Applying data mining approaches for defect diagnosis in manufacturing industry

Tzu Liang Tseng, M. C. Jothishankar, Teresa Wu, Guangming Xing, Fuhua Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

This study presents a new heuristic algorithm for attribute reduction (reduct) selection in the Rough Set Theory (RST) applications, called extended RST. This algorithm is able to derive the rules and identify the most significant features simultaneously, which is unique and useful in solving quality control problems in manufacturing. The developed algorithm is applied to an industrial case study involving the quality control of Printed Circuit Boards (PCBs). The rules derived from the data set provide an indication of how to study this problem further and create a path for effective further investigation.

Original languageEnglish (US)
Title of host publicationIIE Annual Conference and Exhibition 2004
Pages1441-1447
Number of pages7
StatePublished - 2004
EventIIE Annual Conference and Exhibition 2004 - Houston, TX, United States
Duration: May 15 2004May 19 2004

Other

OtherIIE Annual Conference and Exhibition 2004
Country/TerritoryUnited States
CityHouston, TX
Period5/15/045/19/04

Keywords

  • Data mining
  • Defect diagnosis
  • Manufacturing
  • PCB assembly
  • Rough set theory

ASJC Scopus subject areas

  • Engineering(all)

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