DESIGN, FABRICATION, AND PREDICTIVE MODEL OF A 1-DOF TRANSLATIONAL FLEXIBLE BEARING FOR HIGH PRECISION MECHANISM

Abstract

Flexible bearing is significantly associated with high precision manipulators, actuators, and positioning stages. In this paper, a flexible bearing is designed for such applications. The life of a flexible bearing is very sensitively influenced by the stress concentration. The Taguchi method is applied to find the best combination of design variables to reduce the stress concentration. Multivariable linear regression (MLR) is established to model the relationship between the design variables and the stress response. In addition, to enhance the predictive efficiency for predicting, a radial basic function (RBF) neural network is used for this relationship. The effectiveness of all models is compared using statistical methods. It is evident that the relationship derived from RBF neural network is more accurate than that derived from MLR models. The confirmation experiments are conducted to verify the predicted results. The combined methodology in this paper is likely be used for various practical applications.