航空发动机高压转子装配偏心预测和相位优化的智能算法应用研究

张子豪 1; 郭俊康 1; 洪军 1; 2; 孙岩辉 3

doi:10.7652/xjtuxb202102006

您当前的位置：

首页 >

文章列表页 >

航空发动机高压转子装配偏心预测和相位优化的智能算法应用研究

更新时间：2025-07-09

- 航空发动机高压转子装配偏心预测和相位优化的智能算法应用研究
- Application Study of Intelligent Algorithms for Prediction and Phase Optimization of Assembly Eccentricity of Aero-Engine High Pressure Rotor
- 西安交通大学学报 2021年55卷第2期页码：47-54
- 作者机构：
  
  1. 西安交通大学机械工程学院,西安,710049
  2. 西安交通大学现代设计及转子轴承系统教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2020-06-04。作者简介: 张子豪(1996—),男,硕士生;郭俊康(通信作者),男,助理研究员。基金项目: 航空发动机及燃气轮机重大专项基础研究项目(2017-VII-0010-0105);国家自然科学基金资助项目(51805419);中国博士后科学基金资助项目(2019T120899)
- DOI：10.7652/xjtuxb202102006
  中图分类号： TH161.7
- 网络首发：2021-02-10，
  
  纸质出版：2021
- 稿件说明：
移动端阅览
张子豪 1, 郭俊康 1, 洪军 1, 等. 航空发动机高压转子装配偏心预测和相位优化的智能算法应用研究[J]. 西安交通大学学报, 2021,55(2):47-54.

Application Study of Intelligent Algorithms for Prediction and Phase Optimization of Assembly Eccentricity of Aero-Engine High Pressure Rotor[J]. 2021, 55(2): 47-54.
张子豪 1, 郭俊康 1, 洪军 1, 等. 航空发动机高压转子装配偏心预测和相位优化的智能算法应用研究[J]. 西安交通大学学报, 2021,55(2):47-54. DOI： 10.7652/xjtuxb202102006.

Application Study of Intelligent Algorithms for Prediction and Phase Optimization of Assembly Eccentricity of Aero-Engine High Pressure Rotor[J]. 2021, 55(2): 47-54. DOI： 10.7652/xjtuxb202102006.

摘要

为实现航空发动机高压转子零件快速且精确装配

通过智能算法对转子零件装配偏心进行预测进而进行相位优化。首先

运用前30阶傅里叶级数的方法模拟形貌误差并生成误差数据。其次

添加误差数据到有限元模型中计算出装配偏心。再次

搭建BP人工神经网络模型

提取傅里叶级数的幅值和相位作为神经网络的输入

装配偏心作为网络输出

在神经网络中加入衰减学习率、正则化、滑动平均算法

使计算装配偏心更加精确、稳定

使用200组数据完成神经网络训练

并用训练好的网络对3组测试数据进行验证。最后

分别使用神经网络计算得到的不同装配零件每个相位的偏心作为粒子群算法需要优化的目标

通过误差传递计算

得到优化后的零件装配相位。研究结果表明:使用神经网络模型计算装配偏心可以充分考虑止口形貌特征和装配变形

并明显提高计算效率

再运用粒子群算法对不同相位进行最优选择

达到满足航空发动机高压转子装配同轴度要求

提高服役性能。

Abstract

To achieve rapid and accurate assembly of aero-engine high-pressure rotor parts

we attempt to predict the assembly eccentricity of the rotor parts via intelligent algorithms and then optimize the phase. The first 30 orders of Fourier series are adopted to simulate the shape error and generate error data. Adding the error data to the finite element model to calculate the assembly eccentricity

BP artificial neural network model is established. The amplitude and phase of the Fourier series are extracted as the input of the neural network and the assembly eccentricity as the network output. The attenuation learning rate

regularization

and moving average algorithm participate in the neural network to calculate the assembly eccentricity more accurately and stably. 200 sets of data are used to complete the neural network training and the trained network verifies three sets of test data. The eccentricity of each phase of different assembly parts is calculated with this neural network. Taking the phase as the objective of particle swarm optimization

the optimized assembly phase of the parts is obtained by error transfer calculation. This approach shows that this neural network model fully considers the morphology of the flange and assembly deformation

and significantly improves the calculation efficiency. Then particle swarm optimization algorithm is used to optimally select different phases to meet the requirements of aero-engine rotor assembly and promote service performance.

关键词

Keywords

references

焦华宾, 莫松. 航空涡轮发动机现状及未来发展综述 [J]. 航空制造技术, 2015, 58(12): 62-65.

JIAO Huabin, MO Song. Present status and development trend of aircraft turbine engine [J]. Aeronautical Manufacturing Technology, 2015, 58(12): 62-65.

刘永泉, 王德友, 洪杰, 等. 航空发动机整机振动控制技术分析 [J]. 航空发动机, 2013, 39(5): 1-8, 13.

LIU Yongquan, WANG Deyou, HONG Jie, et al. Analysis of whole aeroengine vibration control technology [J]. Aeroengine, 2013, 39(5): 1-8, 13.

YANG Z, HUSSAIN T, POPOV A A, et al. Novel optimization technique for variation propagation control in an aero-engine assembly [J]. Proceedings of the Institution of Mechanical Engineers: Part B Journal of Engineering Manufacture, 2011, 225(1): 100-111.

YANG Z, MCWILLIAM S, POPOV A A, et al. A probabilistic approach to variation propagation control for straight build in mechanical assembly [J]. The International Journal of Advanced Manufacturing Technology, 2013, 64(5/6/7/8): 1029-1047.

孙传智. 基于矢量投影的多级转子同轴度测量方法研究 [D]. 哈尔滨: 哈尔滨工业大学, 2017: 35-55.

丁司懿, 金隼, 李志敏, 等. 航空发动机转子装配同心度的偏差传递模型与优化 [J]. 上海交通大学学报, 2018, 52(1): 54-62.

DING Siyi, JIN Sun, LI Zhimin, et al. Deviation propagation model and optimization of concentricity for aero-engine rotor assembly [J]. Journal of Shanghai Jiao Tong University, 2018, 52(1): 54-62.

陈华, 唐广辉, 陈志强, 等. 基于雅可比旋量统计法的发动机三维公差分析 [J]. 哈尔滨工程大学学报, 2014, 35(11): 1397-1402.

CHEN Hua, TANG Guanghui, CHEN Zhiqiang, et al. Three-dimensional tolerance analysis of engine based on Jacobian-Torsor statistical model [J]. Journal of Harbin Engineering University, 2014, 35(11): 1397-1402.

单福平. 航空发动机转子结构的装配偏差建模分析与工艺优化 [D]. 上海: 上海交通大学, 2015: 19-28.

单福平, 李志敏, 朱彬. 航空发动机典型转子件装配偏差建模及分析 [J]. 制造业自动化, 2015, 37(7): 100-103.

SHAN Fuping, LI Zhimin, ZHU Bin. Modeling and analysis of assembling deviation of typical aeroengine rotor parts [J]. Manufacturing Automation, 2015, 37(7): 100-103.

PRABHAHARAN G, RAMESH R, ASOKAN P. Concurrent optimization of assembly tolerances for quality with position control using scatter search approach [J]. International Journal of Production Research, 2007, 45(21): 4959-4988.

刘海博. 机械产品几何精度设计中的多目标公差优化技术 [D]. 北京: 北京理工大学, 2015: 35-51.

王巍, 梁涛, 刘中文, 等. 基于BP神经网络的装配容差多目标优化设计 [J]. 中国科技纵横, 2012(16): 27.

WANG Wei, LIANG Tao, LIU Zhongwen, et al. Multi-objective optimal design of assembly tolerance based on BP neural network [J]. China Science Technology Panorama Magazine, 2012(16): 27.

刘超, 刘少岗. 基于粒子群算法的并行公差优化设计模型求解 [J]. 天津科技大学学报, 2013, 28(1): 67-70.

LIU Chao, LIU Shaogang. Solution of concurrent tolerancing optimization design model based on particle swarm optimization algorithm [J]. Journal of Tianjin University of Science Technology, 2013, 28(1): 67-70.

MING X G, MAK K L. Intelligent approaches to tolerance allocation and manufacturing operations selection in process planning [J]. Journal of Materials Processing Technology, 2001, 117(1): 75-83.

张铭鑫, 葛茂根, 张玺, 等. 基于模糊机会约束规划的再制造装配车间调度优化方法 [J]. 中国机械工程, 2015, 26(11): 1488-1493.

ZHANG Mingxin, GE Maogen, ZHANG Xi, et al. Optimization method of remanufacturing assembly shop scheduling based on fuzzy chance-constrained programming [J]. China Mechanical Engineering, 2015, 26(11): 1488-1493.

ZHANG Zihao, GUO Junkang, SUN Yanhui, et al. Eccentricity of rotor prediction of aero-engine rotor based on image identification and machine learning [C]∥ASME 2019 International Mechanical Engineering Congress and Exposition. New York, USA: ASME, 2019: 1-9.

黄介武. 线性与广义线性模型中参数估计的一些研究 [D]. 重庆: 重庆大学, 2014: 32-56.

张欣怡, 袁宏俊. 正则化和交叉验证在组合预测模型中的应用 [J]. 计算机系统应用, 2020, 29(4): 18-23.

ZHANG Xinyi, YUAN Hongjun. Application of regularization and cross-validation in combination forecasting model [J]. Computer Systems Applications, 2020, 29(4): 18-23.

SUN Yanhui, GUO Junkang, HONG Jun, et al. Modeling of rotation accuracy of multi-support rotating machinery considering geometric errors and part deformation [J]. Assembly Automation, 2020, 40(5): 665-673.

FORSLUND A, SODERBAERGg R, LOOF J, et al. Virtual robustness evaluation of turbine structure assemblies using 3D scanner data [C]∥ASME 2011 International Mechanical Engineering Congress and Exposition. New York, USA: ASME, 2012: 157-165.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

采用机器学习的大规模并行程序I/O性能建模与预测方法

采用机器学习的天然气掺氢发动机可预测燃烧模型

机器学习驱动天然气掺氢发动机可预测燃烧模型的开发与验证

利用人工神经网络的偏光下热解炭织构类型识别

混合电解槽阵列协同的风光耦合制氢系统双层多目标优化