纺织学报 ›› 2020, Vol. 41 ›› Issue (04): 161-166.doi: 10.13475/j.fzxb.20190101306

• 机械与器材 • 上一篇    下一篇

基于神经网络和遗传算法的锭子弹性管性能优化

莫帅1,2(), 冯战勇1,2, 唐文杰1,2, 党合玉1,2, 邹振兴1,2   

  1. 1.天津工业大学 机械工程学院, 天津 300387
    2.天津工业大学 天津市现代机电装备技术重点实验室, 天津 300387
  • 收稿日期:2019-01-08 修回日期:2020-01-12 出版日期:2020-04-15 发布日期:2020-04-27
  • 作者简介:莫帅(1987—),男,副教授。主要研究方向为现代纺织装备。E-mail: moshuai2010@163.com。
  • 基金资助:
    国家自然科学基金项目(51805368);中国纺织工业联合会应用基础研究项目(J201806);中国科协青年人才托举工程项目(2018QNRC001);天津市自然科学基金项目(17JCQNJC04300)

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

MO Shuai1,2(), FENG Zhanyong1,2, TANG Wenjie1,2, DANG Heyu1,2, ZOU Zhenxing1,2   

  1. 1. School of Mechanical Engineering, Tiangong University, Tianjin 300387, China
    2. Tianjin Key Laboratory of Advanced Mechatronics Equipment Technology, Tiangong University, Tianjin 300387, China
  • Received:2019-01-08 Revised:2020-01-12 Online:2020-04-15 Published:2020-04-27

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摘要：

为得到减振弹性管对下锭胆的支承弹性和锭子高速运动下的稳定性等性能的最优匹配效率,依据减振弹性管的等效抗弯刚度及底部等效刚度系数公式,利用MatLab数值分析软件构建弹性管抗弯刚度和底部挠度数学模型。首先,结合Isight优化软件基于径向基神经网络构建其近似模型,且使精度达到可接受水平,并以模型的关键结构参数弹性模量、螺距、槽宽、壁厚为设计变量,结合遗传算法对弹性管抗弯刚度和底部挠度进行多目标优化设计,得到Pareto最优解集和Pareto前沿图,确定出减振弹性管结构工艺参数的优化方案。通过对优化数据进行分析发现,该方案在保证减振弹性管弹性的同时,其底部振幅明显减弱。

关键词: 减振弹性管, 径向基神经网络, 遗传算法, 多目标优化, 锭子

Abstract:

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.

Key words: damping elastic tube, radial based neural network, genetic algorithm, multi-objective optimization, spindle

中图分类号: 

  • TP391

图1

弹性管实物及螺旋槽分析模型"

图2

弹性管底端振动示意图"

表1

不同卷绕比下应力系数β2值"

Φ 3.0 3.5 4.0 5.0
1.0 3.52 3.35 3.20 3.03
1.2 3.63 3.42 3.31 3.12
1.4 3.72 3.55 3.42 3.25
1.6 3.81 3.65 3.54 3.34
1.8 3.94 3.75 3.65 3.45
2.0 4.00 3.85 3.75 3.56
2.2 4.15 3.95 3.83 3.66
2.4 4.21 4.04 3.90 3.74
2.5 4.25 4.10 3.94 3.79

图3

弹性管抗弯刚度径向基神经网络模型"

图4

弹性管底部振幅径向基神经网络模型"

图5

刚度Jeq实际仿真值和近似模型预测值拟合对比"

图6

振幅A实际仿真值和近似模型预测值拟合对比"

表2

平均相对误差和R2值误差检验"

目标值 平均相对误差 R2值
可接受水平 0.2 0.9
抗弯刚度Jeq 1.731 6×10-4 1
底部振幅A 0.015 87 0.990 67

图7

抗弯刚度Jeq和底部振幅A的Pareto前沿图"

表3

优化前后弹性管工艺参数对比"

状况 设计变量 目标响应
c/mm E/GPa h/mm p/mm Jeq/(N·m2) A/mm
优化前 1.00 207.0 1.850 9.00 0.46 0.388 7
优化后 1.51 208.2 1.602 9.53 0.52 0.119 9

图8

弹性管优化前后底部振幅的变化"

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