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Approximate Stiffness Modelling and Stiffness Defect Identification for a Heavy-load Parallel Manipulator

Published online by Cambridge University Press:  18 February 2019

Shuai Fan
Affiliation:
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, People’s Republic of China. E-mail: fansuai12345@163.com
Shouwen Fan*
Affiliation:
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, People’s Republic of China. E-mail: fansuai12345@163.com
*
*Corresponding author. E-mail: shouwenfan@263.net

Summary

When using parallel manipulators as machine tools, their stiffness is an important factor in the quality of the produced products. This paper presents an overall approximate stiffness model for a heavy-load parallel manipulator, which considers the effects of actuator stiffness, joint clearance, joint contact deformation, and limb deformation. Based on the principle of virtual work and the introduced modified parameters, the proposed overall compliance matrix successfully takes four factors into a unified expression. To obtain the overall compliance matrix, the approximate stiffness models of the joint clearance, joint contact deformation, and limb deformation are given. In addition, by combining the statistical simulation including the random uncertainties and the proposed approximate stiffness models as the basis of the magnitudes for each random variable, an approach based on the expected trajectory and external load is also proposed for stiffness defect identification such that the estimation is more accurate and reliable. Finally, a numerical example of the 1PU+3UPS parallel manipulator and a discussion are presented to demonstrate the practicability of the proposed stiffness model and defect identification approach. After modifying the structure parameters of the defective components, the prototype experiences a significant stiffness improvement.

Type
Articles
Copyright
Copyright © Cambridge University Press 2019 

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