Precise and accurate job cycle time forecasting in a wafer fabrication factory with a fuzzy data mining approach

Tin-Chih Chen*, Richard Romanowski

*Corresponding author for this work

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Many data mining methods have been proposed to improve the precision and accuracy of job cycle time forecasts for wafer fabrication factories. This study presents a fuzzy data mining approach based on an innovative fuzzy backpropagation network (FBPN) that determines the lower and upper bounds of the job cycle time. Forecasting accuracy is also significantly improved by a combination of principal component analysis (PCA), fuzzy c-means (FCM), and FBPN. An applied case that uses data collected from a wafer fabrication factory illustrates this fuzzy data mining approach. For this applied case, the proposed methodology performs better than six existing data mining approaches.

Original languageEnglish
Article number496826
JournalMathematical Problems in Engineering
Volume2013
DOIs
StatePublished - 11 Jun 2013

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