基于动态Bayesian网络的叶片加工质量监控与溯源

王佩, 张定华, 陈冰, 李山, 王明微

doi:CNKI:11-1929/V.20111107.1019.001

航空学报 >

2012 , Vol. 33 >Issue 1: 170 - 181

DOI: https://doi.org/CNKI:11-1929/V.20111107.1019.001

材料工程与机械制造

基于动态Bayesian网络的叶片加工质量监控与溯源

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西北工业大学现代设计与集成制造技术教育部重点实验室, 陕西西安 710072

王佩女,博士研究生.主要研究方向: CAD/CAM技术,集成制造技术,质量工程,制造业信息化技术. Tel: 029-88493232-415 E-mail: abinghoney@163.com
张定华男,博士,教授,博士生导师.主要研究方向: CAD/CAM技术,集成制造技术,计算机图形图像. Tel: 029-88493009 E-mail: dhzhang@nwpu.edu.cn

收稿日期: 2011-05-06

修回日期: 2010-08-22

网络出版日期: 2012-01-16

基金资助

国家自然科学基金(70931004)

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Machining Quality Monitoring of Blades and Source Tracing Based on Dynamic Bayesian Network

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Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi'an 710072, China

Received date: 2011-05-06

Revised date: 2010-08-22

Online published: 2012-01-16

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摘要

针对叶片加工过程中质量精度不高的问题,提出了基于动态Bayesian网络的叶片加工质量监控与溯源方法.利用动态Bayesian网络建立起叶片加工工序间的相互联系,实现对整个加工过程的控制.基于Bayesian网络对影响加工工序的因素集建立因果联系,采用多元统计过程控制中的T²控制图完成对各工序影响因素集的监控.进行误差溯源时,根据Bayesian网络建立的因果关系对失控样本的T²统计量依据原因变量进行误差分解,并构建各分解变量的控制限,将其作为误差源判定的条件.通过对某叶片加工过程的仿真,验证了所提方法的有效性.

关键词： 航空发动机; 叶片加工; 质量监控; 误差溯源; 动态Bayesian网络; 误差分解

本文引用格式

王佩, 张定华, 陈冰, 李山, 王明微 . 基于动态Bayesian网络的叶片加工质量监控与溯源[J]. 航空学报, 2012 , 33(1) : 170 -181 . DOI: CNKI:11-1929/V.20111107.1019.001

Abstract

A methodology of monitoring the machining quality of blades and tracing the error source based on dynamic Bayesian network is proposed for solving the low accuracy of blade machining quality. Dynamic Bayesian network is used to establish the relationship between blade machining operations to realize the control of the whole machining process. The causal relation between the elements in the main process factor set that affects blade machining operations is built by Bayesian network. The control chart T² is used to monitor the factor set of each operation to judge whether the operation is out of control or not. While tracing error sources, the T² statistics of the samples out of control are decomposed according to causal variables described by aforementioned causal relation, and the decomposed variable control limits are built as error source judgment conditions are built. A simulation study on a blade machining process is carried out, which demonstrates that the proposed method is reasonable.

Key words： aeroengine; blade machining; quality monitoring; error source tracing; dynamic Bayesian network; error decomposition

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