Performance optimization of elastic spindle pipe based on neural network and genetic algorithm

被引：0
|
作者：
Mo S. [1 ,2 ]
Feng Z. [1 ,2 ]
Tang W. [1 ,2 ]
Dang H. [1 ,2 ]
Zou Z. [1 ,2 ]
机构：
[1] School of Mechanical Engineering, Tiangong University, Tianjin
[2] Tianjin Key Laboratory of Advanced Mechatronics Equipment Technology, Tiangong University, Tianjin
来源：
Fangzhi Xuebao/Journal of Textile Research | 2020年 / 41卷 / 04期
关键词：
Damping elastic tube; Genetic algorithm; Multi-objective optimization; Radial based neural network; Spindle;
D O I：
10.13475/j.fzxb.20190101306
中图分类号：
学科分类号：
摘要：
The aim of this research is to improve the matching efficiency of the damping elastic tube to the support elasticity of the lower spindle and the stability of the spindle at high speeds. Using the formula of the damping equivalent bending stiffness and the equivalent stiffness coefficient of the bottom of the damping elastic tube, a mathematical model of the bending stiffness and the bottom deflection of the elastic tube was established and calculated using MatLab numerical analysis software. The approximate model of the elastic tube based on radial basis function neural network was combined with Isight optimization software attempting to increase the accuracy to an acceptable level. Taking the elastic modulus, pitch, slot width and wall thickness as the design variables, the multi-objective optimization design of the bending stiffness and bottom deflection of the elastic tube was combined with the genetic algorithm to obtain the Pareto optimal solution set and Pareto front map, leading to the determination of the vibration-damping elastic tube structure. The research results show that the vibration reduction of the elastic tube resulting in improved elastic performance, with a much reduced vibration amplitude at the base of the tube. Copyright No content may be reproduced or abridged without authorization.
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页码：161 / 166
页数：5
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