多源多工序加工过程波动扩散网络建模与波动源辨识

娄洪; 陈琨; 李兴炜; 李丽丽; 张嘉锟; 于慧

doi:10.7652/xjtuxb202102009

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多源多工序加工过程波动扩散网络建模与波动源辨识

更新时间：2025-07-09

- 多源多工序加工过程波动扩散网络建模与波动源辨识
- Fluctuation Diffusion Network Modeling and Fluctuation Source Identification of Multi-Fluctuation-Source and Multi-Process Machining Processes
- 西安交通大学学报 2021年55卷第2期页码：73-83
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2020-07-22。作者简介: 娄洪(1995—),男,硕士生,陈琨(通信作者),男,副教授。基金项目: 国家重点研发计划资助项目(2019YFB1703800)
- DOI：10.7652/xjtuxb202102009
  中图分类号： TH166
- 网络首发：2021-02-10，
  
  纸质出版：2021
- 稿件说明：
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娄洪, 陈琨, 李兴炜, 等. 多源多工序加工过程波动扩散网络建模与波动源辨识[J]. 西安交通大学学报, 2021,55(2):73-83.

Fluctuation Diffusion Network Modeling and Fluctuation Source Identification of Multi-Fluctuation-Source and Multi-Process Machining Processes[J]. 2021, 55(2): 73-83.
娄洪, 陈琨, 李兴炜, 等. 多源多工序加工过程波动扩散网络建模与波动源辨识[J]. 西安交通大学学报, 2021,55(2):73-83. DOI： 10.7652/xjtuxb202102009.

Fluctuation Diffusion Network Modeling and Fluctuation Source Identification of Multi-Fluctuation-Source and Multi-Process Machining Processes[J]. 2021, 55(2): 73-83. DOI： 10.7652/xjtuxb202102009.

摘要

为研究多源多工序加工过程波动扩散的特点

针对多源多工序加工过程质量演变规律的多源性与非线性

基于对加工过程波动传递原理的分析

构建以波动贡献度为基础的网络加权方法。利用复杂网络建模理论

建立面向多源多工序加工过程的波动扩散加权网络模型

通过采用网络特性分析手段和加权半局部中心性节点重要度排序算法

实现网络关键节点识别

提出基于广度优先搜索(BFS)策略的波动扩散路径搜索方法

实现针对关键节点的波动源辨识。最后

以汽轮机叶片加工过程为研究对象

对叶片加工质量波动扩散过程进行建模分析

通过分析得到粗铣叶根进汽侧平面、重打顶针孔、精铣叶根型线、粗铣叶根下平面等关键加工工序

其中以重打顶针孔工序为例

进行基于BFS算法的波动扩散路径的求解

搜索结果显示钻床、钻头设备的状态波动水平以及磨叶根出汽侧平面定位基准的加工精度对该工序加工质量将产生直接显著的影响

属于需要重点监控的质量波动源

同时对通过波动扩散路径进行波动传递的其他重要波动源也需要进行质量控制

将有助于进一步提升关键特征——顶针孔特征的加工质量。以上分析辨识的重要波动源和扩散路径符合实际加工过程和工况

表明波动源辨识方案的有效性。

Abstract

To investigate the characteristics of the fluctuation and diffusion of multi-fluctuation-source and multi-process machining

the features of multi-source and nonlinearity of the quality evolution law are paid attention to. The fluctuation diffusion principle of the machining process is analyzed and a network weighting method is proposed following the complex network modeling theory. Then a weighted fluctuation diffusion network model for multi-fluctuation-source and multi-process machining is constructed. The key nodes of the network are identified with the network characteristic analysis method and the weighted semi-local centrality node importance ranking algorithm. A fluctuation diffusion path search scheme based on the breadth first search(BFS)strategy is proposed to realize the identification of the fluctuation source for the key nodes. Finnally

taking the machining process of steam turbine blades as the research object

the process of blade machining quality fluctuation and diffusion is modeled and analyzed. Through the analysis

the rough milling of blade root inlet steam side plane

re-punching of pinhole

fine milling of blade root profile

and rough milling of blade root are key processing procedures

and the re-punching pinhole procedure is taken as an example to solve the fluctuation diffusion path based on the BFS algorithm. The search results show that the state fluctuation levels of the drilling machine and the drill bit equipment

as well as the machining accuracy of the plane positioning benchmark on the steam outlet side of the blade

will have a direct and significant impact on the processing quality of the process

which is a source of quality fluctuations that need to be monitored. At the same time

other important sources of fluctuations that pass through the fluctuation diffusion path also need to be controlled

which will help further improve the processing quality of the key feature - the pinhole feature. The above analysis and identification of important fluctuation sources and diffusion paths are in line with the actual processing and working conditions

indicating the effectiveness of the fluctuation source identification scheme.

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