沈斌分享 http://blog.sciencenet.cn/u/bshen 同济大学中德学院机械工程系主任、制造执行系统(MES)分会理事长

博文

基于数据挖掘技术的MES质量管理的研究

已有 5471 次阅读 2008-6-15 13:07 |个人分类:硕士研究生毕业论文|关键词:学者

硕士研究生学位论文

硕士研究生:潘多泰

指导教师:沈斌 教授

答辩时间:2008.06.15

摘要

随着制造业信息化建设步伐的逐步加快,越来越多的制造企业通过实施ERP/MES/PCS的三层信息集成结构来实现企业的信息化战略。MES系统在计划管理层与底层控制层之间建立起了信息互通的桥梁。产品质量是企业的生命线,如何利用信息集成结构最大限度地提高产品的质量,一直是MES关注的重点。

本文紧紧围绕MES环境下的质量管理功能展开研究,将产品质量管理和先进的信息处理方法——数据挖掘技术有机结合起来,充分利用MES系统中大量的信息和数据,提取出其中有利于提高生产质量、降低产品不合格率的潜在信息,帮助企业有效地进行决策。

在研究内容方面,本文首先分析了在MES系统下实施产品质量数据挖掘的可行性,得出MES为产品质量数据挖掘提供了很好的挖掘平台;然后在简要介绍产品质量管理和数据挖掘的基本概念的基础上,针对某一实际企业建立了数据挖掘数据集市模型;随后分别从三个方面展开研究并取得成功:将决策树方法应用于产品质量的分析当中;将回归预测方法应用于刀具耐用度的分析当中;将多维关联规则方法应用到供应商的质量管理当中;最后文章利用B/S结构的Web开发技术,建立了小型的示例产品质量数据挖掘平台。

将数据挖掘技术应用到MES系统的质量管理功能当中,能够解决传统质量管理存在的几个问题:质量信息采集和处理方法落后、质量信息流并不畅通、缺乏有效的支持系统与工具,是质量管理领域的一个重要研究方向。

 

关键字:质量管理,MES,数据挖掘,决策树,回归预测,关联规则,信息化


ABSTRACT

In order to keep up with the increasing development of the informatization technology, more and more manufacturing enterprises achieve their information strategy through implementation of the ERP/MES/PCS structure. The MES build up a communication bridge between the management planning level and the process control level. The quality of products is the lifeline of enterprises. How to improve the quality with help of information integrating structure? It is always one of  focuses of MES.

This article focus on the quality management function in MES environment closely, combines quality management methods and data processing methods – data mining - together, takes advantage of the rich information and data in MES system, extracts the useful rules which can improve production quality and lower the ratio of defect products, to help the decision making of enterprises.

Within the scope of research, this article analyzes the feasibility of quality data mining in environment of MES system first and get aware that MES provides a good platform for quality data mining. Then gives a brief introduction of the basic concepts of quality management and data mining and builds a data set for mining. Then begins research in three fields: applying decision tree in analyzing of product quality, applying linear regression in tool life prediction, applying multi-dimension association rule in supplier quality management. And finally, build up a small example quality data mining platform with B/S structure using Web technology.

The application of data mining in MES quality management function can solve the traditional problems: outdated methods of information collecting and processing, unsmoothed communication of quality information, lack of effective support system and tools. It is an important research direction in field of quality management.

 

Key Words: Quality Management, Data Mining, MES, Decision Tree, Regression Prediction, Association Rule, Informatization

 



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