Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (04): 161-166.doi: 10.13475/j.fzxb.20190101306

• Machinery & Accessories • Previous Articles     Next Articles

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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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

CLC Number: 

  • TP391

Fig.1

Elastic tube physical (a) and spiral groove analysis model (b)"

Fig.2

Vibration diagram of bottom of elastic tube"

Tab.1

Stress coefficient β2 at different winding ratios"

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

Fig.3

Radial basis neural network model of elastic tube bending stiffness. (a) Approximate model of groove width and elastic modulus with bending stiffness; (b) Approximate model of pitch and wall thickness with bending stiffness"

Fig.4

Radial-based neural network model of bottom of elastic tube. (a) Approximate model of groove width and elastic modulus with bottom amplitude; (b) Approximate model of pitch and wall thickness with bottom amplitude"

Fig.5

Comparison of actual simulation value of stiffness Jeq and approximate model prediction value"

Fig.6

Comparison of actual simulation value of amplitude A and approximate model prediction value"

Tab.2

Average relative error and R2 value error test"

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

Fig.7

Pareto front of bending stiffness Jeq and amplitude A"

Tab.3

Comparison before and after optimization of elastic pipe process parameters"

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

Fig.8

Variation of bottom amplitude before and after optimization of elastic tube"

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