Structure Design and Optimization of Deep Cavity Rollers of Rotary Steering Spindle System

Abstract

The center shaft of rotary steering spindle system is bendable under bias force. A severe partial load effect occurs among rollers, the inside and outside circles of the first cantilever bearing. Simulation analysis was conducted by loading boundary condition of the spindle under bias force. Furthermore, three different types of deep cavity rollers, which were cylindrical, conical, and spherical, respectively, were analyzed by finite element method. The effects of deep cavity angles, radius, and offset on mechanical properties of bearing were studied. The data obtained by simulation analysis were trained and predicted by Back Propagation (BP) neural network, and then the BP neural network model was incorporated into fmincon function. Thereby, structure optimization of rollers was established based on BP neural network model and fmincon function. The results show that structure of the conical deep cavity roller gets optimal mechanical performance. After being optimized, maximum stress of edge region and elliptical area decreases, respectively, by 22 and 17% than before, indicating that structure optimization method of the neural network and fmincon function can be used in optimization of deep cavity rollers. This method can quickly search for the optimal solution with sufficient engineering accuracy, ease of use, and adaptability.