Abstract
The elastic composite cylindrical roller bearing is a new type of bearing designed with polytetrafluoroethylene filling in a deep hole hollow roller. This design can improve the stress concentration and also performance of the bearing. A study was conducted to parameterize the deep hole structure to obtain its design variables, the angle a and radius c by studying the influence of the design on stress concentration in elastic composite cylindrical roller bearing edge; to determine the BP neural network sample data using orthogonal test and finite element methods; to establish mapping relationship between the design variables and the maximum stress by BP neural network learning algorithm and to obtain the objective function required for structural optimization of genetic algorithm. The study attempts to optimize, search and calculate the most fitting structural parameters of elastic composite cylindrical roller by genetic algorithm with NU318E bearing as the optimization object and obtain the values of optimal design variables as a = 48.68° and c=9.67mm. The work also includes minimization of the comprehensive value of the maximum contact stress and the maximum equivalent stress of the edge and elliptic zones of elastic composite cylindrical roller after optimizing and increasing the contact fatigue service and bearing capacity of the bearing. The optimization procedure and method presented in this paper can serve as a useful reference to the optimization of structure for other elastic composite cylindrical roller edges.
Highlights
Roller bearings are widely used in various types of rotary machine systems
A study was conducted to parameterize the deep hole structure to obtain its design variables, the angle a and radius c by studying the influence of the design on stress concentration in elastic composite cylindrical roller bearing edge; to determine the BP neural network sample data using orthogonal test and finite element methods; to establish mapping relationship between the design variables and the maximum stress by BP neural network learning algorithm and to obtain the objective function required for structural optimization of genetic algorithm
The study attempts to optimize, search and calculate the most fitting structural parameters of elastic composite cylindrical roller by genetic algorithm with NU318E bearing as the optimization object and obtain the values of optimal design variables as a = 48.68° and c=9.67mm
Summary
Roller bearings are widely used in various types of rotary machine systems. The analysis of mechanical characteristics is the basis for research on service life of bearings. The straight bus cylindrical roller bearing has edge stress concentrated at two ends of roller, and it is called as “edge effect”. It is the “edge effect” that causes the breakage of the roller bearing at two ends of the roller and both sides of the raceway. The scholars in industry researched the “edge effect” problem of the roller bearings mainly for bus patching. Paul proposed a simple discretization solution method and constituted a linear equation set and used them for calculating contact stress [2, 3] with convexo cylinder by unknown contact stress by citing influence coefficient method during the structural analysis. J. Hartnett [6] analyzed the dimension and distribution of the edge stress of the roller bearing. Various types of the modified cylinder body were analyzed and log practice was considered as the best approach to
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