基于神经网络优化的正交试验内齿轮齿面偏载矫正研究

王振博; 郑鹏; 刘逸飞

doi:10.16578/j.issn.1004.2539.2023.08.003

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基于神经网络优化的正交试验内齿轮齿面偏载矫正研究

理论研究 | 更新时间：2023-08-22

- 基于神经网络优化的正交试验内齿轮齿面偏载矫正研究
- Research on Correcting Eccentric Load of the Internal Gear Tooth Surface by Orthogonal Tests Based on Neural Network Optimization
- 机械传动 2023年47卷第8期页码：16-23
- 作者机构：
  
  沈阳工业大学机械工程学院，辽宁沈阳 110870
- 作者简介：
  
  王振博(1992— )，男，辽宁沈阳人，博士；主要研究方向为机械传动及动压润滑；wangzhenbo2021123@163.com。
  郑鹏（1964— ），男，辽宁沈阳人，博士，教授；主要研究方向为机械传动；zptougao2021@163.com。
- 基金信息：
  
  中国科学院战略性先导科技专项(XDC04030503)
- DOI：10.16578/j.issn.1004.2539.2023.08.003
  中图分类号：
- 纸质出版日期：2023-08-15，
  
  收稿日期：2022-05-29，
  
  修回日期：2022-06-23，
扫描看全文
王振博,郑鹏,刘逸飞.基于神经网络优化的正交试验内齿轮齿面偏载矫正研究[J].机械传动,2023,47(08):16-23.

Wang Zhenbo,Zheng Peng,Liu Yifei.Research on Correcting Eccentric Load of the Internal Gear Tooth Surface by Orthogonal Tests Based on Neural Network Optimization[J].Journal of Mechanical Transmission,2023,47(08):16-23.
王振博,郑鹏,刘逸飞.基于神经网络优化的正交试验内齿轮齿面偏载矫正研究[J].机械传动,2023,47(08):16-23. DOI： 10.16578/j.issn.1004.2539.2023.08.003.

Wang Zhenbo,Zheng Peng,Liu Yifei.Research on Correcting Eccentric Load of the Internal Gear Tooth Surface by Orthogonal Tests Based on Neural Network Optimization[J].Journal of Mechanical Transmission,2023,47(08):16-23. DOI： 10.16578/j.issn.1004.2539.2023.08.003.

摘要

为解决盾构机大型内啮合齿轮工作时由轴偏角造成的齿面偏载问题，给出了综合修形齿面的齿面方程，提出了采用神经网络联合正交试验的方法对齿面修形参数进行筛选预测。该种方法不再将齿廓修形和螺旋线修形看作相互独立的调整方法，能够将齿面偏载转变为均匀分布在齿面上的载荷。通过有限元方法对修形后的齿轮进行接触分析发现，该方法能够有效降低因轴偏角造成的齿面局部应力，并减小了传动误差；可以一次性找到较优的齿面修形参数值，避免了对大型盾构机内啮合齿轮进行啮合试验成本过高等问题。

Abstract

In order to solve the problem of tooth surface eccentric load caused by shaft deflection angle of large internal meshing gears of the shield machine

the tooth surface equation of comprehensively modified tooth surface is given

and the method of neural network combined with orthogonal test is proposed to screen and predict the tooth surface modification parameters. In this method

the tooth profile modification and tooth direction modification are no longer regarded as independent variables

and the eccentric load on the tooth surface can be transformed into the load evenly distributed on the tooth surface. Through the contact analysis of the modified gear by the finite element method

it is found that this method can effectively reduce the excessive local stress on the tooth surface caused by the shaft deflection angle and reduce the transmission error. The optimal tooth surface modification parameter value can be found at one time

which avoids the high cost of meshing experiment for the internal meshing gear of large shield machines.

关键词

齿轮修形正交试验神经网络大型内齿轮

Keywords

Gear modificationOrthogonal testNeural networkLarge internal gear

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