• CN:11-2187/TH
  • ISSN:0577-6686

›› 2005, Vol. 41 ›› Issue (11): 20-25.

• 论文 • 上一篇    下一篇

基于数据挖掘的产品质量控制建模方法

方喜峰; 赵良才;吴洪涛   

  1. 江苏科技大学机械与动力工程学院;南京航空航天大学机电工程学院
  • 发布日期:2005-11-15

MODELING METHOD OF PRODUCT QUALITY CONTROL BASED ON DATA MINING

Fang Xifeng; Zhao Liangcai;Wu Hongtao   

  1. School of Mechanical and Power Engineering, Jiangsu University of Science and Technology College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics
  • Published:2005-11-15

摘要: 在产品质量控制过程中,按统计学观点产品已发生的故障与元器件检测筛选之间存在某种内在的关系。为了有效地发现这些关联规则,建立了质量信息挖掘模型,它包括质量灰色预测挖掘算法和质量灰色关联—关联规则挖掘算法,目标是在质量信息数据库中发现所有置信度大于预定阈值的规则及预测产品质量将来发展趋势。按此模型,建立了面向产品质量信息生成的数据仓库和数据集市,设计了质量灰色预测挖掘模型和质量灰色关联—关联规则数据挖掘流程,并用电子元器件老炼筛选和故障模式归类规律两个算例,验证了算法的合理性。

关键词: 关联规则, 灰色理论, 计算机辅助质量控制, 数据挖掘, 质量信息库

Abstract: In the process of quality control, certain interrelations exist between product faults and the inspection and selection of the related parts based on statistics theory. The data mining model is established in order to discover these interrelated rules, it includes quality forecast algorithm and quality association- association rule algorithm. The goal is to find rules in which believability is larger than the presetting value and to forecast the situation of the product quality in the future. The data mining tool is developed and the data warehouse and data market for generating the product quality information are constructed, and the discovering flows of predicting quality and finding interrelated rules based on data mining and grey theory are also designed. Finally, two examples of forecasting the situation of selection and inspection of electronic parts and finding classification of the fault modes are presented, and the algorithms’ availability and efficiency are verified.

Key words: Data mining, Association rule, Computer aided quality control(CAQC), Grey theory, Quality information database

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