Hybrid uncertainties-based analysis and optimization methods for axial friction force of drive-shaft systems

Abstract

The axial friction force (AFF) of automotive drive-shaft systems is an important nonlinear dynamic characteristic, which will directly cause the vibration and noise of vehicles. During the operation of drive-shaft systems, the AFF has great uncertainty. Taking a drive-shaft system as the research object, a hybrid random and interval uncertainties (HRIU) model of the AFF is proposed to study the AFF more effectively. In the HRIU model, the articulation angle and the pitch circle radius of tripod joints are regarded as random variables, while the input torque, the shaft angular position and the friction coefficient are regarded as interval variables. To enhance the efficiency and accuracy of the analysis and optimization, a novel method called as the perturbation-vertex method is proposed to calculate the responses of the HRIU model. Due to complex responses of the HRIU model, in order to optimize the AFF more effectively, a reliability-based optimization method for the AFF under the HRIU is proposed, and the lower bound of the AFF reliability is taken as the optimization objective to determine the best design parameters of drive-shaft systems. A test bench for measuring the AFF is subsequently established together with the model verification, as well the analysis and optimization of the AFF with the HRIU are performed through numerical examples. The results suggest that the proposed perturbation-vertex method is very suitable for the analysis and optimization with the HRIU, as well the influence of the HRIU on the AFF cannot be ignored when analyzing and optimizing the AFF.