Advertisement

Development of Automated Data Mining System for Quality Control in Manufacturing

  • Hideyuki Maki
  • Yuko Teranishi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2114)

Abstract

The production process in manufacturing has recently become highly complex. Therefore, it is difficult to solve problems in a process, by only using techniques that depend on the knowledge and know-how of engineers. Knowledge discovery in databases (KDD) techniques are supposed to assist engineers in extracting the non-trivial characteristics of a production process that are beyond their knowledge and know-how. However, the KDD process is basically a user-driven task and such a user-driven manner is not efficient enough for use in a manufacturing application. We developed an automated data-mining system designed for quality control in manufacturing. It has three features; periodical-analysis, storing the result and extracting temporal-variances of the result. We applied it to liquid crystal display fabrication and found that the data-mining system is useful for the rapid recovery from problems of the production process.

Keywords

Liquid Crystal Display Local Database Rule Extraction Irregular Variance Implicit Causality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: Knowledge Discovery and Data Mining: Towards a Unifying Framework. Proc. KDD-96, AAAI Press (1996) 82–88Google Scholar
  2. 2.
    Piatetsky-Shapiro, G., Brachman, R., Khabaza, T.: An Overview of Issues in Developing Industrial Data Mining and Knowledge Discovery Applications. Proc. KDD-96 AAAI Press (1996) 89–95Google Scholar
  3. 3.
    Maeda, A., Maki, H., Akimori, H.: Characteristic Rule Induction Algorithm for Data Mining. Proc. PAKDD-98 (1998) 399–400Google Scholar
  4. 4.
    Ashida, H., Morita, T.: Architecture of Data Mining Server: DATAFRONT/Server. Proc. SCM-99 IEEE (1999) 882–887Google Scholar
  5. 5.
    Maki, H., Maeda, A., Akimori, H.: Data Mining Application to LSI Fault Analysis. Proc. ICEE-98, The Korean Institute of Electrical Engineers (1998) 417–420Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Hideyuki Maki
    • 1
  • Yuko Teranishi
    • 1
  1. 1.Systems Development Laboratory, Hitachi, Ltd.Kanagawa-kenJapan

Personalised recommendations