accounting for the influence of clutch temperature, initial slip speed, torque, and closing time. The experimental data have been collected by using a custom-designed disk-on-disk computer-controlled tribometer and conducting repetitive real operation-like clutch closing cycles for different levels of the above operating parameters. The model is designed to be cycle-wise, predicting cumulative worn volume expectation and standard deviation after each closing cycle. It is organized around three distinctive submodels, which provide predictions of: (i) wear rate expectation, (ii) wear rate variance, and (iii) elevated wear rate during run-in operation. Finally, the wear rate expectation and variance submodels and the overall, cumulative worn volume model are validated on independent experimental datasets. The main novelty of the presented research lies in the development of stochastic multi-input cycle-wise dry cutch wear model for clutch design and monitoring applications.